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Before you start

Author responsibilities.

Our goal is to provide you with a professional and courteous experience at each stage of the review and publication process. There are also some responsibilities that sit with you as the author. Our expectation is that you will:

Respond swiftly to any queries during the publication process.
Be accountable for all aspects of your work. This includes investigating and resolving any questions about accuracy or research integrity
Treat communications between you and the journal editor as confidential until an editorial decision has been made.
Include anyone who has made a substantial and meaningful contribution to the submission (anyone else involved in the paper should be listed in the acknowledgements).
Exclude anyone who hasn’t contributed to the paper, or who has chosen not to be associated with the research.
In accordance with COPE’s position statement on AI tools , Large Language Models cannot be credited with authorship as they are incapable of conceptualising a research design without human direction and cannot be accountable for the integrity, originality, and validity of the published work. The author(s) must describe the content created or modified as well as appropriately cite the name and version of the AI tool used; any additional works drawn on by the AI tool should also be appropriately cited and referenced. Standard tools that are used to improve spelling and grammar are not included within the parameters of this guidance. The Editor and Publisher reserve the right to determine whether the use of an AI tool is permissible.
If your article involves human participants, you must ensure you have considered whether or not you require ethical approval for your research, and include this information as part of your submission. Find out more about informed consent .

Generative AI usage key principles

Copywriting any part of an article using a generative AI tool/LLM would not be permissible, including the generation of the abstract or the literature review, for as per Emerald’s authorship criteria, the author(s) must be responsible for the work and accountable for its accuracy, integrity, and validity.
The generation or reporting of results using a generative AI tool/LLM is not permissible, for as per Emerald’s authorship criteria, the author(s) must be responsible for the creation and interpretation of their work and accountable for its accuracy, integrity, and validity.
The in-text reporting of statistics using a generative AI tool/LLM is not permissible due to concerns over the authenticity, integrity, and validity of the data produced, although the use of such a tool to aid in the analysis of the work would be permissible.
Copy-editing an article using a generative AI tool/LLM in order to improve its language and readability would be permissible as this mirrors standard tools already employed to improve spelling and grammar, and uses existing author-created material, rather than generating wholly new content, while the author(s) remains responsible for the original work.
The submission and publication of images created by AI tools or large-scale generative models is not permitted.

Research and publishing ethics

Our editors and employees work hard to ensure the content we publish is ethically sound. To help us achieve that goal, we closely follow the advice laid out in the guidelines and flowcharts on the COPE (Committee on Publication Ethics) website .

We have also developed our research and publishing ethics guidelines . If you haven’t already read these, we urge you to do so – they will help you avoid the most common publishing ethics issues.

A few key points:

Any manuscript you submit to this journal should be original. That means it should not have been published before in its current, or similar, form. Exceptions to this rule are outlined in our pre-print and conference paper policies . If any substantial element of your paper has been previously published, you need to declare this to the journal editor upon submission. Please note, the journal editor may use Crossref Similarity Check to check on the originality of submissions received. This service compares submissions against a database of 49 million works from 800 scholarly publishers.
Your work should not have been submitted elsewhere and should not be under consideration by any other publication.
If you have a conflict of interest, you must declare it upon submission; this allows the editor to decide how they would like to proceed. Read about conflict of interest in our research and publishing ethics guidelines .
By submitting your work to Emerald, you are guaranteeing that the work is not in infringement of any existing copyright.

Third party copyright permissions

Prior to article submission, you need to ensure you’ve applied for, and received, written permission to use any material in your manuscript that has been created by a third party. Please note, we are unable to publish any article that still has permissions pending. The rights we require are:

Non-exclusive rights to reproduce the material in the article or book chapter.
Print and electronic rights.
Worldwide English-language rights.
To use the material for the life of the work. That means there should be no time restrictions on its re-use e.g. a one-year licence.

We are a member of the International Association of Scientific, Technical, and Medical Publishers (STM) and participate in the STM permissions guidelines , a reciprocal free exchange of material with other STM publishers. In some cases, this may mean that you don’t need permission to re-use content. If so, please highlight this at the submission stage.

Please take a few moments to read our guide to publishing permissions to ensure you have met all the requirements, so that we can process your submission without delay.

Open access submissions and information

All our journals currently offer two open access (OA) publishing paths; gold open access and green open access.

If you would like to, or are required to, make the branded publisher PDF (also known as the version of record) freely available immediately upon publication, you can select the gold open access route once your paper is accepted.

If you’ve chosen to publish gold open access, this is the point you will be asked to pay the APC (article processing charge) . This varies per journal and can be found on our APC price list or on the editorial system at the point of submission. Your article will be published with a Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

Alternatively, if you would like to, or are required to, publish open access but your funding doesn’t cover the cost of the APC, you can choose the green open access, or self-archiving, route. As soon as your article is published, you can make the author accepted manuscript (the version accepted for publication) openly available, free from payment and embargo periods.

You can find out more about our open access routes, our APCs and waivers and read our FAQs on our open research page.

Find out about open

Transparency and Openness Promotion (TOP) guidelines

We are a signatory of the Transparency and Openness Promotion (TOP) Guidelines , a framework that supports the reproducibility of research through the adoption of transparent research practices. That means we encourage you to:

Cite and fully reference all data, program code, and other methods in your article.
Include persistent identifiers, such as a Digital Object Identifier (DOI), in references for datasets and program codes. Persistent identifiers ensure future access to unique published digital objects, such as a piece of text or datasets. Persistent identifiers are assigned to datasets by digital archives, such as institutional repositories and partners in the Data Preservation Alliance for the Social Sciences (Data-PASS).
Follow appropriate international and national procedures with respect to data protection, rights to privacy and other ethical considerations, whenever you cite data. For further guidance please refer to our research and publishing ethics guidelines . For an example on how to cite datasets, please refer to the references section below.

Prepare your submission

Manuscript support services.

We are pleased to partner with Editage, a platform that connects you with relevant experts in language support, translation, editing, visuals, consulting, and more. After you’ve agreed a fee, they will work with you to enhance your manuscript and get it submission-ready.

This is an optional service for authors who feel they need a little extra support. It does not guarantee your work will be accepted for review or publication.

Visit Editage

Manuscript requirements

Before you submit your manuscript, it’s important you read and follow the guidelines below. You will also find some useful tips in our structure your journal submission how-to guide.

	Article files should be provided in Microsoft Word format While you are welcome to submit a PDF of the document alongside the Word file, PDFs alone are not acceptable. LaTeX files can also be used but only if an accompanying PDF document is provided. Acceptable figure file types are listed further below.
	Articles should be between 2000 and 9500 words in length. This includes all text, for example, the structured abstract, references, all text in tables, and figures and appendices. Please allow 280 words for each figure or table.
	A concisely worded title should be provided.
	The names of all contributing authors should be added to the ScholarOne submission; please list them in the order in which you’d like them to be published. Each contributing author will need their own ScholarOne author account, from which we will extract the following details: . . We will reproduce it exactly, so any middle names and/or initials they want featured must be included. . This should be where they were based when the research for the paper was conducted. In multi-authored papers, it’s important that ALL authors that have made a significant contribution to the paper are listed. Those who have provided support but have not contributed to the research should be featured in an acknowledgements section. You should never include people who have not contributed to the paper or who don’t want to be associated with the research. Read about our for authorship.
	If you want to include these items, save them in a separate Microsoft Word document and upload the file with your submission. Where they are included, a brief professional biography of not more than 100 words should be supplied for each named author.
	Your article must reference all sources of external research funding in the acknowledgements section. You should describe the role of the funder or financial sponsor in the entire research process, from study design to submission.
	All submissions must include a structured abstract, following the format outlined below. These four sub-headings and their accompanying explanations must always be included: The following three sub-headings are optional and can be included, if applicable: You can find some useful tips in our how-to guide. The maximum length of your abstract should be 250 words in total, including keywords and article classification (see the sections below).
	Your submission should include up to 12 appropriate and short keywords that capture the principal topics of the paper. Our how to guide contains some practical guidance on choosing search-engine friendly keywords. Please note, while we will always try to use the keywords you’ve suggested, the in-house editorial team may replace some of them with matching terms to ensure consistency across publications and improve your article’s visibility.
	During the submission process, you will be asked to select a type for your paper; the options are listed below. You will also be asked to select a category for your paper. The options for this are listed below. If you don’t see an exact match, please choose the best fit: Reports on any type of research undertaken by the author(s), including: Covers any paper where content is dependent on the author's opinion and interpretation. This includes journalistic and magazine-style pieces. Describes and evaluates technical products, processes or services. Focuses on developing hypotheses and is usually discursive. Covers philosophical discussions and comparative studies of other authors’ work and thinking. Describes actual interventions or experiences within organizations. It can be subjective and doesn’t generally report on research. Also covers a description of a legal case or a hypothetical case study used as a teaching exercise. This category should only be used if the main purpose of the paper is to annotate and/or critique the literature in a particular field. It could be a selective bibliography providing advice on information sources, or the paper may aim to cover the main contributors to the development of a topic and explore their different views. Provides an overview or historical examination of some concept, technique or phenomenon. Papers are likely to be more descriptive or instructional (‘how to’ papers) than discursive.
	Headings must be concise, with a clear indication of the required hierarchy. The preferred format is for first level headings to be in bold, and subsequent sub-headings to be in medium italics.
	Notes or endnotes should only be used if absolutely necessary. They should be identified in the text by consecutive numbers enclosed in square brackets. These numbers should then be listed, and explained, at the end of the article.
	All figures (charts, diagrams, line drawings, webpages/screenshots, and photographic images) should be submitted electronically. Both colour and black and white files are accepted. There are a few other important points to note:
	Tables should be typed and submitted in a separate file to the main body of the article. The position of each table should be clearly labelled in the main body of the article with corresponding labels clearly shown in the table file. Tables should be numbered consecutively in Arabic numerals. Give each table a brief title. Ensure that any superscripts or asterisks are shown next to the relevant items and have explanations displayed as footnotes to the table, figure or plate.
	Where tables, figures, appendices, and other additional content are supplementary to the article but not critical to the reader’s understanding of it, you can choose to host these supplementary files alongside your article on Insight, Emerald’s content hosting platform, or on an institutional or personal repository. All supplementary material must be submitted prior to acceptance. , you must submit these as separate files alongside your article. Files should be clearly labelled in such a way that makes it clear they are supplementary; Emerald recommends that the file name is descriptive and that it follows the format ‘Supplementary_material_appendix_1’ or ‘Supplementary tables’. . A link to the supplementary material will be added to the article during production, and the material will be made available alongside the main text of the article at the point of EarlyCite publication. Please note that Emerald will not make any changes to the material; it will not be copyedited, typeset, and authors will not receive proofs. Emerald therefore strongly recommends that you style all supplementary material ahead of acceptance of the article. Emerald Insight can host the following file types and extensions: , you should ensure that the supplementary material is hosted on the repository ahead of submission, and then include a link only to the repository within the article. It is the responsibility of the submitting author to ensure that the material is free to access and that it remains permanently available. Please note that extensive supplementary material may be subject to peer review; this is at the discretion of the journal Editor and dependent on the content of the material (for example, whether including it would support the reviewer making a decision on the article during the peer review process).
	All references in your manuscript must be formatted using one of the recognised Harvard styles. You are welcome to use the Harvard style Emerald has adopted – we’ve provided a detailed guide below. Want to use a different Harvard style? That’s fine, our typesetters will make any necessary changes to your manuscript if it is accepted. Please ensure you check all your citations for completeness, accuracy and consistency; this enables your readers to exploit the reference linking facility on the database and link back to the works you have cited through Crossref. References to other publications in your text should be written as follows: , 2006) Please note, ‘ ' should always be written in italics. A few other style points. These apply to both the main body of text and your final list of references. At the end of your paper, please supply a reference list in alphabetical order using the style guidelines below. Where a DOI is available, this should be included at the end of the reference.
	Surname, initials (year), , publisher, place of publication. e.g. Harrow, R. (2005), , Simon & Schuster, New York, NY.
	Surname, initials (year), "chapter title", editor's surname, initials (Ed.), , publisher, place of publication, page numbers. e.g. Calabrese, F.A. (2005), "The early pathways: theory to practice – a continuum", Stankosky, M. (Ed.), , Elsevier, New York, NY, pp.15-20.
	Surname, initials (year), "title of article", , volume issue, page numbers. e.g. Capizzi, M.T. and Ferguson, R. (2005), "Loyalty trends for the twenty-first century", , Vol. 22 No. 2, pp.72-80.
	Surname, initials (year of publication), "title of paper", in editor’s surname, initials (Ed.), , publisher, place of publication, page numbers. e.g. Wilde, S. and Cox, C. (2008), “Principal factors contributing to the competitiveness of tourism destinations at varying stages of development”, in Richardson, S., Fredline, L., Patiar A., & Ternel, M. (Ed.s), , Griffith University, Gold Coast, Qld, pp.115-118.
	Surname, initials (year), "title of paper", paper presented at [name of conference], [date of conference], [place of conference], available at: URL if freely available on the internet (accessed date). e.g. Aumueller, D. (2005), "Semantic authoring and retrieval within a wiki", paper presented at the European Semantic Web Conference (ESWC), 29 May-1 June, Heraklion, Crete, available at: ;(accessed 20 February 2007).
	Surname, initials (year), "title of article", working paper [number if available], institution or organization, place of organization, date. e.g. Moizer, P. (2003), "How published academic research can inform policy decisions: the case of mandatory rotation of audit appointments", working paper, Leeds University Business School, University of Leeds, Leeds, 28 March.
	(year), "title of entry", volume, edition, title of encyclopaedia, publisher, place of publication, page numbers. e.g. (1926), "Psychology of culture contact", Vol. 1, 13th ed., Encyclopaedia Britannica, London and New York, NY, pp.765-771. (for authored entries, please refer to book chapter guidelines above)
	Surname, initials (year), "article title", , date, page numbers. e.g. Smith, A. (2008), "Money for old rope", , 21 January, pp.1, 3-4.
	(year), "article title", date, page numbers. e.g. (2008), "Small change", 2 February, p.7.
	Surname, initials (year), "title of document", unpublished manuscript, collection name, inventory record, name of archive, location of archive. e.g. Litman, S. (1902), "Mechanism & Technique of Commerce", unpublished manuscript, Simon Litman Papers, Record series 9/5/29 Box 3, University of Illinois Archives, Urbana-Champaign, IL.
	If available online, the full URL should be supplied at the end of the reference, as well as the date that the resource was accessed. Surname, initials (year), “title of electronic source”, available at: persistent URL (accessed date month year). e.g. Weida, S. and Stolley, K. (2013), “Developing strong thesis statements”, available at: (accessed 20 June 2018) Standalone URLs, i.e. those without an author or date, should be included either inside parentheses within the main text, or preferably set as a note (Roman numeral within square brackets within text followed by the full URL address at the end of the paper).
	Surname, initials (year), , name of data repository, available at: persistent URL, (accessed date month year). e.g. Campbell, A. and Kahn, R.L. (2015), , ICPSR07218-v4, Inter-university Consortium for Political and Social Research (distributor), Ann Arbor, MI, available at: (accessed 20 June 2018)

Submit your manuscript

There are a number of key steps you should follow to ensure a smooth and trouble-free submission.

Double check your manuscript

Before submitting your work, it is your responsibility to check that the manuscript is complete, grammatically correct, and without spelling or typographical errors. A few other important points:

Give the journal aims and scope a final read. Is your manuscript definitely a good fit? If it isn’t, the editor may decline it without peer review.
Does your manuscript comply with our research and publishing ethics guidelines ?
Have you cleared any necessary publishing permissions ?
Have you followed all the formatting requirements laid out in these author guidelines?
If you need to refer to your own work, use wording such as ‘previous research has demonstrated’ not ‘our previous research has demonstrated’.
If you need to refer to your own, currently unpublished work, don’t include this work in the reference list.
Any acknowledgments or author biographies should be uploaded as separate files.
Carry out a final check to ensure that no author names appear anywhere in the manuscript. This includes in figures or captions.

You will find a helpful submission checklist on the website Think.Check.Submit .

The submission process

All manuscripts should be submitted through our editorial system by the corresponding author.

The only way to submit to the journal is through the journal’s ScholarOne site as accessed via the Emerald website, and not by email or through any third-party agent/company, journal representative, or website. Submissions should be done directly by the author(s) through the ScholarOne site and not via a third-party proxy on their behalf.

A separate author account is required for each journal you submit to. If this is your first time submitting to this journal, please choose the Create an account or Register now option in the editorial system. If you already have an Emerald login, you are welcome to reuse the existing username and password here.

Please note, the next time you log into the system, you will be asked for your username. This will be the email address you entered when you set up your account.

Don't forget to add your ORCiD ID during the submission process. It will be embedded in your published article, along with a link to the ORCiD registry allowing others to easily match you with your work.

Don’t have one yet? It only takes a few moments to register for a free ORCiD identifier .

Visit the ScholarOne support centre for further help and guidance.

What you can expect next

You will receive an automated email from the journal editor, confirming your successful submission. It will provide you with a manuscript number, which will be used in all future correspondence about your submission. If you have any reason to suspect the confirmation email you receive might be fraudulent, please contact our Rights team .

Post submission

Review and decision process.

Each submission is checked by the editor. At this stage, they may choose to decline or unsubmit your manuscript if it doesn’t fit the journal aims and scope, or they feel the language/manuscript quality is too low.

If they think it might be suitable for the publication, they will send it to at least two independent referees for double blind peer review. Once these reviewers have provided their feedback, the editor may decide to accept your manuscript, request minor or major revisions, or decline your work.

This journal offers an article transfer service. If the editor decides to decline your manuscript, either before or after peer review, they may offer to transfer it to a more relevant Emerald journal in this field. If you accept, your ScholarOne author account, and the accounts of your co-authors, will automatically transfer to the new journal, along with your manuscript and any accompanying peer review reports. However, you will still need to log in to ScholarOne to complete the submission process using your existing username and password. While accepting a transfer does not guarantee the receiving journal will publish your work, an editor will only suggest a transfer if they feel your article is a good fit with the new title.

While all journals work to different timescales, the goal is that the editor will inform you of their first decision within 60 days.

During this period, we will send you automated updates on the progress of your manuscript via our submission system, or you can log in to check on the current status of your paper. Each time we contact you, we will quote the manuscript number you were given at the point of submission. If you receive an email that does not match these criteria, it could be fraudulent and we recommend you email [email protected] .

If your submission is accepted

Open access.

Once your paper is accepted, you will have the opportunity to indicate whether you would like to publish your paper via the gold open access route.

If you’ve chosen to publish gold open access, this is the point you will be asked to pay the APC (article processing charge). This varies per journal and can be found on our APC price list or on the editorial system at the point of submission. Your article will be published with a Creative Commons CC BY 4.0 user licence , which outlines how readers can reuse your work.

All accepted authors are sent an email with a link to a licence form. This should be checked for accuracy, for example whether contact and affiliation details are up to date and your name is spelled correctly, and then returned to us electronically. If there is a reason why you can’t assign copyright to us, you should discuss this with your journal content editor. You will find their contact details on the editorial team section above.

Proofing and typesetting

Once we have received your completed licence form, the article will pass directly into the production process. We will carry out editorial checks, copyediting, and typesetting and then return proofs to you (if you are the corresponding author) for your review. This is your opportunity to correct any typographical errors, grammatical errors or incorrect author details. We can’t accept requests to rewrite texts at this stage.

When the page proofs are finalised, the fully typeset and proofed version of record is published online. This is referred to as the EarlyCite version. While an EarlyCite article has yet to be assigned to a volume or issue, it does have a digital object identifier (DOI) and is fully citable. It will be compiled into an issue according to the journal’s issue schedule, with papers being added by chronological date of publication.

How to share your paper

Visit our author rights page to find out how you can reuse and share your work.

To find tips on increasing the visibility of your published paper, read about how to promote your work .

Correcting inaccuracies in your published paper

Sometimes errors are made during the research, writing and publishing processes. When these issues arise, we have the option of withdrawing the paper or introducing a correction notice. Find out more about our article withdrawal and correction policies .

Need to make a change to the author list? See our frequently asked questions (FAQs) below.

Frequently asked questions

	The only time we will ever ask you for money to publish in an Emerald journal is if you have chosen to publish via the gold open access route. You will be asked to pay an APC (article-processing charge) once your paper has been accepted (unless it is a sponsored open access journal), and never at submission. At no other time will you be asked to contribute financially towards your article’s publication, processing, or review. If you haven’t chosen gold open access and you receive an email that appears to be from Emerald, the journal, or a third party, asking you for payment to publish, please contact our support team via .
	Please contact the editor for the journal, with a copy of your CV. You will find their contact details on the editorial team tab on this page.
	Typically, papers are added to an issue according to their date of publication. If you would like to know in advance which issue your paper will appear in, please contact the content editor of the journal. You will find their contact details on the editorial team tab on this page. Once your paper has been published in an issue, you will be notified by email.
	Please email the journal editor – you will find their contact details on the editorial team tab on this page. If you ever suspect an email you’ve received from Emerald might not be genuine, you are welcome to verify it with the content editor for the journal, whose contact details can be found on the editorial team tab on this page. Alternatively, you can .
	If you’ve read the aims and scope on the journal landing page and are still unsure whether your paper is suitable for the journal, please email the editor and include your paper's title and structured abstract. They will be able to advise on your manuscript’s suitability. You will find their contact details on the Editorial team tab on this page.
	Authorship and the order in which the authors are listed on the paper should be agreed prior to submission. We have a right first time policy on this and no changes can be made to the list once submitted. If you have made an error in the submission process, please email the 8Journal Editorial Office who will look into your request – you will find their contact details on the editorial team tab on this page.

Christy M. K. Cheung Hong Kong Baptist University - Hong Kong, China [email protected]

Editors Emeriti

1995 – 1998: John Peters Emerald Publishing - UK
1998 – 2011: David G. Schwartz Bar-Ilan University - Israel
2011 – 2016: Bernard J. Jansen The Pennsylvania State University - USA and Qatar Computing Research Institute, Doha - Qatar

Distinguished Board of Advisors

Izak Benbasat The University of British Columbia - Canada
Alexander Benlian Darmstadt University of Technology - Germany
Andrew Burton-Jones The University of Queensland - Australia
Carol Hsu The University of Sydney - Australia
Bernard J. Jansen The Pennsylvania State University - USA and Qatar Computing Research Institute, Doha - Qatar
Atreyi Kankanhalli National University of Singapore - Singapore
Matthew K.O. Lee City University of Hong Kong - Hong Kong
Lynne Markus Bentley University - USA
Chee-Wee Tan Hong Kong Polytechnic University - Hong Kong, China
Monideepa Tarafdar University of Massachusetts Amherst - USA
Jason Thatcher University of Colorado Boulder - USA
Daniel Veit University of Augsburg - Germany
Viswanath Venkatesh Virginia Polytechnic Institute and State University - USA

Senior Editors

Yang Chen Southwestern University of Finance and Economics - People's Republic of China
Juho Hamari Tampere University - Finland
Zach W. Y. Lee University of Leicester - UK
Christian Maier University of Bamberg - Germany
Christian Matt University of Bern - Switzerland
Xiao-Liang Shen Wuhan University - People's Republic of China
Ayoung Suh Sungkyunkwan University - Republic of Korea
Manuel Trenz University of Göttingen - Germany
Ofir Turel The University of Melbourne - Australia
Kem Z. K. Zhang Lakehead University - Canada

Associate Editors

Zhao Cai University of Nottingham Ningbo China - People's Republic of China
Tommy K. H. Chan University of Manchester - UK
Xusen Cheng Renmin University of China - China
Ben Choi Nanyang Technological University - Singapore
Raffaele Filieri Audencia Business School - France
Maryam Ghasemaghaei McMaster University - Canada
Xitong Guo Harbin Institute of Technology - People's Republic of China
Yaniv Gvili Ono Academic College - Israel
Nima Kordzadeh Worcester Polytechnic Institute in Massachusetts - USA
Satish Krishnan Indian Institute of Management, Kozhikode - India
Hongxiu Li Tampere University - Finland
Eric T. K. Lim University of New South Wales - Australia
Yong Liu Aalto University School of Business - Finland
Jian Mou Pusan National University - Republic of Korea
Fiona Nah City University of Hong Kong - Hong Kong
Tiago Oliveira New University of Lisbon - Portugal
Hamed Qahri-Saremi Colorado State University - USA
Marten Risius The University of Queensland - Australia
Nick Roberts Oregon State University - USA
Sebastian Schuetz Florida International University - USA
Isabella Seeber Grenoble Ecole de Management - France
Wen-Lung Shiau Chang Gung University - Taiwan
Barney Tan University of New South Wales - Australia
Christoph Weinert University of Bamberg - Germany
Randy Wong The University of Auckland - New Zealand
Zhijun Yan Beijing Institute of Technology - People's Republic of China
Frederick Yim Hong Kong Baptist University - Hong Kong

Managing Editor for Marketing

Grace Gao Hong Kong Baptist University - Hong Kong

Commissioning Editor

Charlotte Eagles Emerald Publishing [email protected]

Journal Editorial Office (For queries related to pre-acceptance)

Nikita Singh Emerald Publishing [email protected]

Supplier Project Manager (For queries related to post-acceptance)

Sivakeerthika Saravanan Emerald Publishing [email protected]

Editorial Review Board

Muhammad Naseer Akhtar University of East London - UK
Mamoun Akroush German Jordanian University - Jordan and American University of Madaba - Jordan
Joonheui Bae The Hong Kong Polytechnic University - Hong Kong
Snehasish Banerjee University of York - UK
Annika Baumann University of Potsdam - Germany
Luis Vicente Casaló Ariño University of Zaragoza - Spain
Hsiu-Hua Chang Feng Chia University - Taiwan
Wei-Lun Chang National Taipei University of Technology - Taiwan
Chongyang Chen Zhejiang University of Finance and Economics - People's Republic of China
Meng Chen University of Science and Technology of China - People's Republic of China
Xi Chen Zhejiang University - People's Republic of China
Alton Y.K. Chua Nanyang Technological University - Singapore
Francesca D’Errico University of Bari Aldo Moro - Italy
Jie Fang University of Nottingham Ningbo China - People's Republic of China
Samira Farivar Carleton University - Canada
Steven M. Furnell University of Nottingham - UK
Chunmian Ge South China University of Technology - People's Republic of China
Xiang Gong Xi'an JiaoTong University - People's Republic of China
Chong Guan Singapore University of Social Sciences - Singapore
Chris Hand Kingston University - UK
Maximilian Haug Neu-Ulm University of Applied Sciences - Germany
Wei He Texas Tech University - USA
Fangfang Hou Xi’an Jiaotong-Liverpool University - People's Republic of China
Kuo-Lun Hsiao National Taichung University of Science and Technology - Taiwan
Echo Huang National Kaohsiung University of Science and Technology - Taiwan
Tseng-Lung Huang National Pingtung University - Taiwan
Wendy Wan Yee Hui Singapore Institute of Technology - Singapore
Lin Jia Beijing Institute of Technology - People's Republic of China
Hyunjin Kang Nanyang Technological University - Singapore
Ching-Jui Keng National Taipei University of Technology - Taiwan
Hamid Khobzi University of Sussex - UK
Austin Kwak Kent State University - USA
Antonia Köster University of Potsdam - Germany
Cong Li University of Miami - USA
Wenwen Li Fudan University - People's Republic of China
Yang-Jun Li Beijing Institute of Technology - People's Republic of China
Yupeng Li Hong Kong Baptist University - Hong Kong
Chih-Chin Liang National Formosa University - Taiwan
Binshan Lin Louisiana State University in Shreveport - USA
Hsiu-Fen Lin National Taiwan Ocean University - Taiwan
Na Liu The University of Sydney - Australia
Shan Liu Xi'an JiaoTong University - People's Republic of China
William Maule Naval Postgraduate School - USA
Felix Septianto The University of Queensland - Australia
Rinat Shaanan-Satchi Open University of Israel - Israel
Rong-An Shang Soochow University - Taiwan
Chung-Chi Shen National Chiayi University - Taiwan
Si Shi Southwestern University of Finance and Economics - People's Republic of China
Donghee Don Shin Texas Tech University - USA
Garry Wei Han Tan UCSI University - Malaysia
Ching-I Teng Chang Gung University - Taiwan
Leo R Vijayasarathy Colorado State University - USA
Nianxin Wang Jiangsu University of Science and Technology - People's Republic of China
Xiaohui Wang City University of Hong Kong - Hong Kong
Yi Wu Tianjin University - People's Republic of China
Nannan Xi Tampere University - Finland
Vignesh Yoganathan Queen Mary University of London - UK
Jie Yu (Joseph) University of Nottingham Ningbo China - People's Republic of China
Chian-Son Yu Shih Chien University - Taiwan
Xiabing Zheng University of Science and Technology of China - People's Republic of China
Hangjung Zo Korea Advanced Institute of Science and Technology - Republic of Korea

Citation metrics

CiteScore 2023

Further information

CiteScore is a simple way of measuring the citation impact of sources, such as journals.

Calculating the CiteScore is based on the number of citations to documents (articles, reviews, conference papers, book chapters, and data papers) by a journal over four years, divided by the number of the same document types indexed in Scopus and published in those same four years.

For more information and methodology visit the Scopus definition

CiteScore Tracker 2024

(updated monthly)

CiteScore Tracker is calculated in the same way as CiteScore, but for the current year rather than previous, complete years.

The CiteScore Tracker calculation is updated every month, as a current indication of a title's performance.

2023 Impact Factor

The Journal Impact Factor is published each year by Clarivate Analytics. It is a measure of the number of times an average paper in a particular journal is cited during the preceding two years.

For more information and methodology see Clarivate Analytics

5-year Impact Factor (2023)

A base of five years may be more appropriate for journals in certain fields because the body of citations may not be large enough to make reasonable comparisons, or it may take longer than two years to publish and distribute leading to a longer period before others cite the work.

Actual value is intentionally only displayed for the most recent year. Earlier values are available in the Journal Citation Reports from Clarivate Analytics .

Publication timeline

Time to first decision

Time to first decision , expressed in days, the "first decision" occurs when the journal’s editorial team reviews the peer reviewers’ comments and recommendations. Based on this feedback, they decide whether to accept, reject, or request revisions for the manuscript.

Data is taken from submissions between 1st June 2023 and 31st May 2024

Acceptance to publication

Acceptance to publication , expressed in days, is the average time between when the journal’s editorial team decide whether to accept, reject, or request revisions for the manuscript and the date of publication in the journal.

Data is taken from the previous 12 months (Last updated July 2024)

Acceptance rate

The acceptance rate is a measurement of how many manuscripts a journal accepts for publication compared to the total number of manuscripts submitted expressed as a percentage %

Data is taken from submissions between 1st June 2023 and 31st May 2024 .

This figure is the total amount of downloads for all articles published early cite in the last 12 months

(Last updated: July 2024)

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The Chartered Association of Business Schools' Academic Journal Guide: 3
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Reviewer information

Peer review process.

This journal engages in a double-anonymous peer review process, which strives to match the expertise of a reviewer with the submitted manuscript. Reviews are completed with evidence of thoughtful engagement with the manuscript, provide constructive feedback, and add value to the overall knowledge and information presented in the manuscript.

The mission of the peer review process is to achieve excellence and rigour in scholarly publications and research.

Our vision is to give voice to professionals in the subject area who contribute unique and diverse scholarly perspectives to the field.

The journal values diverse perspectives from the field and reviewers who provide critical, constructive, and respectful feedback to authors. Reviewers come from a variety of organizations, careers, and backgrounds from around the world.

All invitations to review, abstracts, manuscripts, and reviews should be kept confidential. Reviewers must not share their review or information about the review process with anyone without the agreement of the editors and authors involved, even after publication. This also applies to other reviewers’ “comments to author” which are shared with you on decision.

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Discover practical tips and guidance on all aspects of peer review in our reviewers' section. See how being a reviewer could benefit your career, and discover what's involved in shaping a review.

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Calls for papers

Ai for a better future.

Click here to Submit! Introduction: Artificial intelligence (AI) is permeating individuals’ daily live...

Thank you to the 2022 Reviewers

The publishing and editorial teams would like to thank the following, for their invaluable service as 2022 reviewers for this journal. We are very grateful for the contributions made. With their help, the journal has been able to publish such high...

Thank you to the 2021 Reviewers

The publishing and editorial teams would like to thank the following, for their invaluable service as 2021 reviewers for this journal. We are very grateful for the contributions made. With their help, the journal has been able to publish such high...

Internet Research Emerald Literati Awards for Excellence 2021

We are pleased to announce the 2021 winners of the Internet Research Emerald Literati Awards for Excellence. The awards celebrate and reward the outstanding contributions of our authors, reviewers, and editorial ...

Internet Research Emerald Literati Awards for Excellence 2015-2020

We are pleased to announce the winners of the Internet Research Emerald Literati Awards for Excellence in 2015 - 2020. The awards celebrate and reward the outstanding contributions of o...

Literati awards

Internet Research - Literati Award Winners 2023

We are pleased to announce our 2023 Literati Award winners. Outstanding Paper Laziness as an explanation for the privacy paradox: a longitudinal ...

Internet Research Emerald Literati Awards for Excellence 2022

We are pleased to announce the 2022 winners of the Internet Research Emerald Literati Awards for Excellence. The awards celebrate and reward the outstanding contributions of our authors, reviewers, and editorial team....

Internet Research - Literati Award Winners 2021

We are pleased to announce our 2021 Literati Award winners. Outstanding Paper Wang, X. and Song, Y. (2020), "Viral misinformation and echo chambers: the diffusion of rumors about genetically mo...

Internet Research - Literati Award Winners 2020

We are pleased to announce our 2020 Literati Award winners. Outstanding paper Hornik, J., Shaanan Satchi, R. and Rachamim, M. (2019), "The joy of pain: A gloating account of negative electronic...

This wide-ranging interdisciplinary journal looks at the social, ethical, economic, managerial and political implications of the internet. Recent issues have focused on metaverse, interpretable AI, digital health, online and mobile gaming, the sharing economy, and the dark side of social media.

Aims and scope

Internet Research is an international, refereed journal that aims to describe, assess and foster understanding of the role of wide-area, multi-purpose computer networks such as the Internet.

The Internet continues to gather influence and momentum, and it becomes increasingly important to be aware of the potential applications of this powerful resource throughout professional, political, personal and academic life. We therefore strongly encourage research that develops theoretical insights and understanding on topics and issues addressing the potential social, ethical, economic, managerial and political implications which arise from mass public access to information resources.

Coverage includes but is not limited to:

Societal and ethical issues (e.g., internet addiction, doxing, cyberbullying, misinformation, fear of missing out)
Economic implications (e.g., electronic commerce, mobile commerce, and social commerce)
Managerial and organisational issues (e.g., Internet use practices, cyberloafing, management policies and governance mechanisms)
Political implications (e.g., civic engagement, collective action, Internet-related policy research)

Latest articles

These are the latest articles published in this journal (Last updated: July 2024)

Seeking Social Support on Social Media: A Coping Perspective

The role of omnichannel integration and digital value in building brand trust: a customer psychological perception perspective, platform control and multi-realized platform benefits: a meta-analysis, top downloaded articles.

These are the most downloaded articles over the last 12 months for this journal (Last updated: July 2024)

AI Governance: Themes, Knowledge Gaps, and Future Agendas

Personal use of smartphones in the workplace and work-life conflict: a natural quasi-experiment, examining technostress and its impact on worker wellbeing in the digital gig economy.

These are the top cited articles for this journal, from the last 12 months according to Crossref (Last updated: July 2024)

Exploring the association between the Proteus effect and intention to play massive multiplayer online role-playing games (MMORPGs)

A review of the literature on the metaverse: definition, technologies, and user behaviors, enterprise social media usage and social cyberloafing: an empirical investigation using the jd-r model, related journals.

This journal is part of our Information & knowledge management collection. Explore our Information & knowledge management subject area to find out more.

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Internet Research

Issue(s) available: 166 – From Volume: 1 Issue: 1 , to Volume: 34 Issue: 7

Issue 7 2024 Open Access Issue in Progress
Issue 4 2024
Issue 3 2024
Issue 2 2024
Issue 1 2024 The Opportunities and Challenges in the Metaverse
Issue 7 2023 Open Access Issue
Issue 6 2023
Issue 5 2023 The social, ethical, economic, and political implications of misinformation
Issue 4 2023
Issue 3 2023
Issue 2 2023
Issue 1 2023
Issue 7 2022 Open Access Issue
Issue 6 2022
Issue 5 2022
Issue 4 2022
Issue 3 2022
Issue 2 2022 Interpretable AI-enabled Online Behavior Analytics
Issue 1 2022
Issue 6 2021 The Bright Side and the Dark Side of Digital Health
Issue 5 2021
Issue 4 2021
Issue 3 2021
Issue 2 2021
Issue 1 2021
Issue 6 2020
Issue 5 2020
Issue 4 2020
Issue 3 2020
Issue 2 2020
Issue 1 2020
Issue 6 2019
Issue 5 2019 The Sharing Economy: Promises and Challenges
Issue 4 2019
Issue 3 2019 Internet research using partial least squares
Issue 2 2019 Online and mobile gaming
Issue 1 2019
Issue 5 2018 The Dark Side of Social Media
Issue 4 2018
Issue 3 2018
Issue 2 2018
Issue 1 2018
Issue 5 2017
Issue 4 2017
Issue 3 2017
Issue 2 2017
Issue 1 2017
Issue 5 2016
Issue 4 2016
Issue 3 2016
Issue 2 2016 Internet of Things
Issue 1 2016
Issue 5 2015
Issue 4 2015
Issue 3 2015
Issue 2 2015
Issue 1 2015
Issue 5 2014
Issue 4 2014
Issue 3 2014
Issue 2 2014
Issue 1 2014
Issue 5 2013 The power of prediction with social media
Issue 4 2013
Issue 3 2013
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Issue 1 2013
Issue 5 2012
Issue 4 2012
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Issue 2 2012
Issue 1 2012
Issue 5 2011
Issue 4 2011
Issue 3 2011
Issue 2 2011
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Issue 5 2010
Issue 4 2010 Internet Research 20th Anniversary Commemorative Issue
Issue 3 2010 Intelligent eservices applied to B2C ecommerce
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Issue 1 2010
Issue 5 2009
Issue 4 2009
Issue 3 2009
Issue 2 2009 Intelligent ubiquitous computing applications and security issues
Issue 1 2009
Issue 5 2008
Issue 4 2008
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Issue 2 2008 Information credibility on the web
Issue 1 2008
Issue 5 2007 Selected research papers from the TERENA networking conference 2007
Issue 4 2007
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Issue 2 2007
Issue 1 2007 The Sixth International Network Conference INC, 2006
Issue 5 2006 Apartthemed issue on internet security
Issue 4 2006
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Issue 2 2006 Privacy and anonymity in the digital era
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Exploring the impact of forced teleworking on counterproductive work behavior: the role of event strength and work-family conflict

Most employees are forced to telework due to the COVID-19 pandemic, which brings novel, disruptive, and critical challenges both in work and life. Based on event system theory and…

Optimizing live streaming engagement through store atmospheric cues: exploring prosocial behavior and social comparison—insights from streamers and viewers

Live Streaming Marketing has emerged as a transformative medium, facilitating real-time product promotion and brand messaging and reshaping consumer engagement. However, knowledge…

Constant or inconstant? The time-varying effect of danmaku on user engagement in online video platforms

As an emerging video comment feature, danmaku is gaining more traction and increasing user interaction, thereby altering user engagement. However, existing research seldom…

Potential treatments of technology addiction: insights for information systems scholars

In this current review, we aimed to understand technology addiction interventions and provide guidelines for IS scholars to use IT to prevent or attenuate technology addiction.

The golden zone of AI’s emotional expression in frontline chatbot service failures

The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of…

Is information normalization helpful in online communication? Evidence from online healthcare consultation

This study aims to investigate the role of information normalization in online healthcare consultation, a typical complex human-to-human communication requiring both effectiveness…

Effect of autonomous vehicle-related eWOM on (fe)males’ attitude and perceived risk as passengers and pedestrians

This study investigates how autonomous vehicle (AV)-related electronic word-of-mouth (eWOM) of different polarities affects attitude and perceived risk from the perspectives of…

Gamification as a panacea to workplace cyberloafing: an application of self-determination and social bonding theories

Gamification has been constantly demonstrated as an effective mechanism for employee engagement. However, little is known about how gamification reduces cyberloafing and the…

How emotions affect the outcomes of information overload: information avoidance or information consumption?

Information overload has become ubiquitous during a public health emergency. The research purpose is to examine the role of mixed emotions in the influence of perceived…

CEO social media celebrity status and credit rating assessment

This paper aims to investigate the effect of CEO social media celebrity status on credit ratings and to determine whether potential threats on the CEO celebrity status negatively…

Seeking social support on social media: a coping perspective

This paper aims to examine the strength characteristics of a stressful event (i.e. novelty, disruption, and criticality) as factors that drive people’s social media use for…

Platform control and multi-realized platform benefits: a meta-analysis

Platform owners need to encourage yet control complementors in ways that generate benefits. Retaining too much control can restrict innovation and knowledge flows; giving up too…

Corporate social responsibility and firm value: exploring the moderating effects of information technology-enabled knowledge capabilities

A substantial amount of research has examined the firm value impact of corporate social responsibility (CSR). Nevertheless, the findings have been inconsistent, prompting…

Seeing the forest and the trees: a meta-analysis of the antecedents to online self-disclosure

A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have…

My online self identifies with you, but my offline self skips you: the duality of online and offline personalities in identification with the endorser

The current study examines a novel model that examines how the online and offline or general personality of the same person predicts social identification with the endorser in a…

Pay with a smile? Modelling the continuance use intention of facial recognition payment

This study synthesises the self-determination theory (SDT), expectation-confirmation model (ECM), and protection motivation theory (PMT) to formulate an integrated theoretical…

Archetypes of influential users in social question-answering sites

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what…

Understanding first aid knowledge adoption on social media with an extended information adoption model

Social media provides a convenient way to popularise first aid knowledge amongst the general public. So far, little is known about the factors influencing individuals’ adoption of…

Mitigating perceived overload of communication visibility: the role of ESM policies

This study aims to explore how communication visibility affects employees’ work engagement from the negative perspective of employees’ perceived overload in the context of…

Inducing shoppers’ impulsive buying tendency in live-streaming: integrating signaling theory with social exchange theory

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange…

Examining the effects of cognitive load on information systems security policy compliance

Enforcing employee compliance with information systems security policies (ISSP) is a herculean task for organizations as security breaches due to non-compliance continue to soar…

Does corporate digitalization improve disclosure quality?

This study investigates the potential association between corporate digitization and disclosure quality, and how this relationship is moderated by non-state ownership and…

How perceived overload leads to WeChat passive usage intention: the mediating roles of social network exhaustion and discontented feeling

The principal purposes of the research are to empirically investigate three forms of perceived overload on social media and shed light on their associations with users’ passive…

Good for all, good for me: the influences of dedication- and constraint-based switching barriers on user-generated contributions

Prior research on user-generated content (UGC) contributions has primarily focused on self-centered or other-centered motives, paying limited attention to the concept of…

Rethinking privacy in the Internet of Things: a comprehensive review of consumer studies and theories

This study aims to analyze and synthesize literature on consumer privacy-related behavior and intelligent device-to-device interactions within the Internet of Things (IoT).

Beyond lazy; external locus of control as an alternative explanation for the privacy paradox

The privacy paradox refers to the situation where users of online services continue to disclose personal information even when they are concerned about their privacy. One recent…

The role of omnichannel integration and digital value in building brand trust: a customer psychological perception perspective

In the dynamic landscape of the digital economy, companies are increasingly adopting omnichannel integration strategies to enhance customer experiences. However, the interplay…

Spillover effects of data breach on consumer perceptions: evidence from the E-commerce industry

This study aims to explore the spillover effects of data breaches from a consumer perspective in the e-commerce context. Specifically, we investigate how an online retailer’s data…

Value implications of followers in social marketplaces: insights into ego network structures

In social marketplaces, follower ego networks are integral social capital assets for online sellers. While previous research has underscored the positive impact of the follower…

How do small-to-medium-sized e-commerce businesses stay competitive? Evidence on the critical roles of IT capability, innovation and multihoming

Small-to-medium-sized enterprises (SMEs) in e-commerce often invest in information technology (IT) to stay competitive. However, whether and how IT capability (ITC) translates…

A study on the cross-platform influence mechanism of physicians’ live streaming behavior on performance

The advent of online live streaming platforms (OLSPs) and online health communities (OHCs) has expedited the integration of traditional medical services with Internet new media…

Selling by contributing: the monetization strategy of individual content providers in the light of human brand

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers…

How do NPOs’ topics and moral foundations in gun-related issues influence public engagement on Twitter?

Drawing on the moral foundations theory (MFT), we examine what nonprofit organizations (NPOs) discuss and how NPOs engage in gun-related issues on Twitter. Specifically, we…

Social influence and the choice of product upgrades: evidence from virtual product adoption in online games

Previous studies on social influence and virtual product adoption have mainly taken users’ purchase behavior as a dichotomous variable (i.e. purchasing or not). Given the…

Sharenting in China: perspectives from mothers and adolescents

Mothers sharing images and information on social media about their children is a contemporary cultural norm. While the practice has been heavily discussed in popular media, there…

Exploring the dual routes in influencing sales and adoption in augmented reality retailing: a mixed approach of SEM and FsQCA

This study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing.

Not all IT addictions are handled equally: guilt-vs shame-driven coping with IT addiction

We build on the transactional model of stress and coping and the appraisal theory of emotions to theorize how users cognitively and emotionally cope with IT addiction-induced…

Stimulating positive reviews by combining financial and compassionate incentives

The objective of this study is to investigate the interaction effect between incentive type (financial and compassionate incentives) and the ethicality of merchant strategy on…

Fostering the digital mindset to mitigate technostress: an empirical study of empowering individuals for using digital technologies

Technostress reduces employees' work performance and increases their turnover intentions, such that technostress harms organizations' success. This paper investigates how the…

Service quality in cloud gaming: instrument development and validation

The global market for cloud gaming is growing rapidly. How gamers evaluate the service quality of this emerging form of cloud service has become a critical issue for both…

How tie strength influences purchasing intention in social recommendation: evidence from behavioral model and brain activity

Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper…

Generativity of enterprise IT infrastructure for digital innovation

Digital innovation requires organizations to reconfigure their information technology infrastructure (ITI) to cultivate creativity and implement fast experimentation. This…

Motivation for writing long online reviews: a big data analysis of an anime community

Based on self-determination theory (SDT), this study aims to determine the motivation factors of reviewers writing long reviews in the anime industry.

Building bonds: an examination of relational bonding in continuous content contribution behaviors on metaverse-based non-fungible token platforms

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their…

A meta-analysis of antecedents and consequences of trust in the sharing economy

Trust plays a crucial role in overcoming uncertainty and reducing risks. Uncovering the trust mechanism in the sharing economy may enable sharing platforms to design more…

The role of para-social relationship in live streaming virtual gift purchase: a two-stage SEM-neural network analysis

Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social…

Motivational profile and knowledge creation in eSports: examining the roles of mutualistic co-presence

Research on knowledge creation within eSports learning is scarce. This study extends the understanding of competition-oriented collaborative learning in eSports by examining the…

Effect of consistency of the review set on causal attribution: the moderating roles of repeating purchase cues and product knowledge

The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and…

Longitudinal relationship between parental and adolescent smartphone addiction: serial mediating effects of adolescent self-esteem and depression

This study longitudinally investigated the predictors and mediators of adolescent smartphone addiction by examining the impact of parental smartphone addiction at T1 on adolescent…

Understanding users' voice assistant exploration intention: unraveling the differential mechanisms of the multiple dimensions of perceived intelligence

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs…

Moment or movement – the heterogeneous impact of the Black Lives Matter movement on personal and societal charitable crowdfunding campaigns

Whenever social injustice tackled by social movements receives heightened media attention, charitable crowdfunding platforms offer an opportunity to proactively advocate for…

Consumer vulnerability: understanding transparency and control in the online environment

In the online environment, consumers increasingly feel vulnerable due to firms’ expanding capabilities of collecting and using their data in an unsanctioned manner. Drawing from…

How does social media use in the workplace affect employee voice? Uncovering the mediation effects of social identity and contingency role of job-social media fit

Employee voice is crucial for organizations to identify problems and make timely adjustments. However, promoting voice in organizations is challenging. This study aims to…

How review content, sentiment and helpfulness votes jointly affect trust of reviews and attitude

We investigate the joint impacts of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of reviews and attitude toward the…

A mixed-methods investigation of the factors affecting the use of facial recognition as a threatening AI application

Artificial intelligence (AI) applications pose a potential threat to users' data security and privacy due to their high data-dependence nature. This paper aims to investigate an…

Effect of the fit between situational regulatory focus and feedback focus on customers' co-design behavior

Customers' co-design behavior is an important source of knowledge for product innovation. Firms can regulate the focus of information interaction with customers to set goals and…

Gaining customer engagement in social media recovery: the moderating roles of timeliness and personalization

This research investigates the role of customer forgiveness as the result of online service recovery transparency in predicting customer engagement. It also examines the…

Using machine learning to investigate consumers' emotions: the spillover effect of AI defeating people on consumers' attitudes toward AI companies

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative…

Factors influencing TikTok-based user purchase intention: comparison between potential customers and repeat customers

The purpose of this paper is to examine how different types of user experience in TikTok impact purchase intention via commitment to the influencer and commitment to the platform…

Fake news detection using machine learning: an adversarial collaboration approach

Purveyors of fake news perpetuate information that can harm society, including businesses. Social media's reach quickly amplifies distortions of fake news. Research has not yet…

What enhances or worsens the user-generated metaverse experience? An application of BERTopic to Roblox user eWOM

Given its growing economic potential and social impact, this study aims to understand the motivations and concerns regarding metaverse usage. It identifies user needs and risks…

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AoIR Selected Papers of Internet Research

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Selected Papers in Internet Research 2023. Research from the Annual Conference of the Association of Internet Researchers

WARTOK: NETWORKED SOUNDSCAPES OF MEMETIC WARFARE

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The Internet and the Pandemic

90% of Americans say the internet has been essential or important to them, many made video calls and 40% used technology in new ways. But while tech was a lifeline for some, others faced struggles

Table of contents.

1. How the internet and technology shaped Americans’ personal experiences amid COVID-19
2. Parents, their children and school during the pandemic
3. Navigating technological challenges
4. The role of technology in COVID-19 vaccine registration
Acknowledgments
Methodology

Pew Research Center has a long history of studying technology adoption trends and the impact of digital technology on society. This report focuses on American adults’ experiences with and attitudes about their internet and technology use during the COVID-19 outbreak. For this analysis, we surveyed 4,623 U.S. adults from April 12-18, 2021. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Chapter 1 of this report includes responses to an open-ended question and the overall report includes a number of quotations to help illustrate themes and add nuance to the survey findings. Quotations may have been lightly edited for grammar, spelling and clarity. The first three themes mentioned in each open-ended response, according to a researcher-developed codebook, were coded into categories for analysis.

Here are the questions used for this report , along with responses, and its methodology .

Technology has been a lifeline for some during the coronavirus outbreak but some have struggled, too

The coronavirus has transformed many aspects of Americans’ lives. It shut down schools, businesses and workplaces and forced millions to stay at home for extended lengths of time. Public health authorities recommended limits on social contact to try to contain the spread of the virus, and these profoundly altered the way many worked, learned, connected with loved ones, carried out basic daily tasks, celebrated and mourned. For some, technology played a role in this transformation.

Results from a new Pew Research Center survey of U.S. adults conducted April 12-18, 2021, reveal the extent to which people’s use of the internet has changed, their views about how helpful technology has been for them and the struggles some have faced.

The vast majority of adults (90%) say the internet has been at least important to them personally during the pandemic, the survey finds. The share who say it has been essential – 58% – is up slightly from 53% in April 2020. There have also been upticks in the shares who say the internet has been essential in the past year among those with a bachelor’s degree or more formal education, adults under 30, and those 65 and older.

A large majority of Americans (81%) also say they talked with others via video calls at some point since the pandemic’s onset. And for 40% of Americans, digital tools have taken on new relevance: They report they used technology or the internet in ways that were new or different to them. Some also sought upgrades to their service as the pandemic unfolded: 29% of broadband users did something to improve the speed, reliability or quality of their high-speed internet connection at home since the beginning of the outbreak.

Still, tech use has not been an unmitigated boon for everyone. “ Zoom fatigue ” was widely speculated to be a problem in the pandemic, and some Americans report related experiences in the new survey: 40% of those who have ever talked with others via video calls since the beginning of the pandemic say they have felt worn out or fatigued often or sometimes by the time they spend on them. Moreover, changes in screen time occurred for Americans generally and for parents of young children . The survey finds that a third of all adults say they tried to cut back on time spent on their smartphone or the internet at some point during the pandemic. In addition, 72% of parents of children in grades K-12 say their kids are spending more time on screens compared with before the outbreak. 1

For many, digital interactions could only do so much as a stand-in for in-person communication. About two-thirds of Americans (68%) say the interactions they would have had in person, but instead had online or over the phone, have generally been useful – but not a replacement for in-person contact. Another 15% say these tools haven’t been of much use in their interactions. Still, 17% report that these digital interactions have been just as good as in-person contact.

About two-thirds say digital interactions have been useful, but not a replacement for in-person contact

Some types of technology have been more helpful than others for Americans. For example, 44% say text messages or group messaging apps have helped them a lot to stay connected with family and friends, 38% say the same about voice calls and 30% say this about video calls. Smaller shares say social media sites (20%) and email (19%) have helped them in this way.

The survey offers a snapshot of Americans’ lives just over one year into the pandemic as they reflected back on what had happened. It is important to note the findings were gathered in April 2021, just before all U.S. adults became eligible for coronavirus vaccine s. At the time, some states were beginning to loosen restrictions on businesses and social encounters. This survey also was fielded before the delta variant became prominent in the United States, raising concerns about new and evolving variants .

Here are some of the key takeaways from the survey.

Americans’ tech experiences in the pandemic are linked to digital divides, tech readiness

Some Americans’ experiences with technology haven’t been smooth or easy during the pandemic. The digital divides related to internet use and affordability were highlighted by the pandemic and also emerged in new ways as life moved online.

For all Americans relying on screens during the pandemic, connection quality has been important for school assignments, meetings and virtual social encounters alike. The new survey highlights difficulties for some: Roughly half of those who have a high-speed internet connection at home (48%) say they have problems with the speed, reliability or quality of their home connection often or sometimes. 2

Beyond that, affordability remained a persistent concern for a portion of digital tech users as the pandemic continued – about a quarter of home broadband users (26%) and smartphone owners (24%) said in the April 2021 survey that they worried a lot or some about paying their internet and cellphone bills over the next few months.

From parents of children facing the “ homework gap ” to Americans struggling to afford home internet , those with lower incomes have been particularly likely to struggle. At the same time, some of those with higher incomes have been affected as well.

60% of broadband users with lower incomes often or sometimes have connection problems, and 46% are worried at least some about paying for broadband

Affordability and connection problems have hit broadband users with lower incomes especially hard. Nearly half of broadband users with lower incomes, and about a quarter of those with midrange incomes, say that as of April they were at least somewhat worried about paying their internet bill over the next few months. 3 And home broadband users with lower incomes are roughly 20 points more likely to say they often or sometimes experience problems with their connection than those with relatively high incomes. Still, 55% of those with lower incomes say the internet has been essential to them personally in the pandemic.

At the same time, Americans’ levels of formal education are associated with their experiences turning to tech during the pandemic.

Adults with a bachelor’s, advanced degree more likely than others to make daily video calls, use tech in new ways, consider internet essential amid COVID-19

Those with a bachelor’s or advanced degree are about twice as likely as those with a high school diploma or less formal education to have used tech in new or different ways during the pandemic. There is also roughly a 20 percentage point gap between these two groups in the shares who have made video calls about once a day or more often and who say these calls have helped at least a little to stay connected with family and friends. And 71% of those with a bachelor’s degree or more education say the internet has been essential, compared with 45% of those with a high school diploma or less.

More broadly, not all Americans believe they have key tech skills. In this survey, about a quarter of adults (26%) say they usually need someone else’s help to set up or show them how to use a new computer, smartphone or other electronic device. And one-in-ten report they have little to no confidence in their ability to use these types of devices to do the things they need to do online. This report refers to those who say they experience either or both of these issues as having “lower tech readiness.” Some 30% of adults fall in this category. (A full description of how this group was identified can be found in Chapter 3. )

‘Tech readiness,’ which is tied to people’s confident and independent use of devices, varies by age

These struggles are particularly acute for older adults, some of whom have had to learn new tech skills over the course of the pandemic. Roughly two-thirds of adults 75 and older fall into the group having lower tech readiness – that is, they either have little or no confidence in their ability to use their devices, or generally need help setting up and learning how to use new devices. Some 54% of Americans ages 65 to 74 are also in this group.

Americans with lower tech readiness have had different experiences with technology during the pandemic. While 82% of the Americans with lower tech readiness say the internet has been at least important to them personally during the pandemic, they are less likely than those with higher tech readiness to say the internet has been essential (39% vs. 66%). Some 21% of those with lower tech readiness say digital interactions haven’t been of much use in standing in for in-person contact, compared with 12% of those with higher tech readiness.

46% of parents with lower incomes whose children faced school closures say their children had at least one problem related to the ‘homework gap’

As school moved online for many families, parents and their children experienced profound changes. Fully 93% of parents with K-12 children at home say these children had some online instruction during the pandemic. Among these parents, 62% report that online learning has gone very or somewhat well, and 70% say it has been very or somewhat easy for them to help their children use technology for online instruction.

Still, 30% of the parents whose children have had online instruction during the pandemic say it has been very or somewhat difficult for them to help their children use technology or the internet for this.

Remote learning has been widespread during the pandemic, but children from lower-income households have been particularly likely to face ‘homework gap’

The survey also shows that children from households with lower incomes who faced school closures in the pandemic have been especially likely to encounter tech-related obstacles in completing their schoolwork – a phenomenon contributing to the “ homework gap .”

Overall, about a third (34%) of all parents whose children’s schools closed at some point say their children have encountered at least one of the tech-related issues we asked about amid COVID-19: having to do schoolwork on a cellphone, being unable to complete schoolwork because of lack of computer access at home, or having to use public Wi-Fi to finish schoolwork because there was no reliable connection at home.

This share is higher among parents with lower incomes whose children’s schools closed. Nearly half (46%) say their children have faced at least one of these issues. Some with higher incomes were affected as well – about three-in-ten (31%) of these parents with midrange incomes say their children faced one or more of these issues, as do about one-in-five of these parents with higher household incomes.

More parents say their screen time rules have become less strict under pandemic than say they’ve become more strict

Prior Center work has documented this “ homework gap ” in other contexts – both before the coronavirus outbreak and near the beginning of the pandemic . In April 2020, for example, parents with lower incomes were particularly likely to think their children would face these struggles amid the outbreak.

Besides issues related to remote schooling, other changes were afoot in families as the pandemic forced many families to shelter in place. For instance, parents’ estimates of their children’s screen time – and family rules around this – changed in some homes. About seven-in-ten parents with children in kindergarten through 12th grade (72%) say their children were spending more time on screens as of the April survey compared with before the outbreak. Some 39% of parents with school-age children say they have become less strict about screen time rules during the outbreak. About one-in-five (18%) say they have become more strict, while 43% have kept screen time rules about the same.

More adults now favor the idea that schools should provide digital technology to all students during the pandemic than did in April 2020

Americans’ tech struggles related to digital divides gained attention from policymakers and news organizations as the pandemic progressed.

On some policy issues, public attitudes changed over the course of the outbreak – for example, views on what K-12 schools should provide to students shifted. Some 49% now say K-12 schools have a responsibility to provide all students with laptop or tablet computers in order to help them complete their schoolwork during the pandemic, up 12 percentage points from a year ago.

Growing shares across political parties say K-12 schools should give all students computers amid COVID-19

The shares of those who say so have increased for both major political parties over the past year: This view shifted 15 points for Republicans and those who lean toward the GOP, and there was a 9-point increase for Democrats and Democratic leaners.

However, when it comes to views of policy solutions for internet access more generally, not much has changed. Some 37% of Americans say that the government has a responsibility to ensure all Americans have high-speed internet access during the outbreak, and the overall share is unchanged from April 2020 – the first time Americans were asked this specific question about the government’s pandemic responsibility to provide internet access. 4

Democrats are more likely than Republicans to say the government has this responsibility, and within the Republican Party, those with lower incomes are more likely to say this than their counterparts earning more money.

Video calls and conferencing have been part of everyday life

Americans’ own words provide insight into exactly how their lives changed amid COVID-19. When asked to describe the new or different ways they had used technology, some Americans mention video calls and conferencing facilitating a variety of virtual interactions – including attending events like weddings, family holidays and funerals or transforming where and how they worked. 5 From family calls, shopping for groceries and placing takeout orders online to having telehealth visits with medical professionals or participating in online learning activities, some aspects of life have been virtually transformed:

“I’ve gone from not even knowing remote programs like Zoom even existed, to using them nearly every day.” – Man, 54

“[I’ve been] h andling … deaths of family and friends remotely, attending and sharing classical music concerts and recitals with other professionals, viewing [my] own church services and Bible classes, shopping. … Basically, [the internet has been] a lifeline.” – Woman, 69

“I … use Zoom for church youth activities. [I] use Zoom for meetings. I order groceries and takeout food online. We arranged for a ‘digital reception’ for my daughter’s wedding as well as live streaming the event.” – Woman, 44

Among those who have used video calls during the outbreak, 40% feel fatigued or worn out at least sometimes from time spent on these calls

When asked about video calls specifically, half of Americans report they have talked with others in this way at least once a week since the beginning of the outbreak; one-in-five have used these platforms daily. But how often people have experienced this type of digital connectedness varies by age. For example, about a quarter of adults ages 18 to 49 (27%) say they have connected with others on video calls about once a day or more often, compared with 16% of those 50 to 64 and just 7% of those 65 and older.

Even as video technology became a part of life for users, many accounts of burnout surfaced and some speculated that “Zoom fatigue” was setting in as Americans grew weary of this type of screen time. The survey finds that some 40% of those who participated in video calls since the beginning of the pandemic – a third of all Americans – say they feel worn out or fatigued often or sometimes from the time they spend on video calls. About three-quarters of those who have been on these calls several times a day in the pandemic say this.

Fatigue is not limited to frequent users, however: For example, about a third (34%) of those who have made video calls about once a week say they feel worn out at least sometimes.

These are among the main findings from the survey. Other key results include:

Some Americans’ personal lives and social relationships have changed during the pandemic: Some 36% of Americans say their own personal lives changed in a major way as a result of the coronavirus outbreak. Another 47% say their personal lives changed, but only a little bit. About half (52%) of those who say major change has occurred in their personal lives due to the pandemic also say they have used tech in new ways, compared with about four-in-ten (38%) of those whose personal lives changed a little bit and roughly one-in-five (19%) of those who say their personal lives stayed about the same.

Even as tech helped some to stay connected, a quarter of Americans say they feel less close to close family members now compared with before the pandemic, and about four-in-ten (38%) say the same about friends they know well. Roughly half (53%) say this about casual acquaintances.

The majority of those who tried to sign up for vaccine appointments in the first part of the year went online to do so: Despite early problems with vaccine rollout and online registration systems , in the April survey tech problems did not appear to be major struggles for most adults who had tried to sign up online for COVID-19 vaccines. The survey explored Americans’ experiences getting these vaccine appointments and reveals that in April 57% of adults had tried to sign themselves up and 25% had tried to sign someone else up. Fully 78% of those who tried to sign themselves up and 87% of those who tried to sign others up were online registrants.

When it comes to difficulties with the online vaccine signup process, 29% of those who had tried to sign up online – 13% of all Americans – say it was very or somewhat difficult to sign themselves up for vaccines at that time. Among five reasons for this that the survey asked about, the most common major reason was lack of available appointments, rather than tech-related problems. Adults 65 and older who tried to sign themselves up for the vaccine online were the most likely age group to experience at least some difficulty when they tried to get a vaccine appointment.

Tech struggles and usefulness alike vary by race and ethnicity. Americans’ experiences also have varied across racial and ethnic groups. For example, Black Americans are more likely than White or Hispanic adults to meet the criteria for having “lower tech readiness.” 6 Among broadband users, Black and Hispanic adults were also more likely than White adults to be worried about paying their bills for their high-speed internet access at home as of April, though the share of Hispanic Americans who say this declined sharply since April 2020. And a majority of Black and Hispanic broadband users say they at least sometimes have experienced problems with their internet connection.

Still, Black adults and Hispanic adults are more likely than White adults to say various technologies – text messages, voice calls, video calls, social media sites and email – have helped them a lot to stay connected with family and friends amid the pandemic.

Tech has helped some adults under 30 to connect with friends, but tech fatigue also set in for some. Only about one-in-five adults ages 18 to 29 say they feel closer to friends they know well compared with before the pandemic. This share is twice as high as that among adults 50 and older. Adults under 30 are also more likely than any other age group to say social media sites have helped a lot in staying connected with family and friends (30% say so), and about four-in-ten of those ages 18 to 29 say this about video calls.

Screen time affected some negatively, however. About six-in-ten adults under 30 (57%) who have ever made video calls in the pandemic say they at least sometimes feel worn out or fatigued from spending time on video calls, and about half (49%) of young adults say they have tried to cut back on time spent on the internet or their smartphone.

Throughout this report, “parents” refers to those who said they were the parent or guardian of any children who were enrolled in elementary, middle or high school and who lived in their household at the time of the survey. ↩
People with a high-speed internet connection at home also are referred to as “home broadband users” or “broadband users” throughout this report. ↩
Family incomes are based on 2019 earnings and adjusted for differences in purchasing power by geographic region and for household sizes. Middle income is defined here as two-thirds to double the median annual family income for all panelists on the American Trends Panel. Lower income falls below that range; upper income falls above it. ↩
A separate Center study also fielded in April 2021 asked Americans what the government is responsible for on a number of topics, but did not mention the coronavirus outbreak. Some 43% of Americans said in that survey that the federal government has a responsibility to provide high-speed internet for all Americans. This was a significant increase from 2019, the last time the Center had asked that more general question, when 28% said the same. ↩
Quotations in this report may have been lightly edited for grammar, spelling and clarity. ↩
There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout this report. ↩

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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

This paper is in the following e-collection/theme issue:

Published on 14.8.2024 in Vol 26 (2024)

Cancer Prevention and Treatment on Chinese Social Media: Machine Learning–Based Content Analysis Study

Authors of this article:

Original Paper

Keyang Zhao 1 * , DPhil ;
Xiaojing Li 1, 2 * , Prof Dr ;
Jingyang Li 3 , DPhil

1 School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China

2 Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China

3 School of Software, Shanghai Jiao Tong University, Shanghai, China

*these authors contributed equally

Corresponding Author:

Xiaojing Li, Prof Dr

School of Media & Communication

Shanghai Jiao Tong University

800 Dongchuan Rd.

Minhang District

Shanghai, 200240

Phone: 86 13918611103

Fax:86 21 34207088

Email: [email protected]

Background: Nowadays, social media plays a crucial role in disseminating information about cancer prevention and treatment. A growing body of research has focused on assessing access and communication effects of cancer information on social media. However, there remains a limited understanding of the comprehensive presentation of cancer prevention and treatment methods across social media platforms. Furthermore, research comparing the differences between medical social media (MSM) and common social media (CSM) is also lacking.

Objective: Using big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM. This approach promises to enhance cancer coverage and assist patients in making informed treatment decisions.

Methods: We collected all posts (N=60,843) from 4 medical WeChat official accounts (accounts with professional medical backgrounds, classified as MSM in this paper) and 5 health and lifestyle WeChat official accounts (accounts with nonprofessional medical backgrounds, classified as CSM in this paper). We applied latent Dirichlet allocation topic modeling to extract cancer-related posts (N=8427) and identified 6 cancer themes separately in CSM and MSM. After manually labeling posts according to our codebook, we used a neural-based method for automated labeling. Specifically, we framed our task as a multilabel task and utilized different pretrained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe), to learn document-level semantic representations for labeling.

Results: We analyzed a total of 4479 articles from MSM and 3948 articles from CSM related to cancer. Among these, 35.52% (2993/8427) contained prevention information and 44.43% (3744/8427) contained treatment information. Themes in CSM were predominantly related to lifestyle, whereas MSM focused more on medical aspects. The most frequently mentioned prevention measures were early screening and testing, healthy diet, and physical exercise. MSM mentioned vaccinations for cancer prevention more frequently compared with CSM. Both types of media provided limited coverage of radiation prevention (including sun protection) and breastfeeding. The most mentioned treatment measures were surgery, chemotherapy, and radiotherapy. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention.

Conclusions: The information about cancer prevention and treatment on social media revealed a lack of balance. The focus was primarily limited to a few aspects, indicating a need for broader coverage of prevention measures and treatments in social media. Additionally, the study’s findings underscored the potential of applying machine learning to content analysis as a promising research approach for mapping key dimensions of cancer information on social media. These findings hold methodological and practical significance for future studies and health promotion.

Introduction

In 2020, 4.57 million new cancer cases were reported in China, accounting for 23.7% of the world’s total [ 1 ]. Many of these cancers, however, can be prevented [ 2 , 3 ]. According to the World Health Organization (WHO), 30%-50% of cancers could be avoided through early detection and by reducing exposure to known lifestyle and environmental risks [ 4 ]. This underscores the imperative to advance education on cancer prevention and treatment.

Mass media serves not only as a primary channel for disseminating cancer information but also as a potent force in shaping the public health agenda [ 5 , 6 ]. Previous studies have underscored the necessity of understanding how specific cancer-related content is presented in the media. For example, the specific cancer types frequently mentioned in news reports have the potential to influence the public’s perception of the actual incidence of cancer [ 7 ].

Nowadays, social media plays an essential role in disseminating health information, coordinating resources, and promoting health campaigns aimed at educating individuals about prevention measures [ 8 ]. Additionally, it influences patients’ decision-making processes regarding treatment [ 9 ]. A study revealed that social media use correlates with increased awareness of cancer screening in the general population [ 10 ]. In recent years, there has been a notable surge in studies evaluating cancer-related content on social media. However, previous studies often focused on specific cancer types [ 11 ] and limited aspects of cancer-related issues [ 12 ]. The most recent comprehensive systematic content analysis of cancer coverage, conducted in 2013, indicated that cancer news coverage has heavily focused on treatment, while devoting very little attention to prevention, detection, or coping [ 13 ].

Evaluating cancer prevention information on social media is crucial for future efforts by health educators and cancer control organizations. Moreover, providing reliable medical information to individuals helps alleviate feelings of fear and uncertainty [ 14 ]. Specifically, patients often seek information online when making critical treatment decisions, such as chemotherapy [ 15 ]. Therefore, it is significant to comprehensively evaluate the types of treatment information available on social media.

Although many studies have explored cancer-related posts from the perspectives of patients with cancer [ 16 ] and caregivers [ 17 ], the analysis of posts from medical professionals has been found to be inadequate [ 18 ]. This paradox arises from the expectation that medical professionals, given their professional advantages, should take the lead in providing cancer education on social media. Nevertheless, a significant number of studies have highlighted the prevalence of unreliable medical information on social media [ 19 ]. A Japanese study highlighted a concerning phenomenon: despite efforts by medical professionals to promote cancer screening online, a significant number of antiscreening activists disseminated contradictory messages on the internet, potentially undermining the effectiveness of cancer education initiatives [ 20 ]. Hence, there is an urgent need for the accurate dissemination of health information on social media, with greater involvement from scientists or professional institutions, to combat the spread of misinformation [ 21 ]. Despite efforts to study professional medical websites [ 22 ] and apps [ 23 ], there remains a lack of comprehensive understanding of the content posted on medical social media (MSM). Further study is thus needed to compare the differences between cancer information on social media from professional medical sources and nonprofessional sources to enhance cancer education.

For this study, we defined social media as internet-based platforms characterized by social interactive functions such as reading, commenting, retweeting, and timely interaction [ 24 ]. Based on this definition, we further classified 2 types of media based on ownership, content, and contributors: common social media (CSM) and MSM. MSM refers to social media platforms owned by professional medical institutions or organizations. It primarily provides medical and health information by medical professionals, including medical-focused accounts on social media and mobile health apps. CSM refers to social media owned or managed by individuals without medical backgrounds. It mainly provides health and lifestyle content.

Similar to Facebook (Meta Platforms, Inc.), WeChat (Tencent Holdings Limited) is the most popular social media platform in China, installed on more than 90% of smartphones. Zhang et al [ 25 ] has indicated that 63.26% of people prefer to obtain health information from WeChat. Unlike other Chinese social media platforms, WeChat has a broader user base that spans various age groups [ 26 ]. WeChat Public Accounts (WPAs) operate within the WeChat platform, offering services and information to the public. Many hospitals and primary care institutions in China have increasingly registered WPAs to provide health care services, medical information, health education, and more [ 27 ]. Therefore, this study selected WPA as the focus of research.

Based on big data analytics, this study aims to comprehensively map the characteristics of cancer treatment and prevention information on MSM and CSM, which could significantly enhance cancer coverage and assist patients in treatment decision-making. To address the aforementioned research gaps, 2 research questions were formulated.

Research question 1: What are the characteristics of cancer prevention information discussed on social media? What are the differences between MSM and CSM?
Research question 2: What are the characteristics of cancer treatment information discussed on social media? What are the differences between MSM and CSM?

Data Collection and Processing

We selected representative WPAs based on the reports from the “Ranking of Influential Health WeChat Public Accounts” [ 28 ] and the “2021 National Rankings of Best Hospitals by Specialty” [ 29 ]. In this study, we focused on 4 medical WPAs within MSM: Doctor Dingxiang (丁香医生), 91Huayi (华医网), The Cancer Hospital of Chinese Academy of Medical Sciences (中国医学科学院肿瘤医院), and Fudan University Shanghai Cancer Center (复旦大学附属肿瘤医院). We also selected 5 health and lifestyle WeChat Official Accounts classified as CSM for this study: Health Times (健康时报), Family Doctor (家庭医生), CCTV Lifestyle (CCTV 生活圈), Road to Health (健康之路), and Life Times (生命时报).

We implemented a Python-based (Python Foundation) crawler to retrieve posts from the aforementioned WPAs. Subsequently, we implemented a filtration process to eliminate noisy and unreliable data. Note that our focus is on WPAs that provide substantial information, defined as containing no fewer than a certain number of characters. We have deleted documents that contain less than 100 Chinese characters. Furthermore, we have removed figures and videos from the remaining documents. Eventually, we conducted an analysis at the paragraph level. According to our findings from random sampling, noise in articles from WPAs mostly originates from advertisements, which are typically found in specific paragraphs. Therefore, we retained only paragraphs that did not contain advertising keywords. In total, we collected 60,843 posts from these WPAs, comprising 20,654 articles from MSM and 40,189 articles from CSM.

The workflow chart in Figure 1 depicts all procedures following data collection and preprocessing. After obtaining meaningful raw documents, we performed word-level segmentation on the texts. We then removed insignificant stopwords and replaced specific types of cancers with a general term to facilitate coarse-grained latent Dirichlet allocation (LDA)–based filtering. Subsequently, we conducted fine-grained LDA topic modeling on the filtered documents without replacing keywords to visualize the topics extracted from the WPAs. Furthermore, we utilized a manually labeled codebook to train a long short-term memory (LSTM) network for document classification into various categories. Finally, we performed data analysis using both the topic distribution derived from fine-grained LDA and the classified documents.

Latent Dirichlet Allocation Topic Modeling

LDA is a generative statistical model that explains sets of observations by latent groups, revealing why some parts of the data are similar [ 30 ]. The LDA algorithm can speculate on the topic distribution of a document.

When comparing LDA with other natural language processing methods such as LSTM-based deep learning, it is worth noting that LDA stands out as an unsupervised learning algorithm. Unlike its counterparts, LDA has the ability to uncover hidden topics without relying on labeled training data. Its strength lies in its capability to automatically identify latent topics within documents by analyzing statistical patterns of word co-occurrences. In addition, LDA provides interpretable outcomes by assigning a probability distribution to each document, representing its association with various topics. Similarly, it assigns a probability distribution to each topic, indicating the prevalence of specific words within that topic. This feature enables researchers to understand the principal themes present in their corpus and the extent to which these themes are manifested in individual documents.

The foundational principle of LDA involves using probabilistic inference to estimate the distribution of topics and word allocations. Specifically, LDA assumes that each document is composed of a mixture of a small number of topics, and each word’s presence can be attributed to one of these topics. This approach allows for overlapping content among documents, rather than strict categorization into separate groups. For a deeper understanding of the technical and theoretical aspects of the LDA algorithm, readers are encouraged to refer to the research conducted by Blei et al [ 30 ]. In this context, our primary focus was on the application of the algorithm to our corpus, and the procedure is outlined in the following sections.

Document Selection

Initially, document selection involves using a methodological approach to sample documents from the corpus, which may include random selection or be guided by predetermined criteria such as document relevance or popularity within the social media context.

Topic Inference

Utilizing LDA or a similar topic modeling technique, we infer the underlying topical structure within each document. This involves modeling documents as mixtures of latent topics represented by a Dirichlet distribution, from which topic proportions are sampled.

Topic Assignment to Words

After determining topic proportions, we proceed to assign topics to individual words in the document. Using a multinomial distribution, each word is probabilistically associated with one of the inferred topics based on the previously derived topic proportions.

Word Distribution Estimation

Each topic is characterized by a distinct distribution over the vocabulary, representing the likelihood of observing specific words within that topic. Using a Dirichlet distribution, we estimate the word distribution for each inferred topic.

Word Generation

Finally, using the multinomial distribution again, we generate words for the document by sampling from the estimated word distribution corresponding to the topic assigned to each word. This iterative process produces synthetic text that mirrors the statistical properties of the original corpus.

To filter out noncancer-related documents in our case, we replaced cancer-related words with “癌症” (cancer or tumor in Chinese) in all documents. We then conducted an LDA analysis to compute the topic distribution of each document and retained documents related to topics where “癌症” appears among the top 10 words.

In our study, we used Python packages such as jieba and gensim for document segmentation and extracting per-topic-per-word probabilities from the model. During segmentation, we applied a stopword dictionary to filter out meaningless words and transformed each document into a cleaned version containing only meaningful words.

During the LDA analysis, to determine the optimal number of topics, our main goal was to compute the topic coherence for various numbers of topics and select the model that yielded the highest coherence score. Coherence measures the interpretability of each topic by assessing whether the words within the same topic are logically associated with each other. The higher the score for a specific number k , the more closely related the words are within that topic. In this phase, we used the Python package pyLDAvis to compare coherence scores with different numbers of topics. Subsequently, we filtered and retained only the documents related to cancer topics, resulting in 4479 articles from MSM and 3948 articles from CSM.

Among the filtered articles, we conducted another LDA analysis to extract topics from the original articles without replacing cancer-related words. Using pyLDAvis, we calculated the coherence score and identified 6 topics for both MSM and CSM articles.

To visualize the topic modeling results, we created bar graphs where the y-axis indicates the top 10 keywords associated with each topic, and the x-axis represents the weight of each keyword (indicating its contribution to the topic). At the bottom of each graph ( Figures 2 and 3 ), we generalized and presented the name of each topic based on the top 10 most relevant keywords.

Manual Content Analysis: Coding Procedure

Based on the codebook, 2 independent coders (KZ and JL) engaged in discussions regarding the coding rules to ensure a shared understanding of the conceptual and operational distinctions among the coding items. To ensure the reliability of the coding process, both coders independently coded 100 randomly selected articles. Upon completion of the pilot coding, any disagreements were resolved through discussion between the 2 coders.

For the subsequent coding phase, each coder was assigned an equitable proportion of articles, with 10% of the cancer-related articles randomly sampled from both MSM samples (450/4479) and CSM samples (394/3948). Manual coding was performed on a total of 844 articles, which served as the training data set for the machine learning model. The operational definitions of each coding variable are detailed in Multimedia Appendix 1 .

Coding Measures

Cancer prevention measures.

Coders identified whether an article mentioned any of the following cancer prevention measures [ 31 - 35 ]: (1) avoid tobacco use, (2) maintain a healthy weight, (3) healthy diet, (4) exercise regularly, (5) limit alcohol use, (6) get vaccinated, (7) reduce exposure to ultraviolet radiation and ionizing radiation, (8) avoid urban air pollution and indoor smoke from household use of solid fuels, (9) early screening and detection, (10) breastfeeding, (11) controlling chronic infections, and (12) other prevention measures.

Cancer Treatment Measures

Coders identified whether an article mentioned any of the following treatments [ 36 ]: (1) surgery (including cryotherapy, lasers, hyperthermia, photodynamic therapy, cuts with scalpels), (2) radiotherapy, (3) chemotherapy, (4) immunotherapy, (5) targeted therapy, (6) hormone therapy, (7) stem cell transplant, (8) precision medicine, (9) cancer biomarker testing, and (10) other treatment measures.

Neural-Based Machine Learning

In this part, we attempted to label each article using a neural network. As mentioned earlier, we manually labeled 450 MSM articles and 394 CSM articles. We divided the labeled data into a training set and a test set with a ratio of 4:1. We adopted the pretrained Bidirectional Encoder Representations from Transformers (BERT) model. As BERT can only accept inputs with fewer than 512 tokens [ 37 ], we segmented each document into pieces of 510 tokens (accounting for BERT’s automatic [CLS] and [SEP] tokens, where [CLS] denotes the start of a sentence or a document, and [SEP] denotes the end of a sentence or a document) with an overlap of 384 tokens between adjacent pieces. We began by utilizing a BERT-based encoder to encode each piece and predict its labels using a multioutput decoder. After predicting labels for each piece, we pooled the outputs for all pieces within the same document and used an LSTM network to predict final labels for each document.

Ethical Considerations

This study did not require institutional research board review as it did not involve interactions with humans or other living entities, private or personally identifiable information, or any pharmaceuticals or medical devices. The data set consists solely of publicly available social media posts.

Cancer Topics on Social Media

Applying LDA, we identified 6 topics each for MSM and CSM articles. The distribution of topics among MSM and CSM is presented in Table 1 , while the keyword weights for each topic are illustrated in Figures 2 and 3 .

Media type and topic number			Topic description		Articles, n (%)		Top 10 keywords

	Topic 1	Liver cancer and stomach cancer		1519 (18.03)		Cancer (癌症), liver cancer (肝癌), stomach cancer (胃癌), factors (因素), food (食物), disease (疾病), (幽门), exercise (运动), patient (患者), and diet (饮食)
	Topic 2	Female and cancer		1611 (19.12)		Breast cancer (乳腺癌), female (女性), patient (患者), lung cancer (肺癌), surgery (手术), tumor (肿瘤), mammary gland (乳腺), expert (专家), ovarian cancer (卵巢癌), and lump (结节)
	Topic 3	Breast cancer		1093 (12.97)		Breast cancer (乳腺癌), surgery (手术), thyroid (甲状腺), lump (结节), breast (乳房), patient (患者), female (女性), screening and testing (检查), mammary gland (乳腺), and tumor (肿瘤)
	Topic 4	Cervical cancer		1019 (12.09)		Vaccine (疫苗), cervical cancer (宫颈癌), virus (病毒), cervix (宫颈), patient (患者), nation (国家), female (女性), nasopharynx cancer (鼻咽癌), medicine (药品), and hospital (医院)
	Topic 5	Clinical cancer treatment		2548 (30.24)		Tumor (肿瘤), patient (患者), screening (检查), chemotherapy (化疗), clinic (临床), symptom (症状), hospital (医院), surgery (手术), medicine (药物), and disease (疾病)
	Topic 6	Diet and cancer risk		1741 (20.66)		Patient (患者), tumor (肿瘤), food (食物), polyp (息肉), professor (教授), nutrition (营养), expert (专家), surgery (手术), cancer (癌症), and disease (疾病)

	Topic 1	Cancer-causing substances		1136 (13.48)		Foods (食物), nutrition (营养), carcinogen (致癌物), food (食品), ingredient (含量), vegetable (蔬菜), cancer (癌症), body (人体), lump (结节), and formaldehyde (甲醛)
	Topic 2	Cancer treatment		1319 (15.65)		Patient (患者), cancer (癌症), hospital (医院), lung cancer (肺癌), tumor (肿瘤), medicine (药物), disease (疾病), professor (教授), surgery (手术), and clinic (临床)
	Topic 3	Female and cancer risk		1599 (18.97)		Screening and testing (检查), female (女性), disease (疾病), breast cancer (乳腺癌), cancer (癌症), lung cancer (肺癌), patient (患者), body (身体), tumor (肿瘤), and risk (风险)
	Topic 4	Exercise, diet, and cancer risk		1947 (23.10)		Cancer (癌症), exercise (运动), food (食物), risk (风险), body (身体), disease (疾病), suggestion (建议), patient (患者), fat (脂肪), and hospital (医院)
	Topic 5	Screening and diagnosis of cancer		1790 (21.24)		Screening and testing (检查), disease (疾病), hospital (医院), stomach cancer (胃癌), symptom (症状), patient (患者), cancer (癌症), liver cancer (肝癌), female (女性), and suggestion (建议)
	Topic 6	Disease and body parts		869 (10.31)		Disease (疾病), intestine (肠道), food (食物), hospital (医院), oral cavity (口腔), patient (患者), teeth (牙齿), cancer (癌症), ovary (卵巢), and garlic (大蒜)

a In each article, different topics may appear at the same time. Therefore, the total frequency of each topic did not equate to the total number of 8427 articles.

b To ensure the accuracy of the results, directly translating sampled texts from Chinese into English posed challenges due to differences in semantic elements. In English, cancer screening refers to detecting the possibility of cancer before symptoms appear, while diagnostic tests confirm the presence of cancer after symptoms are observed. However, in Chinese, the term “检查” encompasses both meanings. Therefore, we translated it as both screening and testing.

Among MSM articles, topic 5 was the most frequent (2548/8427, 30.24%), followed by topic 6 (1741/8427, 20.66%) and topic 2 (1611/8427, 19.12%). Both topics 5 and 6 focused on clinical treatments, with topic 5 specifically emphasizing cancer diagnosis. The keywords in topic 6, such as “polyp,” “tumor,” and “surgery,” emphasized the risk and diagnosis of precancerous lesions. Topic 2 primarily focused on cancer surgeries related to breast cancer, lung cancer, and ovarian cancer. The results indicate that MSM articles concentrated on specific cancers with higher incidence in China, including stomach cancer, liver cancer, lung cancer, breast cancer, and cervical cancer [ 10 ].

On CSM, topic 4 (1947/8427, 23.10%) had the highest proportion, followed by topic 5 (1790/8427, 21.24%) and topic 3 (1599/8427, 18.97%). Topic 6 had the smallest proportion. Topics 1 and 4 were related to lifestyle. Topic 1 particularly focused on cancer-causing substances, with keywords such as “food,” “nutrition,” and “carcinogen” appearing most frequently. Topic 4 was centered around exercise, diet, and their impact on cancer risk. Topics 3 and 5 were oriented toward cancer screening and diagnosis. Topic 3 specifically focused on female-related cancers, with discussions prominently featuring breast cancer screening and testing. Topic 5 emphasized early detection and diagnosis of stomach and lung cancers, highlighting keywords such as “screening” and “symptom.”

Cancer Prevention Information

Our experiment on the test set showed that the machine learning model achieved F 1 -scores above 85 for both prevention and treatment categories in both MSM and CSM. For subclasses within prevention and treatment, we achieved F 1 -scores of at least 70 for dense categories (with an occurrence rate >10%, ie, occurs in >1 of 10 entries) and at least 50 for sparse categories (with an occurrence rate <10%, ie, occurs in <1 of 10 entries). Subsequently, we removed items labeled as “other prevention measures” and “other treatment measures” due to semantic ambiguity.

Table 2 presents the distribution of cancer prevention information across MSM (n=4479) and CSM (n=3948).

Type of cancer prevention measures	Number of articles on MSM (n=4479), n (%)	Number of articles on CSM (n=3948), n (%)
Articles containing prevention information	1137 (25.39)	1856 (47.01)
Early screening and testing	737 (16.45)	1085 (27.48)
Healthy diet	278 (6.21)	598 (15.15)
Get vaccinated	261 (5.83)	113 (2.86)
Avoid tobacco use	186 (4.15)	368 (9.32)
Exercise regularly	135 (3.01)	661 (16.74)
Limit alcohol use	128 (2.86)	281 (7.12)
Avoid urban air pollution and indoor smoke from household use of solid fuels	19 (0.42)	64 (1.62)
Maintain a healthy weight	18 (0.40)	193 (4.89)
Practice safe sex	12 (0.27)	4 (0.10)
Controlling chronic infections	3 (0.07)	32 (0.81)
Reduce exposure to radiation	2 (0.04)	1 (0.03)
Breastfeeding	1 (0.02)	1 (0.03)

a MSM: medical social media.

b CSM: common social media.

Cancer Prevention Information on MSM

The distribution of cancer prevention information on MSM (n=4479) is as follows: articles discussing prevention measures accounted for 25.39% (1137/4479) of all MSM cancer-related articles. The most frequently mentioned measure was “early screening and testing” (737/4479, 16.45%). The second and third most frequently mentioned prevention measures were “healthy diet” (278/4479, 6.21%) and “get vaccinated” (261/4479, 5.83%). The least mentioned prevention measures were “controlling chronic infections” (3/4479, 0.07%), “reduce exposure to radiation” (2/4479, 0.04%), and “breastfeeding” (1/4479, 0.02%), each appearing in only 1-3 articles.

Cancer Prevention Information on CSM

As many as 1856 out of 3948 (47.01%) articles on CSM referred to cancer prevention information. Among these, “early screening and testing” (1085/3948, 27.48%) was the most commonly mentioned prevention measure. “Exercise regularly” (661/3948, 16.74%) and “healthy diet” (598/3948, 15.15%) were the 2 most frequently mentioned lifestyle-related prevention measures. Additionally, “avoid tobacco use” accounted for 9.32% (368/3948) of mentions. Other lifestyle-related prevention measures were “limit alcohol use” (281/3948, 7.12%) and “maintain a healthy weight” (193/3948, 4.89%). The least mentioned prevention measures were “practice safe sex” (4/3948, 0.10%), “reduce exposure to radiation” (1/3948, 0.03%), and “breastfeeding” (1/3948, 0.03%), each appearing in only 1-4 articles.

Cancer Prevention Information on Social Media

Table 3 presents the overall distribution of cancer prevention information on social media (N=8427). Notably, CSM showed a stronger focus on cancer prevention (1856/3948, 47.01%) compared with MSM (1137/8427, 13.49%). Both platforms highlighted the importance of early screening and testing. However, MSM placed greater emphasis on vaccination as a prevention measure. In addition to lifestyle-related prevention measures, both CSM and MSM showed relatively less emphasis on avoiding exposure to environmental carcinogens, such as air pollution, indoor smoke, and radiation. “Breastfeeding” was the least mentioned prevention measure (2/3948, 0.05%) on both types of social media.

Type of cancer prevention measures	Number of articles on MSM , n (%)	Number of articles on CSM , n (%)	Number of articles overall (N=8427), n (%)
Articles containing prevention information	1137 (13.49)	1856 (22.02)	2993 (35.52)
Early screening and testing	737 (8.75)	1085 (12.88)	1822 (21.62)
Healthy diet	278 (3.30)	598 (7.10)	876 (10.40)
Get vaccinated	261 (3.10)	113 (1.34)	374 (4.44)
Avoid tobacco use	186 (2.21)	368 (4.37)	554 (6.57)
Exercise regularly	135 (1.60)	661 (7.84)	796 (9.45)
Limit alcohol use	128 (1.52)	281 (3.33)	409 (4.85)
Avoid urban air pollution and indoor smoke from household use of solid fuels	19 (0.23)	64 (0.76)	83 (0.98)
Maintain a healthy weight	18 (0.21)	193 (2.29)	211 (2.50)
Practice safe sex	12 (0.14)	4 (0.05)	16 (0.19)
Controlling chronic infections	3 (0.04)	32 (0.38)	35 (0.42)
Reduce exposure to radiation	2 (0.02)	1 (0.01)	3 (0.04)
Breastfeeding	1 (0.01)	1 (0.01)	2 (0.02)

Cancer Treatment Information

Table 4 presents the distribution of cancer treatment information on MSM (n=4479) and CSM (n=3948).

Type of cancer treatment measures	Number of articles on MSM (n=4479), n (%)	Number of articles on CSM (n=3948), n (%)
Articles containing treatment information	2966 (66.22)	778 (19.71)
Surgery	2045 (45.66)	419 (10.61)
Chemotherapy	1122 (25.05)	285 (7.22)
Radiation therapy	1108 (24.74)	232 (5.88)
Cancer biomarker testing	380 (8.48)	55 (1.39)
Targeted therapy	379 (8.46)	181 (4.58)
Immunotherapy	317 (7.08)	22 (0.56)
Hormone therapy	47 (1.05)	14 (0.35)
Stem cell transplantation therapy	5 (0.11)	0 (0)

Cancer Treatment Information on MSM

Cancer treatment information appeared in 66.22% (2966/4479) of MSM posts. “Surgery” was the most frequently mentioned treatment measure (2045/4479, 45.66%), followed by “chemotherapy” (1122/4479, 25.05%) and “radiation therapy” (1108/4479, 24.74%). The proportions of “cancer biomarker testing” (380/4479, 8.48%), “targeted therapy” (379/4479, 8.46%), and “immunotherapy” (317/4479, 7.08%) were comparable. Only a minimal percentage of articles (47/4479, 1.05%) addressed “hormone therapy.” Furthermore, “stem cell transplantation therapy” was mentioned in just 5 out of 4479 (0.11%) articles.

Cancer Treatment Information on CSM

Cancer treatment information accounted for only 19.71% (778/3948) of CSM posts. “Surgery” was the most frequently mentioned treatment measure (419/3948, 10.61%), followed by “chemotherapy” (285/3948, 7.22%) and “radiation therapy” (232/3948, 5.88%). Relatively, the frequency of “targeted therapy” (181/3948, 4.58%) was similar to that of the first 3 types. However, “cancer biomarker testing” (55/3948, 1.39%), “immunotherapy” (22/3948, 0.56%), and “hormone therapy” (14/3948, 0.35%) appeared rarely on CSM. Notably, there were no articles on CSM mentioning stem cell transplantation.

Cancer Treatment Information on Social Media

Table 5 shows the overall distribution of cancer treatment information on social media (N=8427). A total of 44.43% (3744/8427) of articles contained treatment information. MSM (2966/8427, 35.20%) discussed treatment information much more frequently than CSM (778/8427, 9.23%). Furthermore, the frequency of all types of treatment measures mentioned was higher on MSM than on CSM. The 3 most frequently mentioned types of treatment measures were surgery (2464/8427, 29.24%), chemotherapy (1407/8427, 16.70%), and radiation therapy (1340/8427, 15.90%). Relatively, MSM (380/8427, 4.51%) showed a higher focus on cancer biomarker testing compared with CSM (55/8427, 0.65%).

Type of cancer treatment measures	Number of articles on MSM , n (%)	Number of articles on CSM , n (%)	Number of articles overall (N=8427), n (%)
Articles containing treatment information	2966 (35.20)	778 (9.23)	3744 (44.43)
Surgery	2045 (24.27)	419 (4.97)	2464 (29.24)
Radiation therapy	1108 (13.15)	232 (2.75)	1340 (15.90)
Chemotherapy	1122 (13.31)	285 (3.38)	1407 (16.70)
Immunotherapy	317 (3.76)	22 (0.26)	339 (4.02)
Targeted therapy	379 (4.50)	181 (2.15)	560 (6.65)
Hormone therapy	47 (0.56)	14 (0.17)	61 (0.72)
Stem cell transplant	5 (0.06)	0 (0.00)	5 (0.06)
Cancer biomarker testing	380 (4.51)	55 (0.65)	435 (5.16)

Cancer Topics on MSM and CSM

In MSM, treatment-related topics constituted the largest proportion, featuring keywords related to medical examinations. Conversely, in CSM, the distribution of topics appeared more balanced, with keywords frequently associated with cancer risk and screening. Overall, the distribution of topics on MSM and CSM revealed that CSM placed greater emphasis on lifestyle factors and early screening and testing. Specifically, CSM topics focused more on early cancer screening and addressed cancer types with high incidence rates. By contrast, MSM topics centered more on clinical treatment, medical testing, and the cervical cancer vaccine in cancer prevention. Additionally, MSM focused on types of cancers that are easier to screen and prevent, including liver cancer, stomach cancer, breast cancer, cervical cancer, and colon cancer.

Cancer Prevention Information on MSM and CSM

Through content analysis, it was found that 35.52% (2993/8427) of articles on social media contained prevention information, and 44.43% (3744/8427) contained treatment information. Compared with MSM (1137/8427, 13.49%), CSM (2993/8427, 35.52%) focused more on prevention.

Primary prevention mainly involves adopting healthy behaviors to lower the risk of developing cancer, which has been proven to have long-term effects on cancer prevention. Secondary prevention focuses on inhibiting or reversing carcinogenesis, including early screening and detection, as well as the treatment or removal of precancerous lesions [ 38 ]. Compared with cancer screening and treatment, primary prevention is considered the most cost-effective approach to reducing the cancer burden.

From our results, “early screening and testing” (1822/8427, 21.62%) was the most frequently mentioned prevention measure on both MSM and CSM. According to a cancer study from China, behavioral risk factors were identified as the primary cause of cancer [ 10 ]. However, measures related to primary prevention were not frequently mentioned. Additionally, lifestyle-related measures such as “healthy diet,” “regular exercise,” “avoiding tobacco use,” and “limiting alcohol use” were mentioned much less frequently on MSM compared with CSM.

Furthermore, “avoiding tobacco use” (554/8427, 6.57%) and “limiting alcohol use” (409/8427, 4.85%) were rarely mentioned, despite tobacco and alcohol being the leading causes of cancer. In China, public policies on the production, sale, and consumption of alcohol are weaker compared with Western countries. Notably, traditional Chinese customs often promote the belief that moderate drinking is beneficial for health [ 39 ]. Moreover, studies indicated that the smoking rate among adult men exceeded 50% in 2015. By 2018, 25.6% of Chinese adults aged 18 and above were smokers, totaling approximately 282 million smokers in China (271 million males and 11 million females) [ 40 ]. These statistics align with the consistently high incidence of lung cancer among Chinese men [ 41 ]. Simultaneously, the incidence and mortality of lung cancer in Chinese women were more likely associated with exposure to second-hand smoke or occupation-related risk factors.

Although MSM (261/8427, 3.10%) mentioned vaccination more frequently than CSM (113/8427, 1.34%), vaccination was not widely discussed on social media overall (374/8427, 4.44%). The introduction of human papillomavirus vaccination in China has lagged for more than 10 years compared with Western countries. A bivalent vaccine was approved by the Chinese Food and Drug Administration in 2017 but has not been included in the national immunization schedules up to now [ 42 ].

According to the “European Code Against Cancer” [ 43 ], breastfeeding is recommended as a measure to prevent breast cancer. However, there were no articles mentioning the role of breastfeeding in preventing breast cancer on social media.

One of the least frequently mentioned measures was “radiation protection,” which includes sun protection. Although skin cancer is not as common in China as in Western countries, China has the largest population in the world. A study showed that only 55.2% of Chinese people knew that ultraviolet radiation causes skin cancer [ 33 ]. Additional efforts should be made to enhance public awareness of skin cancer prevention through media campaigns.

Overall, our results indicate that social media, especially MSM, focused more on secondary prevention. The outcomes of primary prevention are challenging to identify in individuals, and studies on cancer education may partly explain why primary prevention was often overlooked [ 44 ].

Cancer Treatment Information on MSM and CSM

Compared with a related content analysis study in the United States, our findings also indicate that the media placed greater emphasis on treatment [ 45 ]. Treatment information on MSM was more diverse than on CSM, with a higher proportion of the 3 most common cancer treatments—surgery, chemotherapy, and radiation therapy—mentioned on MSM compared with CSM. Notably, CSM (232/8427, 2.75%) mentioned radiation therapy less frequently compared with MSM (1108/8427, 13.15%), despite it being one of the most common cancer treatment measures in clinical practice.

In addition to common treatment methods, other approaches such as targeted therapy (560/8427, 6.65%) and immunotherapy (339/8427, 4.02%) were rarely discussed. This could be attributed to the high costs associated with these treatments. A study revealed that each newly diagnosed patient with cancer in China faced out-of-pocket expenses of US $4947, amounting to 57.5% of the family’s annual income, posing an unaffordable economic burden of 77.6% [ 46 ]. In 2017, the Chinese government released the National Health Insurance Coverage (NHIC) policy to improve the accessibility and affordability of innovative anticancer medicines, leading to reduced prices and increased availability and utilization of 15 negotiated drugs. However, a study indicated that the availability of these innovative anticancer drugs remained limited. By 2019, the NHIC policy had benefited 44,600 people, while the number of new cancer cases in China in 2020 was 4.57 million [ 47 ]. The promotion of information on innovative therapies helped patients gain a better understanding of their cancer treatment options [ 48 ].

Practical Implications

This research highlighted that MSM did not fully leverage its professional background in providing comprehensive cancer information to the public. In fact, MSM holds substantial potential for contributing to cancer education. The findings from the content analysis also have practical implications for practitioners. They provide valuable insights for experts to assess the effectiveness of social media, monitor the types of information available to the public and patients with cancer, and guide communication and medical professionals in crafting educational and persuasive messages based on widely covered or less attended content.

Limitations and Future Directions

This study had some limitations. First, we only collected 60,843 articles from 9 WPAs in China. Future research could broaden the scope by collecting data from diverse countries and social media platforms. Second, our manual labeling only extracted 10% (450/4479 for MSM and 394/3948 for CSM) of the samples; the accuracy of the machine learning model could be enhanced by training it with a larger set of labeled articles. Finally, our results only represented the media’s presentation, and the impact of this information on individuals remains unclear. Further work could examine its influence on behavioral intentions or actions related to cancer prevention among the audience.

Conclusions

The analysis of cancer-related information on social media revealed an imbalance between prevention and treatment content. Overall, there was more treatment information than prevention information. Compared with MSM, CSM mentioned more prevention information. On MSM, the proportion of treatment information was greater than prevention information, whereas on CSM, the 2 were equal. The focus on cancer prevention and treatment information was primarily limited to a few aspects, with a predominant emphasis on secondary prevention rather than primary prevention. There is a need for further improvement in the coverage of prevention measures and treatments for cancer on social media. Additionally, the findings underscored the potential of applying machine learning to content analysis as a promising research paradigm for mapping key dimensions of cancer information on social media. These findings offer methodological and practical significance for future studies and health promotion.

Acknowledgments

This study was funded by The Major Program of the Chinese National Foundation of Social Sciences under the project “The Challenge and Governance of Smart Media on News Authenticity” (grant number 23&ZD213).

Conflicts of Interest

None declared.

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Abbreviations

Bidirectional Encoder Representations from Transformers

common social media

Global Vectors for Word Representation

latent Dirichlet allocation

long short-term memory

medical social media

National Health Insurance Coverage

World Health Organization

WeChat public account

Edited by S Ma; submitted 02.01.24; peer-reviewed by F Yang, D Wawrzuta; comments to author 20.03.24; revised version received 19.04.24; accepted 03.06.24; published 14.08.24.

©Keyang Zhao, Xiaojing Li, Jingyang Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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1. Identify Your Data Quarry: Understand precisely which data will best serve your research, whether it be social media trends, market statistics or educational resources.

2. Choose Your Tools Wisely: Select web scraping tools that match your technical proficiency and research requirements, ranging from user-friendly platforms to more complex, customizable software.

3. Ethical Gathering: Always scrape data with integrity , respecting data privacy laws and website terms of service. Ethical research is credible research.

4. Cleanse And Analyze: Simply having raw data is not enough. Apply data-cleaning techniques to ensure accuracy, then use robust analytical methods to extract insights.

5. Stay Informed And Agile: The digital landscape is in constant flux. Keep abreast of legal and technological developments in web scraping to maintain the validity and relevance of your work.

Predicting A New Dawn In Research

The potential of web scraping in research is vast, poised to be a catalyst for innovation and enabling swift, adaptive studies. Its predictive power could redefine entire disciplines, from market research to the social sciences. Web scraping extends beyond mere data collection; it enables comprehensive market analyses and real-time monitoring of public opinion shifts and enhances academic studies with broader data sets for analysis and validation.

The new digital landscape brings with it responsibilities, particularly regarding data privacy and ethical information sharing. Researchers must adeptly navigate these challenges, balancing ease of data access with considerations of consent and ownership. The reliability of sources, data veracity and methodological integrity remain crucial in maintaining research credibility.

As we advance through the digital revolution, the landscape of research is not just growing—it's flourishing. By embracing digital tools and the myriad opportunities they offer, researchers are making pivotal contributions to their fields, aiding in the creation of a more informed and interconnected world. This transformation of research, brought on by the internet age, is not just a change; it represents a paradigm shift, heralding a new era in our journey of exploration and understanding.

While opinions may vary, one fact stands out: Modern technologies have cultivated an environment where hypotheses can be rapidly tested, reviewed and refined. Today, the journey from "what if?" to "eureka!" is shorter than ever before.

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Effective ways to communicate research in a journal article

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By Megan Taphouse , Anne Foster , Eduardo Franco , Howard Browman , and Michael Schnoor
August 12 th 2024

In this blog post, editors of OUP journals delve into the vital aspect of clear communication in a journal article. Anne Foster (Editor of Diplomatic History ), Eduardo Franco (Editor-in-Chief of JNCI: Journal of the National Cancer Institute and JNCI Monographs ), Howard Browman (Editor-in-Chief of ICES Journal of Marine Science ), and Michael Schnoor (Editor-in-Chief of Journal of Leukocyte Biology ) provide editorial recommendations on achieving clarity, avoiding common mistakes, and creating an effective structure.

Ensuring clear communication of research findings

AF : To ensure research findings are clearly communicated, you should be able to state the significance of those findings in one sentence—if you don’t have that simple, clear claim in your mind, you will not be able to communicate it.

MS : The most important thing is clear and concise language. It is also critical to have a logical flow of your story with clear transitions from one research question to the next.

EF : It is crucial to write with both experts and interested non-specialists in mind, valuing their diverse perspectives and insights.

Common mistakes that obscure authors’ arguments and data

AF : Many authors do a lovely job of contextualizing their work, acknowledging what other scholars have written about the topic, but then do not sufficiently distinguish what their work is adding to the conversation.

HB : Be succinct—eliminate repetition and superfluous material. Do not attempt to write a mini review. Do not overinterpret your results or extrapolate far beyond the limits of the study. Do not report the same data in the text, tables, and figures.

The importance of the introduction

AF : The introduction is absolutely critical. It needs to bring them straight into your argument and contribution, as quickly as possible.

EF : The introduction is where you make a promise to the reader. It is like you saying, “I identified this problem and will solve it.” What comes next in the paper is how you kept that promise.

Structural pitfalls

EF : Remember, editors are your first audience; make sure your writing is clear and compelling because if the editor cannot understand your writing, chances are that s/he will reject your paper without sending it out for external peer review.

HB : Authors often misplace content across sections, placing material in the introduction that belongs in methods, results, or discussion, and interpretive phrases in results instead of discussion. Additionally, they redundantly present information in multiple sections.

Creating an effective structure

AF : I have one tip which is more of a thinking and planning strategy. I write myself letters about what I think the argument is, what kinds of support it needs, how I will use the specific material I have to provide that support, how it fits together, etc.

EF : Effective writing comes from effective reading—try to appreciate good writing in the work of others as you read their papers. Do you like their writing? Do you like their strategy of advancing arguments? Are you suspicious of their methods, findings, or how they interpret them? Do you see yourself resisting? Examine your reactions. You should also write frequently. Effective writing is like a physical sport; you develop ‘muscle memory’ by hitting a golf ball or scoring a 3-pointer in basketball.

The importance of visualizing data and findings

MS : It is extremely important to present your data in clean and well-organized figures—they act as your business card. Also, understand and consider the page layout and page or column dimensions of your target journal and format your tables and figures accordingly.

EF : Be careful when cropping gels to assemble them in a figure. Make sure that image contrasts are preserved from the original blots. Image cleaning for the sake of readability can alter the meaning of results and eventually be flagged by readers as suspicious.

The power of editing

AF : Most of the time, our first draft is for ourselves. We write what we have been thinking about most, which means the article reflects our questions, our knowledge, and our interests. A round or two of editing and refining before submission to the journal is valuable.

HB : Editing does yourself a favour by minimizing distractions-annoyances-cosmetic points that a reviewer can criticize. Why give reviewers things to criticize when you can eliminate them by submitting a carefully prepared manuscript?

Editing mistakes to avoid

AF : Do not submit an article which is already at or above the word limit for articles in the journal. The review process rarely asks for cuts; usually, you will be asked to clarify or add material. If you are at the maximum word count in the initial submission, you then must cut something during the revision process.

EF : Wait 2-3 days and then reread your draft. You will be surprised to see how many passages in your great paper are too complicated and inscrutable even for you. And you wrote it!

Featured image by Charlotte May via Pexels .

Megan Taphouse , Marketing Executive

Anne Foster , (Editor of Diplomatic History)

Eduardo Franco , (Editor-in-Chief of JNCI: Journal of the National Cancer institute and JNCI Monographs)

Howard Browman , (Editor-in-Chief of ICES Journal of Marine Science)

Michael Schnoor , (Editor-in-Chief of Journal of Leukocyte Biology)

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The Influence of Internet Usage on Student’s Academic Performance

September 2017
International Journal of Academic Research in Business and Social Sciences 7(8)

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Effective ways to communicate research in a journal article
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The Influence of Internet Usage on Student's Academic Performance
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