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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Feb 26, 2025
Open Peer Review Period: Feb 27, 2025 - Apr 24, 2025
Date Accepted: Aug 6, 2025
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Quality of Cancer-Related Information on New Media (2014-2023): Systematic Review and Meta-Analysis

Liu XJ, Valdez D, Parker MA, Walsh-Buhi E

Quality of Cancer-Related Information on New Media (2014-2023): Systematic Review and Meta-Analysis

J Med Internet Res 2025;27:e73185

DOI: 10.2196/73185

PMID: 41061257

PMCID: 12547337

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Quality and Misinformation of Cancer-Related Information on Social Media: A Systematic Review of Literature (2014–2023)

  • Xue-Jing Liu; 
  • Danny Valdez; 
  • Maria A Parker; 
  • Eric Walsh-Buhi

ABSTRACT

Background:

Social media has become a vital source of cancer-related health information, offering patients, caregivers, and the public a platform for sharing knowledge and experiences. However, concerns regarding the quality, accuracy, and potential misinformation of cancer information on social media persist.

Objective:

This study systematically reviewed literature published between 2014 and 2023 evaluating the quality of cancer-related information on social media. It aimed to identify common characteristics of these studies, assess patterns in information quality across platforms and cancer types, and explore factors associated with study outcomes.

Methods:

This systematic review searched PubMed, Web of Science, Scopus, and Medline. Studies were included if they analyzed cancer-related social media content and assessed information quality using standardized tools (e.g., the DISCERN tool). Extracted data included study characteristics, social media platform, cancer types, and quality assessment methods. Meta-analysis and ordinal logistic regression analysis were performed to pool findings from multiple studies.

Results:

A total of 75 studies were included, covering various a range of social media platforms, such as YouTube, TikTok, Facebook, Twitter, and Reddit. Findings indicated that video-based platforms, particularly YouTube and TikTok, were the most studied but also contained misinformation. Overall, 27% of social media cancer-related content included misinformation, with common false claims regarding alternative treatments and unproven therapies. Studies assessing rare cancers reported lower information quality compared to those focusing on common cancers. Additionally, content from medical professionals was of higher quality but less engaging than user-generated content.

Conclusions:

While social media serves as an essential platform for cancer-related health information, concerns remain about misinformation, completeness, and actionability. Future research should prioritize improving information accuracy, leveraging AI for content verification, and promoting authoritative sources to enhance public health outcomes.


 Citation

Please cite as:

Liu XJ, Valdez D, Parker MA, Walsh-Buhi E

Quality of Cancer-Related Information on New Media (2014-2023): Systematic Review and Meta-Analysis

J Med Internet Res 2025;27:e73185

DOI: 10.2196/73185

PMID: 41061257

PMCID: 12547337

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