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Aim <p>We conducted a systematic literature review to synthesize the fragmented body of research on health misinformation spread via social media. The study aimed to explore how health misinformation is defined, what typologies exist, and how various dimensions such as data formats, methodologies, and health topics are addressed across disciplines.</p> Subject and methods <p>We conducted a systematic review to map the current state of knowledge and identify key gaps for future research. Focusing on studies published between 2010 and 2022, we analyzed peer-reviewed articles sourced from PubMed and Web of Science.</p> Results <p>We found that while “misinformation” was the most frequently used term, clear definitions were often lacking. The majority of studies relied on text data, primarily from platforms such as Twitter and Facebook, with relatively little attention given to images and videos. Quantitative methods, particularly computational techniques like machine learning and natural language processing, were widely used, reflecting the data-driven nature of the field. Most studies were exploratory in design, with COVID-19 the most common, followed by research on vaccines and cancer. Overall, two dominant research themes surfaced: the diffusion of misinformation and strategies for its detection and mitigation.</p> Conclusion <p>This synthesis not only outlines the limitations of existing research but also presents a structured research agenda to guide future studies aimed at understanding and mitigating the spread of health misinformation in digital environments. Researchers, public health practitioners, and policymakers can gain valuable insights from this study to better understand, detect, and combat health misinformation on social media platforms.</p>

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Unveiling the information mirage: a systematic literature review of health misinformation on social media

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Amir Karami,
  • Ali Zain,
  • Ahmad Jamal

摘要

Aim

We conducted a systematic literature review to synthesize the fragmented body of research on health misinformation spread via social media. The study aimed to explore how health misinformation is defined, what typologies exist, and how various dimensions such as data formats, methodologies, and health topics are addressed across disciplines.

Subject and methods

We conducted a systematic review to map the current state of knowledge and identify key gaps for future research. Focusing on studies published between 2010 and 2022, we analyzed peer-reviewed articles sourced from PubMed and Web of Science.

Results

We found that while “misinformation” was the most frequently used term, clear definitions were often lacking. The majority of studies relied on text data, primarily from platforms such as Twitter and Facebook, with relatively little attention given to images and videos. Quantitative methods, particularly computational techniques like machine learning and natural language processing, were widely used, reflecting the data-driven nature of the field. Most studies were exploratory in design, with COVID-19 the most common, followed by research on vaccines and cancer. Overall, two dominant research themes surfaced: the diffusion of misinformation and strategies for its detection and mitigation.

Conclusion

This synthesis not only outlines the limitations of existing research but also presents a structured research agenda to guide future studies aimed at understanding and mitigating the spread of health misinformation in digital environments. Researchers, public health practitioners, and policymakers can gain valuable insights from this study to better understand, detect, and combat health misinformation on social media platforms.