ArticleStudies in health technology and informatics2022
Unpacking Misinfodemic During a Global Health Crisis: A Qualitative Inquiry of Psychosocial Characteristics in Social Media Interactions.
Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Learning to see clearly again: How critical media engagement can save public health.Frontiers in public health · 2026Article
- Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework.Journal of biomedical informatics · 2023Article
- Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science.JMIR infodemiology · 2023Article
- Latent Linguistic Motifs in Social Media Postings Resisting COVID-19 Misinformation.Studies in health technology and informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The pervasiveness of health information in social media has led to a modern misinformation crisis, also known as a misinfodemic. Misinfodemics have upended public health activities as clearly evident during the COVID-19 pandemic. The objective of this study is to characterize social media content and information sources using theory-driven health behavior and psychology constructs to better understand the motifs of misinformation and their role in the dissemination of health (mis)information in Twitter posts. We analyzed 1,400 randomly selected tweets related to COVID-19 to ascertain four important variables, what is the tweet about (content), how is it structured (linguistic features), who is tweeting (source), and what is the reach of the tweet (dissemination). Results showed there was a significant difference between themes expressed, health beliefs manifested, and observed linguistic patterns in true and false information. Implications for informatics-driven digital health utilities, such as theory-informed knowledge models and context-aware risk communications, are discussed.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.