# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 7 Datasets
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Work** & **Type** & **URL** & **Annotation** & **Characteristics** \\ \hline BioClaim [6] & -Biomedical claims & [[https://www.simi-suttgart.de/documents/resources/korpora/bioclaim/WheetlrKlinger-Bioclaim2021.zip](https://www.simi-suttgart.de/documents/resources/korpora/bioclaim/WheetlrKlinger-Bioclaim2021.zip)]([https://www.simi-suttgart.de/documents/resources/korpora/bioclaim/WheetlrKlinger-Bioclaim2021.zip](https://www.simi-suttgart.de/documents/resources/korpora/bioclaim/WheetlrKlinger-Bioclaim2021.zip)) & Manual & A total of 1200 tweets of implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase), including COVID-19, measles, cystic fibrosis, and depression \\ \hline NewsClaims [44] & -COVID-19 claims -News & [[https://github.com/blender-nlp/NewsClaims](https://github.com/blender-nlp/NewsClaims)]([https://github.com/blender-nlp/NewsClaims](https://github.com/blender-nlp/NewsClaims)) & Manual & A total of 889 claims annotated over 143 pieces of news, including claim sentences, claim objects, claim stances, claim span, and claimet detection \\ \hline RedHOT [57] & -Health issue claims -Reddit & [[https://github.com/sominw/redhot](https://github.com/sominw/redhot)]([https://github.com/sominw/redhot](https://github.com/sominw/redhot)) & Manual (crowd-sourced and experts) & A total of 22,000 posts from 24 medical condition subreddits, annotated as claims, personal experiences, and questions \\ \hline PubHealth [55] & -Health issue claims -Claims from fact-checking sites and news & [[https://github.com/neemakot/Health-Fact-Checking](https://github.com/neemakot/Health-Fact-Checking)]([https://github.com/neemakot/Health-Fact-Checking](https://github.com/neemakot/Health-Fact-Checking)) & Manual & A total of 27,578 fact-checked claims, 9023 claims from Associated Press and Reuters News, and 2700 claims from Health News Review. Labels are true, false, mixture, and unproven \\ \hline HealthVer [56] & -Health issue claims -Search engines -Snippets & [[https://github.com/sarrouti/HealthVer](https://github.com/sarrouti/HealthVer)]([https://github.com/sarrouti/HealthVer](https://github.com/sarrouti/HealthVer)) & Manual (authors) & A total of 14,330 evidence claim pairs, labeled as support, refute, and neutral \\ \hline ANTi-Vax [198] & -COVID-19 vaccine misinformation -Tweets & [[https://github.com/[PERSON]/ANTiVax](https://github.com/[PERSON]/ANTiVax)]([https://github.com/[PERSON]/ANTiVax](https://github.com/SakkiShahriar95/ANTiVax)) & Manual (experts) & A total of 15 million COVID-19 vaccine-related tweets and 15 thousand labeled tweets for vaccine misinformation detection (binary classification) \\ \hline  & & & A total of 4573 annotated tweets, comprising 3629 users. Labels are: irrelevant, conspiracy, true treatment, true prevention, fake cure, fake treatment, false fact or prevention, correction/calling out, sarcasm/satire, true public health, response, false public health response, politics, ambiguous/difficult to classify, commercial activity or promotion, emergency response, news, panic buying \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Public datasets related to medical fact-checking, fake news detection, and social media (all websites accessed on 25 February 2025).