Myth detection¶
Computational and manual approaches to identify myths—widely-believed false narratives or claims—circulating on social media and online platforms. Myth detection typically involves: (1) identifying candidate myths through crowdsourced lists or expert input, (2) developing word/phrase heuristics or machine learning models to recognize myth-related content, and (3) measuring prevalence and spread patterns. Related to but distinct from rumor detection and fact-checking; often operates on established false narratives rather than emerging unverified claims.
Key papers¶
- A first look at COVID-19 information and misinformation sharing on Twitter — myth detection applied to COVID-19 tweets using phrase weighting and hand validation
Related topics¶
- rumor detection social media (related method for emerging claims)
- Fact-checking and corrections (verification-focused counterpart)
- Misinformation and fake news detection (broader category)