- Machine learning and natural language processing for information identification 
- Linguistic and stylistic patterns 
- Knowledge bases for detection of misinformation 
- Ask professional fact-checkers to rate information and send corrections to users sharing misinformation 
- Misinformation nudges 

University of Michigan - Ann Arbor

- Factors that drive the sharing of misinformation
- Anti-misinformation interventions

Perverse Downstream Consequences of Debunking - Related Work

Mosleh, M., Martel, C., Eckles, D., & Rand, D. (2021, May). Perverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field Experiment. In proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-13). https://dl.acm.org/doi/10.1145/3411764.3445642 

Perverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field Experiment

- People share more posts that trigger emotions, and emotions also contribute to interactions following the shared content 
- Accuracy does not seem to impact sharing patterns
- Previous studies have found that posts with false fact-checker ratings were shared more than true claims, as well as no difference between accuracy and sharing 
- It’s not that people don’t care about accuracy; they simply don’t pay enough attention to determine whether the content they are sharing is true or not 

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