Experimental groups who saw misinformation accompanied by a reduced post size, warning, peer's opinions, or explanation, did better at rating false headlines as false. The advantage was most pronounced in the group that received an explanation as to why to the post was flagged as false.

University of Delaware

Warning-based approaches were found to be most well-liked by users and also most effective at preventing the spread of misinformation in a study investigating different types of misinformation interventions on social media.

Warning-based Approaches

Kirchner, J. & Reuter, C. (2020). Countering Fake News: A Comparison of Possible Solutions Regarding User Acceptance and Effectiveness. The Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-27. https://doi.org/10.1145/3415211

Countering Fake News: A Comparison of Possible Solutions Regarding User Acceptance and Effectiveness

Kirchner and Reuter conducted a survey asking 1012 participants about their personal strategy for fake-news detection. Participants were recruited via "Respondi". They were also given questions from the General Decision-Making Style and Rationale-Experiential Inventory Scales. Survey data was compiled and some key results were as follows: 
- Over 50% of respondents reported being somewhat careful or more careful with a potential fake news source
- 72% said they read an article before liking or sharing
- 20% said they trust the author to verify the facts themselves
- 71% of people liked having warnings about fake-news stories, but wanted the reason for the red-flag explained. 
- Older participants tended to "agree" with more of the questions
- Those who valued analytical thinking were more likely to be wary of a marked post

This shows that only a minority of people are vulnerable to fake-news. However, social desirability bias could have impacted the results.   

How People React to Potential Fake-News

For the second part of Kirchner and Reuter's study, they conducted 15 qualitative interviews regarding participants' likes and dislikes about certain interventions on social media to prevent users from spreading misinformation. These methods were:
- Labeling the post with a warning that independent fact-checkers dispute the content
- Reducing the size of the picture and article in the news feed
- Including related articles disputing the article underneath it
- Covering/blurring the article (similar to explicit content strategies)
- Requiring confirmation before the user can interact with the fake-news article

Participants were also asked for suggestions for improving each method, additional ideas for interventions, and their opinion on the practicality of applying it. All of the participants liked the warning strategy, while other, less transparent methods were less popular. Additional ideas for interventions included showing how many friends thought the article was false and adding an explanation for the discrepancy right underneath the article. 

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