Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter
Filipo Sharevski, Amy Devine, Emma Pieroni, Peter Jacnim

TL;DR
This study explores improved misinformation warning designs on Twitter, finding that context-enhanced warnings are generally preferred and more effective in helping users recognize misinformation, with preferences influenced by political orientation and education.
Contribution
The paper introduces and evaluates context-enhanced misinformation warnings with iconography, demonstrating their effectiveness and user preference over standard tags in a Twitter usability study.
Findings
Enhanced warnings are preferred by most users.
Left-leaning and moderate users favor the enhancements.
Education level influences warning preferences.
Abstract
Warning users about misinformation on social media is not a simple usability task. Soft moderation has to balance between debunking falsehoods and avoiding moderation bias while preserving the social media consumption flow. Platforms thus employ minimally distinguishable warning tags with generic text under a suspected misinformation content. This approach resulted in an unfavorable outcome where the warnings "backfired" and users believed the misinformation more, not less. In response, we developed enhancements to the misinformation warnings where users are advised on the context of the information hazard and exposed to standard warning iconography. We ran an A/B evaluation with the Twitter's original warning tags in a 337 participant usability study. The majority of the participants preferred the enhancements as a nudge toward recognizing and avoiding misinformation. The enhanced…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Social Media and Politics · Hate Speech and Cyberbullying Detection
