All Warnings Are Not Equal: A User-Centered Approach to Comparing General and Specific Contextual Warnings against Misinformation

Abstract

Misinformation warnings have become the de facto solution for fighting untruthful messages online. Our study brings forth new understandings as to how users cognitively process two types of warning: general and specific contextual warnings; both are adopted by TikTok and Instagram Reels. Actual TikTok and Instagram users were recruited in this study. In general, we confirm that misinformation warnings indeed aid participants in making sound judgments on the video's veracity. However, general contextual warnings are rated the least effective by participants due to (1) the lack of ability to attract and maintain attention, and (2) the inability to provide relevant information to verify specific video content. Secondly, warnings with “False Information” labels are more effective in helping users to make high-quality accuracy judgments than warnings with “Missing Context” or “Partially False Information” labels, indicating that people prefer affirmative misinformation warnings. Finally, our findings contribute to the evolving scholarship on misinformation warning compliance by casting light on nuanced participant perceptions and behaviors that, if not carefully addressed, may hinder the efforts of misinformation mitigation on social media.