Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Health misinformation on social media has become a major threat to users. To alleviate this issue, platforms such as Twitter have started labeling posts considered as misinformation to warn users. However, the effectiveness of such labels on user perceptions and actions are not clear, as it has not yet been examined by researchers in prior studies. We aim to address this gap through a model, which draws upon concepts from color theory and construal level theory and focuses on the impact of three misinformation label characteristics: background color of the label, abstractness of the message, and assertiveness of the message language. We propose that the effectiveness of these warning labels will lead users to verify, avoid using, and avoid sharing such labeled posts on social media. This paper provides important theoretical contributions and aids policymakers and platform providers by offering insights on what motivates users to take protective actions.
Recommended Citation
Varzgani, Huma; Kordzadeh, Nima; and Lee, Kyumin, "Toward Designing Effective Warning Labels for Health Misinformation on Social Media" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 3.
https://aisel.aisnet.org/hicss-56/cl/social_media/3
Toward Designing Effective Warning Labels for Health Misinformation on Social Media
Online
Health misinformation on social media has become a major threat to users. To alleviate this issue, platforms such as Twitter have started labeling posts considered as misinformation to warn users. However, the effectiveness of such labels on user perceptions and actions are not clear, as it has not yet been examined by researchers in prior studies. We aim to address this gap through a model, which draws upon concepts from color theory and construal level theory and focuses on the impact of three misinformation label characteristics: background color of the label, abstractness of the message, and assertiveness of the message language. We propose that the effectiveness of these warning labels will lead users to verify, avoid using, and avoid sharing such labeled posts on social media. This paper provides important theoretical contributions and aids policymakers and platform providers by offering insights on what motivates users to take protective actions.
https://aisel.aisnet.org/hicss-56/cl/social_media/3