Major
Business Administration
Anticipated Graduation Year
2025
Access Type
Open Access
Abstract
Misinformation on social media influences public opinion, shaping beliefs, behaviors, and policy decisions. Platforms have implemented varying misinformation warning messages to curb “fake news,” but their impact on user trust and engagement remains unclear. Our research examines how different misinformation content moderation approaches influence users' perceptions and biometric responses related to attention and affective states. We propose a within-subject experimental study with three conditions: platform-driven moderation, community-driven moderation, and no moderation. By comparing these, we assess user reactions to misinformation warnings. Using eye-tracking, galvanic skin response (GSR), and facial expression analysis, we explore how these responses relate to users’ trust in moderation and willingness to engage (like, comment, share) with flagged content. This research contributes to a deeper understanding of how different moderation approaches shape user trust and engagement, informing the development of evidence-based strategies for balancing misinformation control and freedom of expression.
Faculty Mentors & Instructors
Dinko Bačić
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Navigating Trust and Engagement: The Impact of Misinformation Moderation on User Perception and Biometric Response
Misinformation on social media influences public opinion, shaping beliefs, behaviors, and policy decisions. Platforms have implemented varying misinformation warning messages to curb “fake news,” but their impact on user trust and engagement remains unclear. Our research examines how different misinformation content moderation approaches influence users' perceptions and biometric responses related to attention and affective states. We propose a within-subject experimental study with three conditions: platform-driven moderation, community-driven moderation, and no moderation. By comparing these, we assess user reactions to misinformation warnings. Using eye-tracking, galvanic skin response (GSR), and facial expression analysis, we explore how these responses relate to users’ trust in moderation and willingness to engage (like, comment, share) with flagged content. This research contributes to a deeper understanding of how different moderation approaches shape user trust and engagement, informing the development of evidence-based strategies for balancing misinformation control and freedom of expression.