UPDF AI

Intents and Motivations to Like, Comment and Share Posts With Warnings of Misinformation

Gionnieve Lim,S. Perrault

2023 · DOI: 10.1145/3638380.3638390
Australasian Computer-Human Interaction Conference · 1 Citations

TLDR

It is found that warning labels generally reduce engagement with posts, even when different reasons for the warnings are given, suggesting their viability and encouraging studies to consider more post engagement measures when evaluating the effectiveness of misinformation interventions.

Abstract

Social media platforms engage fact-checkers to label inaccurate posts and use detection algorithms to remove them. A desired secondary effect of the labels is to dampen post engagement. While studies have largely looked into how labels affect sharing intents, there is less work on other forms of post engagement and on labels provided by automated fact-checkers. As such, we investigate these aspects in a within-subjects experimental study with 36 participants set in the US political context. We find that warning labels generally reduce engagement with posts, even when different reasons for the warnings are given, suggesting their viability. Observing low correlations between the intents to like, comment and share posts and underlying differences in the reasons that motivate these engagements, we further encourage studies to consider more post engagement measures when evaluating the effectiveness of misinformation interventions.