Real-world user response to social correction: corrective vs. backfire effects

Determine whether ordinary users’ replies to counter-misinformation messages (i.e., social correction consisting of direct counter-misinformation replies to misinformation posts on Twitter) elicit corrective effects, backfire effects, or other outcomes in real-world scenarios, by characterizing and quantifying user responses to these social correction messages.

Background

The paper focuses on "social correction," defined as ordinary users directly replying to misinformation posts with counter-misinformation messages in conversational threads on Twitter. While prior work examined the efficacy of corrections in experiments or with professional fact-checkers, the authors highlight a lack of clarity about how real users respond to these peer corrections in situ.

This uncertainty motivates the paper’s contributions: assembling a large dataset of misinformation posts, counter-replies, and subsequent user responses; constructing a taxonomy of responses; analyzing linguistic, engagement, and poster attributes associated with corrective versus backfire outcomes; and developing a prediction model to classify likely effects. The stated uncertainty frames the need for these analyses and tools.