Countering Misinformation via Emotional Response Generation
Daniel Russo Shane Peter Kaszefski-Yaschuk Jacopo Staiano Marco Guerini
The proliferation of misinformation on social media platforms (SMPs) poses a
significant danger to public health, social cohesion and ultimately democracy.
Previous research has shown how social correction can be an effective way to
curb misinformation, by engaging directly in a constructive dialogue with users
who spread -- often in good faith -- misleading messages. Although professional
fact-checkers are crucial to debunking viral claims, they usually do not engage
in conversations on social media. Thereby, significant effort has been made to
automate the use of fact-checker material in social correction; however, no
previous work has tried to integrate it with the style and pragmatics that are
commonly employed in social media communication. To fill this gap, we present
VerMouth, the first large-scale dataset comprising roughly 12 thousand
claim-response pairs (linked to debunking articles), accounting for both
SMP-style and basic emotions, two factors which have a significant role in
misinformation credibility and spreading. To collect this dataset we used a
technique based on an author-reviewer pipeline, which efficiently combines LLMs
and human annotators to obtain high-quality data. We also provide comprehensive
experiments showing how models trained on our proposed dataset have significant
improvements in terms of output quality and generalization capabilities.