Countering Misinformation via Emotional Response Generation

• URL: http://arxiv.org/abs/2311.10587v1
• Date: Fri, 17 Nov 2023 15:37:18 GMT
• Title: Countering Misinformation via Emotional Response Generation
• Authors: Daniel Russo, Shane Peter Kaszefski-Yaschuk, Jacopo Staiano, Marco Guerini
• Abstract summary: proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and democracy. Previous research has shown how social correction can be an effective way to curb misinformation. We present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs.
• Score: 15.383062216223971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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