Countering Misinformation via Emotional Response Generation
Daniel Russo, Shane Kaszefski-Yaschuk, Jacopo Staiano, Marco Guerini
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.- Anthology ID:
- 2023.emnlp-main.703
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11476–11492
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.703
- DOI:
- 10.18653/v1/2023.emnlp-main.703
- Cite (ACL):
- Daniel Russo, Shane Kaszefski-Yaschuk, Jacopo Staiano, and Marco Guerini. 2023. Countering Misinformation via Emotional Response Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11476–11492, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Countering Misinformation via Emotional Response Generation (Russo et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.emnlp-main.703.pdf