Coronabot: A conversational ai system for tackling misinformation

Gunson, Nancie; Sieińska, Weronika; Yu, Yanchao; Hernandez Garcia, Daniel; Part, Jose L; Dondrup, Christian; Lemon, Oliver

Abstract

Covid-19 has brought with it an onslaught of information for the public, some true and some false, across virtually every platform. For an individual, the task of sifting through the deluge for reliable, accurate facts is significant and potentially off-putting. This matters since fundamentally, containment of the pandemic relies on individuals' compliance with public health measures and their understanding of the need for them, and any barrier to this, including misinformation, can have profoundly negative effects. In this paper we present a conversational AI system which tackles misinformation using a two-pronged approach: firstly, by giving users easy, Natural Language access via speech or text to concise, reliable information synthesised from multiple authoritative sources; and secondly, by directly rebutting commonly circulated myths surrounding coronavirus. The initial system is targeted at staff and students of a University, but has the potential for wide applicability. In tests of the system's Natural Language Understanding (NLU) we achieve an F1-score of 0.906. We also discuss current research challenges in the area of conversational Natural Language interfaces for health information.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: Conference on Information Technology for Social Good
• Start Date: Sep 9, 2021
• End Date: Sep 11, 2021
• Online Publication Date: Sep 9, 2021
• Publication Date: 2021-09
• Deposit Date: Jun 27, 2023
• Publisher: Association for Computing Machinery (ACM)
• Pages: 265-270
• Book Title: GoodIT '21: Proceedings of the Conference on Information Technology for Social Good
• ISBN: 9781450384780
• DOI: https://doi.org/10.1145/3462203.3475874