Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation

Yunyao Li, Tyrone Grandison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, Haiqi Zhang


Abstract
Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer–a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.
Anthology ID:
2020.nlpcovid19-acl.9
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Month:
July
Year:
2020
Address:
Online
Editors:
Karin Verspoor, Kevin Bretonnel Cohen, Mark Dredze, Emilio Ferrara, Jonathan May, Robert Munro, Cecile Paris, Byron Wallace
Venue:
NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
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URL:
https://aclanthology.org/2020.nlpcovid19-acl.9
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Cite (ACL):
Yunyao Li, Tyrone Grandison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, and Haiqi Zhang. 2020. Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
Cite (Informal):
Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation (Li et al., NLP-COVID19 2020)
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PDF:
https://aclanthology.org/2020.nlpcovid19-acl.9.pdf