University of Illinois Urbana-Champaign

Detecting public discourse about COVID-19

Xu, Huimin; Erk, Katrin

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282-abstract.docx
282-poster.pdf

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https://hdl.handle.net/2142/117364

Description

Title
Detecting public discourse about COVID-19
Author(s)
  • Xu, Huimin
  • Erk, Katrin
Issue Date
2023-03-13
Keyword(s)
  • COVID-19
  • NLI task
  • Public discourse
  • Lockdown policy and RoBERTa model
Abstract
The discussions about COVID-19 vary from government policy, treatment effec-tiveness, to origin of the virus. These controversial topics have increased the need for understanding the public’s opinions about COVID-19. Detecting the mutual relationship between opinions (entailment, contradiction, neutral) has important implications for governments to ease anxiety and take action. In this project, we fine-tune RoBERTa models based on 10,320 relationships between 637 COVID-19 related claims, reaching 82% accuracy. By comparing the difference between model prediction and human annotation, we found that models have huge room to improve in knowledge similarity and implicit negation. We also apply the trained models to another lockdown dataset on Twitter, in order to help us understand the adaptability and limitation of our COVID-19 models.
Publisher
iSchools
Series/Report Name or Number
iConference 2023 Proceedings
Type of Resource
  • Other
  • text
Genre of Resource
Conference Poster
Language
eng
Handle URL
https://hdl.handle.net/2142/117364
Copyright and License Information
Copyright 2023 is held by Xu, Huimin, Erk, Katrin. Copyright permissions, when appropriate, must be obtained directly from the author.

Owning Collections

iConference 2023 Posters PRIMARY