Classification aware neural topic model for COVID-19 disinformation categorisation

Xingyi Song; Johann Petrak; Ye Jiang; Iknoor Singh; Diana Maynard; Kalina Bontcheva

doi:10.1371/journal.pone.0247086

Classification aware neural topic model for COVID-19 disinformation categorisation

PLoS One. 2021 Feb 18;16(2):e0247086. doi: 10.1371/journal.pone.0247086. eCollection 2021.

Authors

Xingyi Song¹, Johann Petrak^{1

2}, Ye Jiang¹, Iknoor Singh^{1

3}, Diana Maynard¹, Kalina Bontcheva¹

Affiliations

¹ Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.
² Austrian Research Institute for Artificial Intelligence, Vienna, Austria.
³ Panjab University, Chandigarh, India.

Abstract

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Classification / methods*
Communication*
Data Curation
Neural Networks, Computer*

Grants and funding

This research has been supported by European Union under grant agreement No.825297 WeVerify (https://weverify.eu/) and No. 825091 RISIS (https://www.risis2.eu/). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.