Classification Aware Neural Topic Model for COVID-19 Disinformation Categorisation

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2021 Feb 18

PMID 33600477

Citations 9

Authors

Xingyi Song

Johann Petrak

Ye Jiang

Iknoor Singh

Diana Maynard

Kalina Bontcheva

Affiliations

Soon will be listed here.

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.

Citing Articles

Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.

Sharifpoor E, Okhovati M, Ghazizadeh-Ahsaee M, Avaz Beigi M BMC Med Inform Decis Mak. 2025; 25(1):73.

PMID: 39934858 PMC: 11817542. DOI: 10.1186/s12911-025-02895-y.

A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets.

Jahin M, Shovon M, Mridha M, Islam M, Watanobe Y Sci Rep. 2024; 14(1):24882.

PMID: 39438715 PMC: 11496622. DOI: 10.1038/s41598-024-76079-5.

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19).

Su X, Wang S PLoS One. 2024; 19(4):e0299374.

PMID: 38573976 PMC: 10994342. DOI: 10.1371/journal.pone.0299374.

User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults.

Bondaronek P, Papakonstantinou T, Stefanidou C, Chadborn T Public Health Pract (Oxf). 2023; 6:100401.

PMID: 38099087 PMC: 10719408. DOI: 10.1016/j.puhip.2023.100401.

CovTiNet: Covid text identification network using attention-based positional embedding feature fusion.

Hossain M, Hoque M, Siddique N, Sarker I Neural Comput Appl. 2023; 35(18):13503-13527.

PMID: 37213320 PMC: 10011801. DOI: 10.1007/s00521-023-08442-y.

References

Park S, Han S, Kim J, Molaie M, Vu H, Singh K . COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication. J Med Internet Res. 2021; 23(3):e23272. PMC: 8108572. DOI: 10.2196/23272. View

Muller M, Salathe M, Kummervold P . COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter. Front Artif Intell. 2023; 6:1023281. PMC: 10043293. DOI: 10.3389/frai.2023.1023281. View

Feng Y, Zhou W . Work from home during the COVID-19 pandemic: An observational study based on a large geo-tagged COVID-19 Twitter dataset (UsaGeoCov19). Inf Process Manag. 2021; 59(2):102820. PMC: 8656435. DOI: 10.1016/j.ipm.2021.102820. View

Wicke P, Bolognesi M . Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter. PLoS One. 2020; 15(9):e0240010. PMC: 7526906. DOI: 10.1371/journal.pone.0240010. View

Medford R, Saleh S, Sumarsono A, Perl T, Lehmann C . An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak. Open Forum Infect Dis. 2020; 7(7):ofaa258. PMC: 7337776. DOI: 10.1093/ofid/ofaa258. View

Banda J, Tekumalla R, Wang G, Yu J, Liu T, Ding Y . A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration. Epidemiologia (Basel). 2022; 2(3):315-324. PMC: 9620940. DOI: 10.3390/epidemiologia2030024. View

Xue J, Chen J, Hu R, Chen C, Zheng C, Su Y . Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. J Med Internet Res. 2020; 22(11):e20550. PMC: 7690968. DOI: 10.2196/20550. View

Rao H, Vemprala N, Akello P, Valecha R . Retweets of officials' alarming vs reassuring messages during the COVID-19 pandemic: Implications for crisis management. Int J Inf Manage. 2020; 55:102187. PMC: 7309924. DOI: 10.1016/j.ijinfomgt.2020.102187. View

Mehrpour O, Sadeghi M . Toll of acute methanol poisoning for preventing COVID-19. Arch Toxicol. 2020; 94(6):2259-2260. PMC: 7251552. DOI: 10.1007/s00204-020-02795-2. View

10.

Wang X, Zou C, Xie Z, Li D . Public Opinions towards COVID-19 in California and New York on Twitter. medRxiv. 2020; . PMC: 7373144. DOI: 10.1101/2020.07.12.20151936. View

11.

Cinelli M, Quattrociocchi W, Galeazzi A, Valensise C, Brugnoli E, Schmidt A . The COVID-19 social media infodemic. Sci Rep. 2020; 10(1):16598. PMC: 7538912. DOI: 10.1038/s41598-020-73510-5. View

12.

Kouzy R, Abi Jaoude J, Kraitem A, Alam M, Karam B, Adib E . Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus. 2020; 12(3):e7255. PMC: 7152572. DOI: 10.7759/cureus.7255. View

13.

Braunstein A, Jensen S, Wei Z, McAuliffe J . A spatially varying two-sample recombinant coalescent, with applications to HIV escape response. Adv Neural Inf Process Syst. 2010; 21:662. PMC: 2881484. View

14.

Chen E, Lerman K, Ferrara E . Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health Surveill. 2020; 6(2):e19273. PMC: 7265654. DOI: 10.2196/19273. View