(Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic

In this work, we collect a moderate-sized representative corpus of tweets (over 200 000) pertaining to COVID-19 vaccination spanning for a period of seven months (September 2020–March 2021). Following a transfer learning approach, we utilize a pretrained transformer-based XLNet model to classify twe...

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Main Authors: Sharma, Shakshi, Sharma, Rajesh, Datta, Anwitaman
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170558
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spelling sg-ntu-dr.10356-1705582023-09-19T05:31:44Z (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic Sharma, Shakshi Sharma, Rajesh Datta, Anwitaman School of Computer Science and Engineering Engineering::Computer science and engineering COVID-19 Explainable AI In this work, we collect a moderate-sized representative corpus of tweets (over 200 000) pertaining to COVID-19 vaccination spanning for a period of seven months (September 2020–March 2021). Following a transfer learning approach, we utilize a pretrained transformer-based XLNet model to classify tweets as misleading or nonmisleading and manually validate the results with random subsets of samples. We leverage this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. This exploratory analysis enables us to design features such as sentiments, hashtags, nouns, and pronouns which can, in turn, be exploited for classifying tweets as (non-)misleading using various machine learning (ML) models in an explainable manner. Specifically, several ML models are employed for prediction, with up to 90% accuracy, with the importance of each feature is explained using SHAP Explainable AI (XAI) tool. While the thrust of this work is principally exploratory in nature to obtain insight on the online discourse on COVID-19 vaccination, we conclude the article by outlining how these insights provide the foundations for a more actionable approach to mitigate misinformation. We have made the curated data as well as the accompanying code available so that the research community at large can reproduce, compare against, or build upon this work. The work of Shakshi Sharma and Rajesh Sharma was supported in part by the European Commission (EU) H2020 Program under the SoBigData++ Project under Agreement 871042, in part by CHIST-ERA under Grant CHIST-ERA-19-XAI-010, and in part by Eesti Teadusagentuur (ETAg) under Grant SLTAT21096. 2023-09-19T05:31:43Z 2023-09-19T05:31:43Z 2022 Journal Article Sharma, S., Sharma, R. & Datta, A. (2022). (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic. IEEE Transactions On Computational Social Systems. https://dx.doi.org/10.1109/TCSS.2022.3225216 2329-924X https://hdl.handle.net/10356/170558 10.1109/TCSS.2022.3225216 2-s2.0-85144755761 en IEEE Transactions on Computational Social Systems © 2022 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
COVID-19
Explainable AI
spellingShingle Engineering::Computer science and engineering
COVID-19
Explainable AI
Sharma, Shakshi
Sharma, Rajesh
Datta, Anwitaman
(Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic
description In this work, we collect a moderate-sized representative corpus of tweets (over 200 000) pertaining to COVID-19 vaccination spanning for a period of seven months (September 2020–March 2021). Following a transfer learning approach, we utilize a pretrained transformer-based XLNet model to classify tweets as misleading or nonmisleading and manually validate the results with random subsets of samples. We leverage this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. This exploratory analysis enables us to design features such as sentiments, hashtags, nouns, and pronouns which can, in turn, be exploited for classifying tweets as (non-)misleading using various machine learning (ML) models in an explainable manner. Specifically, several ML models are employed for prediction, with up to 90% accuracy, with the importance of each feature is explained using SHAP Explainable AI (XAI) tool. While the thrust of this work is principally exploratory in nature to obtain insight on the online discourse on COVID-19 vaccination, we conclude the article by outlining how these insights provide the foundations for a more actionable approach to mitigate misinformation. We have made the curated data as well as the accompanying code available so that the research community at large can reproduce, compare against, or build upon this work.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Sharma, Shakshi
Sharma, Rajesh
Datta, Anwitaman
format Article
author Sharma, Shakshi
Sharma, Rajesh
Datta, Anwitaman
author_sort Sharma, Shakshi
title (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic
title_short (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic
title_full (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic
title_fullStr (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic
title_full_unstemmed (Mis)leading the COVID-19 vaccination discourse on Twitter: an exploratory study of infodemic around the pandemic
title_sort (mis)leading the covid-19 vaccination discourse on twitter: an exploratory study of infodemic around the pandemic
publishDate 2023
url https://hdl.handle.net/10356/170558
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