Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach

Article


Nanath, K. and Joy, G. 2023. Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach. Behaviour and Information Technology. 42 (2), pp. 196-214. https://doi.org/10.1080/0144929X.2021.1941259
TypeArticle
TitleLeveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach
AuthorsNanath, K. and Joy, G.
Abstract

As the novel coronavirus spreads across the world, work, pleasure, entertainment, social interactions, and meetings have shifted online. The conversations on social media have spiked, and given the uncertainties and new policies, COVID-19 remains the trending topic on all such platforms, including Twitter. This research explores the factors that affect COVID-19 content-sharing by Twitter users. The analysis was conducted using 57,000 plus tweets that mentioned COVID-19 and related keywords. The tweets were subjected to the Natural Language Processing (NLP) techniques like Topic modelling, Named Entity-Relationship, Emotion & Sentiment analysis, and Linguistic feature extraction. These methods generated features that could help explain the retweet count of the tweets. The results indicate that tweets with named entities (person, organisation, and location), expression of negative emotions (anger, disgust, fear, and sadness), reference to mental health, optimistic content, and greater length have higher chances of being shared (retweeted). On the other hand, tweets with more hashtags and user mentions are less likely to be shared.

KeywordsCovid-19; retweet; content sharing; mental health; emotion-focused content; social media
PublisherTaylor and Francis
JournalBehaviour and Information Technology
ISSN0144-929X
Electronic1362-3001
Publication dates
Online17 Jun 2021
Print25 Jan 2023
Publication process dates
Deposited28 Jun 2021
Accepted04 Jun 2021
Submitted10 Oct 2020
Output statusPublished
Accepted author manuscript
Copyright Statement

This is an Accepted Manuscript of an article published by Taylor & Francis in Behaviour and Information Technology on 17 June 2021, available online: http://www.tandfonline.com/10.1080/0144929x.2021.1941259

Digital Object Identifier (DOI)https://doi.org/10.1080/0144929X.2021.1941259
Web of Science identifierWOS:000662754200001
LanguageEnglish
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