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

Nanath, Krishnadas ORCID: https://orcid.org/0000-0002-3515-9084 and Joy, Geethu (2021) Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach. Behaviour and Information Technology . pp. 1-19. ISSN 0144-929X [Article] (Published online first) (doi:10.1080/0144929x.2021.1941259)

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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.

Item Type: Article
Keywords (uncontrolled): Human-Computer Interaction, Arts and Humanities (miscellaneous), General Social Sciences, Developmental and Educational Psychology
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 33456
Notes on copyright: 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
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Depositing User: Jisc Publications Router
Date Deposited: 28 Jun 2021 14:43
Last Modified: 06 Jul 2021 15:29
URI: https://eprints.mdx.ac.uk/id/eprint/33456

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