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2020 | OriginalPaper | Buchkapitel

Profiling Bot Accounts Mentioning COVID-19 Publications on Twitter

verfasst von : Yingxin Estella Ye, Jin-Cheon Na

Erschienen in: Digital Libraries at Times of Massive Societal Transition

Verlag: Springer International Publishing

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Abstract

This paper presents preliminary findings regarding automated bots mentioning scientific papers about COVID-19 publications on Twitter. A quantitative approach was adopted to characterize social and posting patterns of bots, in contrast to other users, in Twitter scholarly communication. Our findings indicate that bots play a prominent role in research dissemination and discussion on the social web. We observed 0.45% explicit bots in our sample, producing 2.9% of tweets. The results implicate that bots tweeted differently from non-bot accounts in terms of the volume and frequency of tweeting, the way handling the content of tweets, as well as preferences in article selection. In the meanwhile, their behavioral patterns may not be the same as Twitter bots in another context. This study contributes to the literature by enriching the understanding of automated accounts in the process of scholarly communication and demonstrating the potentials of bot-related studies in altmetrics research.

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Vorheriges Kapitel Identifying the Types of Digital Footprint Data Used to Predict Psychographic and Human Behaviour

Nächstes Kapitel Uncovering Topics Related to COVID-19 Pandemic on Twitter

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Titel: Profiling Bot Accounts Mentioning COVID-19 Publications on Twitter
verfasst von: Yingxin Estella Ye
Jin-Cheon Na
Verlag: Springer International Publishing
Buch: Digital Libraries at Times of Massive Societal Transition
Print ISBN: 978-3-030-64451-2

Electronic ISBN: 978-3-030-64452-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-64452-9_27

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