Profiling Bot Accounts Mentioning COVID-19 Publications on Twitter

YE Ye, JC Na - Digital Libraries at Times of Massive Societal Transition …, 2020 - Springer
Digital Libraries at Times of Massive Societal Transition: 22nd International …, 2020Springer
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 …
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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