From neuroscience to computer science: a topical approach on Twitter

C. A. Piña-García (), J. Mario Siqueiros-García, E. Robles-Belmont, Gustavo Carreón, Carlos Gershenson and Julio Amador Díaz López
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C. A. Piña-García: Universidad Veracruzana
J. Mario Siqueiros-García: Universidad Nacional Autónoma de México
E. Robles-Belmont: Universidad Nacional Autónoma de México
Gustavo Carreón: Universidad Nacional Autónoma de México
Carlos Gershenson: Universidad Nacional Autónoma de México
Julio Amador Díaz López: Imperial College London

Journal of Computational Social Science, 2018, vol. 1, issue 1, No 12, 187-208

Abstract: Abstract Twitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.

Keywords: Computational social science; Social media; Twitter; Social networking; Complex systems (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s42001-017-0002-9

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