Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162615
Title: Are automated accounts driving scholarly communication on Twitter? a case study of dissemination of COVID-19 publications
Authors: Ye, Estella Yingxin
Na, Jin-Cheon
Oh, Poong
Keywords: Social sciences::Communication
Issue Date: 2022
Source: Ye, E. Y., Na, J. & Oh, P. (2022). Are automated accounts driving scholarly communication on Twitter? a case study of dissemination of COVID-19 publications. Scientometrics, 127(5), 2151-2172. https://dx.doi.org/10.1007/s11192-022-04343-4
Journal: Scientometrics
Abstract: From a network perspective, this study analyzes 659 users mentioning sampled COVID-19 articles 10 or more times on Twitter with a focus on their roles in facilitating the process of scholarly communication. Different from existing studies, we consider both the user types and the automation of accounts to profile influential users in the network of research dissemination. Our study found that similar to academic users, non-academic users can also be active players in communicating scientific publications. The results highlight the intensive interactions between human users and automated accounts, including bots and cyborgs, which accounted for 45% of connections among the top users. This study also demonstrates the important role of automated accounts in initiating and facilitating research dissemination. Specifically, (1) bot-assisted academic publishers showed the highest amplifier scores, which measures a user's tendency of being the first to share information and reach out to others within their trusted networks, (2) 5.28% of the selected articles was first tweeted by automated research feeds, ranking the fourth among the 22 classified user groups, and (3) bot-assisted publishers and automated feeds of generic topics and news alerts were highly ranked in authority, a network measure to quantify the degree to which a user consumes important resources of relevant topics. In the conclusion section, we discuss future directions to improve the validity of Twitter metrics in assessing research impacts.
URI: https://hdl.handle.net/10356/162615
ISSN: 0138-9130
DOI: 10.1007/s11192-022-04343-4
Rights: © 2022 Akadémiai Kiadó, Budapest, Hungary.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:WKWSCI Journal Articles

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