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Communities of attention networks: introducing qualitative and conversational perspectives for altmetrics

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  • Ronaldo Ferreira Araujo

    (Federal University of Alagoas)

Abstract

We propose to analyze the level of recommendation and spreading in the sharing of scientific papers on Twitter to understand the interactions of communities around papers and to develop the “community of attention network” (CAN). In this paper, a pilot case study was conducted for the paper ‘Pharmacological Treatment of Obesity’ authored by Mancini and Halpern (Arquivos Brasileiros de Endocrinologia & Metabologia 46(5):497–512, 2002. https://doi.org/10.1590/S0004-27302002000500003 ), an extensive review of the criteria for evaluating the efficacy of anti-obesity treatments and derived pharmacological agents. The altmetric data was collected from Altmetric.com and the description information for each tweeter was extracted from their Twitter profiles. The data were analyzed with Microanalysis of Online Data perspective to investigate the formation of a CAN around this focal paper and the context of its formation. The studied article received 736 tweets from 134 different users with a combined exposure of more than 459,018 followers and a high level of spreading (67.26%) and recommendation (28.53%). The user’s bios information analysis of who shares the article indicate individual profiles focused on personal issues and strong civic and political engagement. Personal-professional and institutional tweeters of the national political scene are often mentioned in the tweets. In analyzing the content of the tweets, we note that the altmetric score of the paper is a result of its strategic use as an online activism resource and a digital advocacy tool used to mobilize stakeholders for awareness and support activities. This study and the contextual and network perspective it introduces may help to understand the social impact of publications by using altmetrics.

Suggested Citation

  • Ronaldo Ferreira Araujo, 2020. "Communities of attention networks: introducing qualitative and conversational perspectives for altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1793-1809, September.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03566-7
    DOI: 10.1007/s11192-020-03566-7
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    References listed on IDEAS

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    1. Yu, Houqiang & Xiao, Tingting & Xu, Shenmeng & Wang, Yuefen, 2019. "Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters," Journal of Informetrics, Elsevier, vol. 13(3), pages 841-855.
    2. Cassidy R. Sugimoto & Sam Work & Vincent Larivière & Stefanie Haustein, 2017. "Scholarly use of social media and altmetrics: A review of the literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(9), pages 2037-2062, September.
    3. Anthony F. J. van Raan, 2004. "Sleeping Beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 59(3), pages 467-472, March.
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    Cited by:

    1. Hou, Jianhua & Wang, Yuanyuan & Zhang, Yang & Wang, Dongyi, 2022. "How do scholars and non-scholars participate in dataset dissemination on Twitter," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Zhichao Fang & Rodrigo Costas & Wencan Tian & Xianwen Wang & Paul Wouters, 2021. "How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 918-932, July.
    3. Zhichao Fang & Rodrigo Costas & Paul Wouters, 2022. "User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4523-4546, August.
    4. Jianhua Hou & Hao Li & Yang Zhang, 2023. "Altmetrics-based sleeping beauties: necessity or just a supplement?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5477-5506, October.

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