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

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          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 & Halpern (2002), 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.

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          Author and article information

          Journal
          14 June 2020
          Article
          10.1007/s11192-020-03566-7
          2006.07937
          78c4f3fb-4c67-4f4e-8937-8be924a10a64

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          Scientometrics, 2020
          23 pages, 5 figures, paper accepted for publication in Scientometrics
          cs.DL

          Information & Library science
          Information & Library science

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