Towards a second generation of ‘social media metrics’: Characterizing Twitter communities of attention around science

Abstract

‘Social media metrics’ are bursting into science studies as emerging new measures of impact related to scholarly activities. However, their meaning and scope as scholarly metrics is still far from being grasped. This research seeks to shift focus from the consideration of social media metrics around science as mere indicators confined to the analysis of the use and visibility of publications on social media to their consideration as metrics of interaction and circulation of scientific knowledge across different communities of attention, and particularly as metrics that can also be used to characterize these communities. Although recent research efforts have proposed tentative typologies of social media users, no study has empirically examined the full range of Twitter user’s behavior within Twitter and disclosed the latent dimensions in which activity on Twitter around science can be classified. To do so, we draw on the overall activity of social media users on Twitter interacting with research objects collected from the Altmetic.com database. Data from over 1.3 million unique users, accounting for over 14 million tweets to scientific publications, is analyzed. Based on an exploratory and confirmatory factor analysis, four latent dimensions are identified: ‘Science Engagement’, ‘Social Media Capital’, ‘Social Media Activity’ and ‘Science Focus’. Evidence on the predominant type of users by each of the four dimensions is provided by means of VOSviewer term maps of Twitter profile descriptions. This research breaks new ground for the systematic analysis and characterization of social media users’ activity around science.

“Heterogeneous couplings”: Operationalizing network perspectives to study science‐society interactions through social media metrics

“…In that study, over 125 thousand Twitter users were studied and categorized based on their engagement with scientific publications. The study found that tweeters of scientific papers tend to describe themselves in their Twitter profiles with a combination of academic, personal, and topical terms (a similar pattern as also as in (Díaz-Faes, Bowman, & Costas, 2019). Another example of communities of attention is the analysis of (Van Van Schalkwyk, 2019) on the communities of tweeters involved in the anti-vaccine campaign, identifying groups of tweeters pro and anti-vaccine mentioning different sets of scientific publications.…”

Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation

“…Online social media is no exception: Patterns of network homophily have been demonstrated in numerous studies of Twitter users [ 19 – 23 ], and there is evidence to suggest that the homophily of an individual’s online connections generally mirrors their face-to-face networks [ 24 ]. Recent studies have applied this principle to the study of altmetrics and demonstrated that deeper contextualization of a publication’s audience on social media can be achieved by examining various aspects of how individual users are networked with others on Twitter [ 13 , 25 ]. More specifically, network homophily enables the identification of various characteristics of an individual Twitter user based on the self-descriptions of the accounts connected with that individual on Twitter [ 20 , 26 ].…”

The stability of Twitter metrics: A study on unavailable Twitter mentions of scientific publications

“…Therefore, researchers are increasingly paying more attention to the content analysis of Twitter mentions and the behavioral analysis of Twitter users, going beyond the mere counting of tweets linking to scientific publications (Bornmann, 2014; Haustein, 2019). Twitter usersʼ identities, motivations, and related interactions or engagement behaviors have been widely analyzed in order to improve the understanding of Twitter metrics in a much more fine‐grained manner (Díaz‐Faes, Bowman, & Costas, 2019; Haustein, Bowman, & Costas, 2015; Holmberg, Bowman, Haustein, & Peters, 2014). Nevertheless, rethinking the tweeting patterns and Twitter user behaviors in more detail comes with worries and problems that have aroused the concern of researchers.…”