Large-scale identification and characterization of scholars on Twitter

Abstract

This paper presents a new method for identifying scholars who have a Twitter account from bibliometric data from Web of Science (WoS) and Twitter data from Altmetric.com . The method reliably identifies matches between Twitter accounts and scholarly authors. It consists of a matching of elements such as author names, usernames, handles, and URLs, followed by a rule-based scoring system that weights the common occurrence of these elements related to the activities of Twitter users and scholars. The method proceeds by matching the Twitter accounts against a database of millions of disambiguated bibliographic profiles from WoS. This paper describes the implementation and validation of the matching method, and performs verification through precision-recall analysis. We also explore the geographical, disciplinary, and demographic variations in the distribution of scholars matched to a Twitter account. This approach represents a step forward in the development of more advanced forms of social media studies of science by opening up an important door for studying the interactions between science and social media in general, and for studying the activities of scholars on Twitter in particular.

Who tweets climate change papers? investigating publics of research through users’ descriptions

“…However, there is a possibility that scholars were not categorized as such depending on the words used in their Twitter bios. Other methods are better suited to assess overall participation by scholars [ 3 , 57 , 58 ], whereas our method focuses on their participation according to other groups of users.…”

“…Specifically, we categorized expressions and keywords used in Twitter profile descriptions to assess how they represent identity markers, and so type of users sharing climate change research papers. As with other studies, the detection of words related to the academic world is precise in that it reflects potential individual users involved in research, although it does not distinguish how close their research interests are to the topic at hand [ 57 , 76 ]. The overlap with other categories also shows how actors of research are not restricted to this role, whether through communicational (science communication), professional (administrative functions), political (policy making) or simply personal (being a parent, having pets, hobbies) activities [ 77 ].…”

“…Specifically, we looked at specific expressions that indicate how users present themselves on Twitter [ 28 ]. As such, our analysis does not aim to provide an exact mapping of the scientists or journalists on Twitter [ 57 , 64 ]. Rather, we understand expressions in Twitter descriptions as a proxy to investigate the potential reach of scholarly documents outside of academia.…”

“…Usually employed in conjuncture with textual analysis of Twitter profile description, these methods aim to understand how discussions or communities build up around scholarly documents. More direct approaches rely on the matching of bibliometric information with Twitter data to capture the scholars involved on Twitter [ 57 ], as well as the use of Twitter lists [ 51 , 58 ]. Our method build on these by investigating the categories of users sharing climate change research papers on Twitter through specific expressions in Twitter profiles descriptions.…”

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

“…New technological and methodological developments in altmetrics are also opening new perspectives on how social media metrics can inform broader debates about changing forms of engagement between science and society in a computational age. Recent work on the identification of individual scholars on Twitter (Costas, 2017;Costas et al, 2020;Ke, Ahn, & Sugimoto, 2017), as well as the identification of the social media presence of journals (Fraumann, Costas, Mugnaini, Packer, & Zahedi, 2016) or academic institutions (Adams, Gurney, & Marshall, 2007;Shields, 2016;Yolcu, 2013), point to the shift from an initial unidirectional perspective in altmetrics (focused on quantifying the reception of science objects on social media) toward a more bi-directional, relational perspective, in which a variety of forms of engagement with scholarly entities by a variety of actors on social media platforms can be studied. For the operationalization of more relational and bi-directional perspectives on science-society engagement, networkbased methodologies are well suited, as such perspectives can surface how diverse science-social actors and entities are brought into relation (coupled) in a social media environment.…”