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

Abstract

Social media metrics have a genuine networked nature, reflecting the networking characteristics of the social media platform from where they are derived. This networked nature has been relatively less explored in the literature on altmetrics, although new network-level approaches are starting to appear. A general conceptualization of the role of social media networks in science communication, and particularly of social media as a specific type of interface between science and society, is still missing. The aim of this paper is to provide a conceptual framework for appraising interactions between science and society in multiple directions, in what we call heterogeneous couplings. Heterogeneous couplings are conceptualized as the co-occurrence of science and nonscience objects, actors, and interactions in online media environments. This conceptualization provides a common framework to study the interactions between science and non-science actors as captured via online and social media platforms. The conceptualization of heterogeneous couplings opens wider opportunities for the development of network applications and analyses of the interactions between societal and scholarly entities in social media environments, paving the way toward more advanced forms of altmetrics, social (media) studies of science, and the conceptualization and operationalization of more advanced science-society studies.

A value creation model from science-society interconnections: Archetypal analysis combining publications, survey and altmetric data

“…Social media are communication channels with distinctive properties that shape the proliferation of science-society interactions (Costas et al, 2021). Actors of all kinds, including researchers and potential research users, government, and citizens, can communicate directly (Davies & Hara, 2017;Kavanaugh et al, 2012;N.…”

“…Real-time knowledgefocused social media interactions include monitoring and managing crisis events (Yin et al, 2012), and tracking and tracing research use for public health outcomes (Young et al, 2014). Costas and colleagues propose a general framework for social media structuring of science-society interactions as 'heterogeneous couplings' or the 'co-occurrence of science and non-science objects, actors, and interactions' (Costas et al, 2021). From our perspective, of particular interest is the potential of social media to amplify science communication through mechanisms including 'likes' and 're-tweets' (Twitter) and 'shares' (Facebook).…”

“…Such approaches are useful for reconstructing pathways between research activities, research outputs, and linked outcomes at the level of specific research projects, programmes, or organisations (Matt et al, 2017). Each of these approaches treats the conceptualisation of societal value from research differently (Smit & Hessels, 2021) and focuses on different configurations of connections between 'science and non-science' entities (Costas et al, 2021). This paper seeks to contribute to this discussion and to empirical analysis of the generation of value from research in society, but in a different way to that found in the 'research impact' literature.…”

How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications

“…For instance, user mentions establish the relationships among users who might be related to or interested in the mentioned research, based on which the communities of users sharing interest can be detected (Araujo, 2020; Pearce, Holmberg, Hellsten, & Nerlich, 2014; Said et al, 2019; Van Schalkwyk, Dudek, & Costas, 2020). Hashtags added in scholarly Twitter mentions indicate particular concepts in relation to the mentioned publications (Haustein, Bowman, & Costas, 2016); therefore, the adoption of hashtags provides the opportunities of identifying not only the connections among tweets or users focusing on the same topics (Costas, Rijcke, & Marres, 2020; Hellsten & Leydesdorff, 2020), but also the broader public concerns about some specific research topics (Haunschild, Leydesdorff, Bornmann, Hellsten, & Marx, 2019; Lyu & Costas, 2020).…”

Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics

“…This leads to a new scenario in which the altmetric research is focused on the relational attributes of the social media activity rather than focusing on features (i.e., impact) related to scientific publications. To do so, the methodological framing has also changed, focusing now on techniques which help discover and analyze different kinds of social interactions (Costas et al, 2020 ) that allow a better understanding of science-society relations. However, these new approaches focus mainly on researchers discovering and topic visualizations in social media.…”