Tweet Coupling: a social media methodology for clustering scientific publications

Abstract

We argue that classic citation-based scientific document clustering approaches, like co-citation or bibliographic coupling, lack to leverage the social-usage of the scientific literature originate through online information dissemination platforms, such as Twitter. In this paper, we present the methodology tweet coupling, which measures the similarity between two or more scientific documents if one or more Twitter users mention them in the tweet(s). We evaluate our proposal on an altmetric dataset, which consists of 3,081 scientific documents and 8,299 unique Twitter users. By employing the clustering approaches of bibliographic coupling and tweet coupling, we find the relationship between the bibliographic and tweet coupled scientific documents. Further, using VOSviewer, we empirically show that tweet coupling appears to be a better clustering methodology to generate cohesive clusters since it groups similar documents from the subfields of the selected field, in contrast to the bibliographic coupling approach that groups cross-disciplinary documents in the same cluster.

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

“…Applying the heterogeneous coupling framework proposed here, we would argue that regarding Twitter, in fact they performed a co‐tweeter analysis (rather than a co‐tweet analysis), since as per our model in Figure 3, they focused on the coupling of papers by tweeters (i.e., the agents) and not just in tweets (i.e., the acts). Another example in which certain level of confusion could be found is in the tweet coupling suggested by (Hassan et al, 2020), which they defined as the “similarity between two or more scientific documents if one or more Twitter users mention then in the tweet(s)”. In our framework, the proposal by Hassan and colleagues would fit better in the co‐tweeter analysis, and conceptually speaking is virtually the same approach as described by (Didegah & Thelwall, 2018).…”

“…Thus, for example the work by Haunschild and Bornmann (2015a, 2015b) could have been supported to clarify the “readership coupling” nature of their own work. Similarly, the work by Hassan et al (2020) and Didegah and Thelwall (2018) would have counted with a common framework to frame their work as co‐tweeter analyses, instead of considering their study as a tweet coupling analysis (in the case of Hassan and colleagues) or a co‐tweet analysis (in the case of (Didegah & Thelwall, 2018)). Moreover, the heterogeneous coupling framework has value for the more traditional types of altmetric studies.…”

“…However, this networked nature has been relatively less explored in the literature on altmetrics and scholarly communications, although new network-level approaches are starting to appear. Recent work on the analysis of communities of attention (Haustein, Bowman, & Costas, 2015), Twitter-based disciplinary social media communities (Said et al, 2019), the follower/followee relationships of scholarly authors on Twitter (Robinson-García, Van Leeuwen, & Rafols, ), the co-saved and co-tweet linkages of scientific publications (Didegah & Thelwall, 2018), tweet coupling (Hassan et al, 2020) or the proposal of co-readership (Kraker, Schlögl, Jack, & Lindstaedt, 2015), and readership coupling (Haunschild & Bornmann, 2015a) on Mendeley, are good examples of this new networked dimension in altmetrics research. However, 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.…”

Social media usage to share information in communication journals: An analysis of social media activity and article citations

“…With the data obtained from these platforms, a digital score was acquired for academic output [ 11 ]. All altmetrics are based on the using social media and other online tools for disseminating scholarly information [ 38 ]. The use of social media platforms contributes significantly the spread of shared information in a wider environment.…”

Online Social Network Information Dissemination Integrating Overconfidence and Evolutionary Game Theory

“…To date, the number of Chinese netizens has reached 854 million [1]. Thus, significant attention has been drawn to the online social network information dissemination (OSNID) research field [2][3][4][5].…”