Academic Social Networking Sites are Smaller, Denser Networks Conducive to Formal Identity Management, Whereas Academic Twitter is Larger, More Diffuse, and Affords More Space for Novel Connections
A Review of:
Jordan, K. (2019). Separating and merging professional and personal selves online: The structure and processes that shape academics’ ego-networks on academic social networking sites and Twitter. Journal of the Association for Information Science and Technology, 70(8), 830-842. https://doi.org/10.1002/asi.24170
Objective – To examine the structure of academics’ online social networks and how academics understand and interpret them.
Design – Mixed methods consisting of network analysis and semi-structured interviews.
Setting – Academics based in the United Kingdom.
Subjects – 55 U.K.-based academics who use an academic social networking site and Twitter, of whom 18 were interviewed.
Methods – For each subject, ego-networks were collected from Twitter and either ResearchGate or Academia.edu. Twitter data were collected primarily via the Twitter API, and the social networking site data were collected either manually or using a commercial web scraping program. Edge tables were created in Microsoft Excel spreadsheets and imported into Gephi for analysis and visualization. A purposive subsample of subjects was interviewed via Skype using a semi-structured format intended to illuminate further the network analysis findings. Transcripts were deductively coded using a grounded theory-based approach.
Main Results – Network analysis replicated earlier findings in the literature. A large number of academics have relatively few connections to others in the network, while a small number have relatively many connections. In terms of reciprocity (the proportion of mutual ties or pairings out of all possible pairings that could exist in the network), arts and humanities disciplines were significantly more reciprocal. Communities (measured using the modularity algorithm, which looks at the density of links within and between different subnetworks) are more frequently defined by institutions and research interests on academic social networking sites and by research interests and personal interests on Twitter. The overall picture was reinforced by the qualitative analysis. According to interview participants, academic social networking sites reflect pre-existing professional relationships and do not foreground social interaction, serving instead as a kind of virtual CV. By contrast, Twitter is analogized to a conference coffee break, where users can form new connections.
Conclusion – Academic social networking sites exhibit networks that are smaller, denser, more clustered around discrete modularity classes, and more reciprocal. Twitter networks are larger and more diffuse, which is more conducive to fostering novel connections. The author makes suggestions for how academic social networking sites could encourage network building and rethink how academic reputation is measured.
Copyright (c) 2020 Scott Goldstein
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