Network Analysis of Digital and Social Media Minitrack
Permanent URI for this collection
Network science focuses on the structure of systems and how the components of a system come together, expressed as patterns or regularities in relationships among interacting units. Network analysis reveals the underlying structure and the dynamic interactions among system components. Network science and the development of digital and social media have co-evolved as catalysts of each other’s advancement, and the increased use of social and digital media provides scientists with a wealth of precise and novel data.
We welcome submissions that represent insightful ways that network analysis can be used to better understand social and digital media. Both methodologically and theoretically driven papers are encouraged, as well as empirical research (e.g. telecommunication, health, website specific, international, organizational, etc.) that push the boundaries of network science as applied to social and digital media. Forward-thinking and boundary-spanning forms of research including, but not limited to, the study of multi-level, localized and ego-centric networks are particularly welcome.
List of topics, as related to Digital and/or Social Media:
- Theories of Social Networks
- Models of Social Networks
- Communication Networks
- Telecommunication Networks
- Internet
- World-wide Web
- Longitudinal Network Analysis
- Visualization of Social Networks
- Two-mode / Affiliation / Bi-partite Networks
- Determining Network Structure and Processes
- Self-organizing and Decentralized Networks
- Social Influence
- Social Factors
Minitrack Co-Chairs:
Devan Rosen (Primary Contact)
Ithaca College
Email: drosen@ithaca.edu
George Barnett
University of California - Davis
Email: gbarnett@ucdavis.edu
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Recent Submissions
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ItemCross-National Proximity in Online Social Network and Protest Diffusion: An Event History Analysis of Arab Spring( 2017-01-04)This study examines the role of online social network proximity in cross-national diffusion of offline protests. Drawn upon Valente’s (1995) network diffusion model, the study operationalizes social network proximity-based protest exposure, using the international Facebook friendship share data. One year-long onsite protests during Arab Spring 2011 are examined using event history modeling. The findings offer evidence of an contemporaneous online network exposure effect on cross-national diffusion of protests. An expected lagged diffusion effect was not found, however. The paper presents an innovative approach to the scholarship of global protest diffusion and collective actions. \
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ItemExamining Collective Memory Building in Wikipedia: A Multilevel Network Approach( 2017-01-04)This study interprets Wikipedia as a memory place where independent contributors discuss and negotiate the meanings of past events in a collaborative way. We examine how interconnections between high-impact events lead to the differential patterns of collective memory building. The results show that the presence of a direct network tie between two events is related to a smaller difference in the patterns of collective memory building among Wikipedia users. In addition, a higher degree of structural equivalence between two events makes them more similar in terms of the patterns of collective memory building. Our findings reveal that the range of network effects is not confined within the local substructure but can extend to the global level as well. The results also confirm that Wikipedia editors will refer to the editing patterns of the events that have direct links or occupy similar structural positions with those they intend to contribute to. \ \
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ItemApplications of Cohesive Subgraph Detection Algorithms to Analyzing Socio-Technical Networks( 2017-01-04)Socio-technical networks can be productively modeled at several granularities, including the interaction of actors, how this interaction is mediated by digital artifacts, and sociograms that model direct ties between the actors themselves. Cohesive subgraph detection algorithms (CSDA, a.k.a. “community detection algorithms”) are often applied to sociograms, but also have utility in analyzing graphs corresponding to other levels of modeling. This paper illustrates applications of CSDA to graphs modeling interaction and mediated association. It reviews some leading candidate algorithms (particularly InfoMap, link communities, the Louvain method, and weakly connected components, all of which are available in R), and evaluates them with respect to how useful they have been in analyzing a large dataset derived from a network of educators known as Tapped In. This practitioner-oriented evaluation is a complement to more formal benchmark based studies common in the literature.
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