Automated Delineation of Subgroups in Web Video
A Medical Activism Case Study
Bibliographic Data
| ID | 12449333 |
|---|---|
| Authors | Alvin Chin (0000-0003-2201-8220, University of Toronto, corresponding author), Jennifer Keelan (0000-0003-0985-5139, University of Toronto), George Tomlinson (0000-0002-9328-6399, University of Toronto), Vera Pavri-Garcia (York University), Kumanan Wilson (0000-0002-1741-7705, University of Ottawa), Mark Chignell (0000-0001-8120-6905, University of Toronto) |
| Year | 2010 |
| Volume | 15 |
| Issue | 3 |
| Pages | 447-464 |
| Publication date | 2010-04-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Computer-Mediated Communication (JOURNAL) |
| Journal identifiers | ISSN: 1083-6101 • E-ISSN: 1083-6101 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/j.1083-6101.2010.01507.x |
| OpenAlex | W2081356122 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
Web 2.0 tools in general, and Web video in particular, provide new ways for activists to express their viewpoints to a broad audience. In this paper we deployed tools that have been used to find subgroups automatically in social networks and applied them to the problem of distinguishing between two sides of a controversial issue based on patterns of online interaction. We explored the problem of distinguishing between anti- and pro-vaccination activists based on a social network of videos and associated comments posted on YouTube. Videos for the analysis were selected by submitting the term "vaccination" to a search on YouTube. A content analysis of the selected videos was then performed (Keelan et al, 2007) to classify videos as pro- or anti-vaccination. Then, a modified version of the SCAN method (Chin and Chignell, 2008) for identifying cohesive subgroups in social networks was applied to the social network inferred from the discussions about the videos. Results showed that a cohesive subgroup of anti-vaccination people existed in discussions around anti-vaccination videos, whereas discussions around pro-vaccination videos included both anti-vaccination and pro-vaccination people. Implications of the method and results for more general delineation of types of medical activism and the opposing camps within those camps are discussed
Internet privacy · Pathology · Social media · Social network (sociolinguistics · Social network analysis · Vaccination · Viewpoints · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Medicine · Misinformation and Its Impacts · Psychology · Wikis in Education and Collaboration
Social Network Analysis
Centrality measures in spatial networks of urban streets
Assessing, Controlling, and Assuring the Quality of Medical Information on the Internet
Community structure in social and biological networks
Modularity and community structure in networks
Social networks and Internet connectivity effects
Centrality in social networks conceptual clarification
The stability of centrality measures when networks are sampled
Issue Publics on the Web
Publicly Private and Privately Public
Studying Online Social Networks
E-Mail as Spectroscopy
A graph‐theoretic definition of a sociometric clique
Cohesion as a Basic Bond in Groups
Doctor in the house
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |