Automated Delineation of Subgroups in Web Video: A Medical Activism Case Study |
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| Authors: | Alvin Chin Jennifer Keelan George Tomlinson Vera Pavri‐Garcia Kumanan Wilson Mark Chignell |
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| Affiliation: | 1. Department of Computer Science, University of Toronto, Toronto, Canada (alvin.chin@utoronto.ca);2. Alvin Chin's current position is at the Nokia Research Center, Beijing, China. He may be contacted at . The work reported in this paper was done while he was a PhD student at the University of Toronto.;3. Dalla Lana School of Public Health, University of Toronto, Toronto, Canada (jenn.keelan@utoronto.ca, george.tomlinson@utoronto.ca);4. Division of Natural Sciences, York University, Toronto, Canada (pavri@yorku.ca);5. Canada Research Chair in Public Health Policy, University of Ottawa, Ottawa, Canada (kwilson@ohri.ca);6. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada (chignell@mie.utoronto.ca) |
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| Abstract: | 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. |
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| Keywords: | medical activism social network analysis cluster analysis Web Video hierarchical clustering subgroups |
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