Palo Alto, California USA
Sept. 18, 2011 to Sept. 21, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICSC.2011.51
Web-based social media networks have an increasing frequency of health-related information, resources, and networks (both support and professional). Although we are aware of the presence of these health networks, we do not yet know their ability to (1) influence the flow of health-related behaviors, attitudes, and information and (2) what resources have the most influence in shaping particular health outcomes. Lastly, the health research community lacks easy-to-use data gathering tools to conduct applied research using data from social media websites. In this position paper we discuss and sketch our current work on addressing fundamental questions about information flow in cancer-related social media networks by visualizing and understanding authority, trust, and cohesion. We discuss the development of methods to visualize these networks and information flow on them using real-time data from the social media website Twitter and how these networks influence health outcomes by examining responses to specific health messages.
social network analysis, virtual social networks, cancer-based Twitter networks, data visualization, trust inference, information-flow
Alexander Gross, Daniela Oliveira, "Understanding Cancer-Based Networks in Twitter Using Social Network Analysis", ICSC, 2011, 2012 IEEE Sixth International Conference on Semantic Computing, 2012 IEEE Sixth International Conference on Semantic Computing 2011, pp. 559-566, doi:10.1109/ICSC.2011.51