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Much of human communication is now mediated by online social networks. Twitter, Facebook, and Youtube now compete for our collective attention in much the same way as television, radio, and newspapers did for previous generations. But contemporary online social media are qualitatively different from media of the past. Online communication leaves a record of who said what to whom, when, and on what topic. The development of new analytical tools offer the possibility to use these records to track popular on-line topics and to identify the demographics of groups contributing to these topics, including the geographic location of contributors, as well as their age, gender, and ethnicity. What is more, it is possible for ad hoc groups to coalesce around topics in real time. On the one hand, these data present a challenge for computer scientists to develop new tools that enable the tracking of these kinds of information. Success in this domain offers significant practical benefits in business, marketing, and politics. At the same time, however, the ability to track these kinds of information raise privacy concerns, both for individuals and for members of groups who can be identified by the emerging technology. In our research, computer scientists will develop tools that enable tracking of topics and group memberships, and communication researchers will identify the kinds of privacy concerns that people develop around these kinds of information, when, why, and with what consequences.
The research described in this project builds on prior work on trend analysis and community extraction, seeking to advance research on two fronts: the efficient identification of ad hoc communities which focus on a popular topic, and the understanding of individual versus group privacy based on the identified communities. Although trend analysis is a burgeoning area of research in Computer Science, existing models focus on the correlation of only one dimension (e.g., location) with trending topics. This research represents a step forward by providing a more sophisticated and powerful tool that allows for the extraction of interesting and potentially useful trend patterns through Topic Based Community Identification. At the same time, this community identification may prompt new types of group privacy concerns that have not been researched in social science, which has mainly focused on individual rather than group privacy. This approach provides a unique opportunity to significantly impact scholarly understanding of the mechanisms and dynamics of individual and group privacy concern, especially with regard to ad-hoc, topic-based groups, and their effects on Social Media users' attitudes and behavior. If group-level privacy is a concern beyond individual privacy, then we expect to find that people will express group privacy concerns when a group they identify with is included in the tracking information, especially when topics are morally loaded, but independent of whether the individual participant is personally involved in the Twitter conversation. This has the potential to develop necessary and sufficient conditions for the emergence of group privacy concerns.
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EAGER: Towards a Better Understanding of Group Privacy in Social Media Community Detection