{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90843"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90843","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A preliminary approach to detect and track events in social media","abstract":"Many algorithms have been proposed to model spatiotemporal events in both sensor network and social networks. However, most of them can not fullfil the task in a social network data streaming context. We proposed an evolving Mean Shift clustering based algorithm to formulate a robust system to automatically detect and track events in social network media. We also demonstrate its performance in empirical experiments. 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However, most of them can not fullfil the task in a social network data streaming context. We proposed an evolving Mean Shift clustering based algorithm to formulate a robust system to automatically detect and track events in social network media. We also demonstrate its performance in empirical experiments. 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