{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/85229"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/85229","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Visualizing Trends on Twitter","abstract":"With its popularity, Twitter has become an increasingly valuable source of real-time, user-generated information about interesting events in our world. This thesis presents TwitGeo, a system to explore and visualize trending topics on Twitter. It features an interactive map that summarizes trends across dierent geographical regions. Powered by a novel GPU-based datastore, this system performs ad hoc trend detection without predefined temporal or geospatial indexes, and is capable of discovering trends with arbitrary granularity in both dimensions. 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