Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TI: an efficient indexing mechanism for real-time search on tweets
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Tweets as data: demonstration of TweeQL and Twitinfo
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
QuickView: advanced search of tweets
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Open domain event extraction from twitter
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Online social streams such as Twitter/Facebook timelines and forum discussions have emerged as prevalent channels for information dissemination. As these social streams surge quickly, information overload has become a huge problem. Existing keyword search engines on social streams like Twitter Search are not successful in overcoming the problem, because they merely return an overwhelming list of posts, with little aggregation or semantics. In this demo, we provide a new solution called \keysee by grouping posts into events, and track the evolution patterns of events as new posts stream in and old posts fade out. Noise and redundancy problems are effectively addressed in our system. Our demo supports refined keyword query on evolving events by allowing users to specify the time span and designated evolution pattern. For each event result, we provide various analytic views such as frequency curves, word clouds and GPS distributions. We deploy \keysee on real Twitter streams and the results show that our demo outperforms existing keyword search engines on both quality and usability.