Twitter, one of the most popular microblogging services, allows users to publish short messages on a wide variety of subjects such as news, events, stories, ideas, and opinions, called tweets. The popularity of Twitter, to some extent, arises from its capability of letting users promptly and conveniently contribute tweets to convey diverse information. Specifically, with people discussing what is happening outside in the real world by posting tweets, Twitter captures invaluable information about real-world news and events, spanning a wide scale from large national or international stories like a presidential election to small local stories such as a local farmers market.  Detecting and extracting small news and events for a local place is a challenging problem and the focus of this thesis.  In this thesis, we explore several directions to extract and detect local news and events using tweets in Twitter: a) how to identify local influential people on Twitter for potential news seeders; b) how to recognize unusualness in tweet volume as signals of potential local events;  c) how to overcome the data sparsity of local tweets to detect more and smaller undergoing local news and events. Additionally, we also try to uncover implicit correlations between location, time and text in tweets by learning embeddings for them using a universal representation under the same semantic space.

 

                           Chair:              Dr. Hanan Samet
                          Dean's rep:      Dr. Leila De Floriani
                          Members:        Dr. David Mount
                                                    Dr. Dinesh Manocha