Xuchao Zhang, Zhiqian Chen, Liang Zhao, Arnold Boediardjo


In the era of information overload, people are struggling to make sense of complex story events in massive social media data. Most existing approaches are designed to address event extraction in news reports, documents and abstracts, but such approaches are not suitable for Twitter data streams due to their unstructured language, short-length messages, and heterogeneous features; few existing approach generates a story by considering both the shared topics throughout the story and the smooth connection between successive nodes simultaneously. In this paper, a novel Twitter stoRy generation framework via shAred subspaCe and tEmporal Smoothness called TRACES is proposed. Given a query of an ongoing event, a novel multi-task clustering method integrated with shared subspace and temporal smoothness (STMTC) is proposed to generate the event stories. Extensive experimental evaluations of data sets for different events demonstrate the effectiveness of this new approach.

Xuchao Zhang, Zhiqian Chen, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:
TRACES: Generating Twitter stories via shared subspace and temporal smoothness. IEEE BigData2017: 1688-1693


Publication Details

Date of publication:
January 15, 2018
IEEE International Conference on Big Data
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