Research Papers

Detecting Rumor Patterns in Streaming Social Media

Proc. 2015 IEEE International Conference on Big Data

Rumor detection in streaming social media is a significant but challenging problem. In this paper, we present a method to identify rumor patterns in the streaming social media environment. Patterns which combine both structural and behavioral properties of rumor are firstly proposed to distinguish false rumors from valid news. A novel graph-based pattern matching algorithm is also described to detect rumor patterns from streaming social media data. Compared within Twitter data of rumors and non-rumors, our selected rumor patterns contain distinct properties of rumors in short-term series.

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Tokyo Institute of Technology
Shihan Wang and Takao Terano

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