Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach

@article{Guille2015EventDT,
  title={Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach},
  author={Adrien Guille and C{\'e}cile Favre},
  journal={Social Network Analysis and Mining},
  year={2015},
  volume={5},
  pages={1-18}
}
The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and… CONTINUE READING
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