DeLLe

Abstract

Geotagged tweet streams contain invaluable information about the real-world local events like sports games, protests and traffic accidents. Timely detecting and extracting such events may have various applications but yet unsolved challenges. In this paper, we present DeLLe, a methodology for automatically Detecting Latest Local Events from geotagged tweets. With the help of novel spatio temporal tweet count prediction models, DeLLe first finds unusual locations which have aggregated unexpected number of tweets in the latest time period and thereby imply potential local events. Next, DeLLe calculates, for each such unusual location, a ranking score to identify the ones most likely having ongoing local events by addressing the temporal burstiness, spatial burstiness and topical coherence. Furthermore, DeLLe infers an event candidate's spatio temporal range by tracking its event-focus point, which essentially reflects the most recent representative occurrence site. Finally, DeLLe chooses the most influential tweets to summarize local events and thereby presents succinct but yet representative descriptions. We evaluate DeLLe on the city of Seattle, WA as well as a larger city of New York. The results show that the proposed method generally outperforms competitive baseline approaches.