Power-law Verification for Event Detection at Multi-spatial Scales from Geo-tagged Tweet Streams

Compared with traditional news media, social media nowadays provides a richer and more timely source of news. We are interested in multi-spatial level event detection from geo-tagged tweet streams. Specifically, in this paper we (1) examine the statistical characteristic for the time series of the number of geo-tagged tweets posted from specific regions during a short time interval, e.g., ten seconds or one minute; (2) verify from over thirty datasets that while almost all such time series exhibit self-similarity, those that correspond to events, especially short-term and unplanned outbursts, follow a power-law distribution; (3) demonstrate that these findings can be applied to facilitate event detection from tweet streams---we propose a simple algorithm that only checks the existence of power-law distributions in the time series from tweet streams at multi-spatial scales, without looking into the content of each tweet. Our experiments on multiple datasets show that by considering spatio-temporal statistical distributions of tweets alone, this seemingly naive algorithm achieves comparable results with event detection methods that perform semantic analysis. We further discuss how to integrate the proposed technique into existing algorithms for better performance.