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Compared with traditional news media, social media nowadays provides a richerand more timely source of news. We are interested in multi-spatial level eventdetection from geo-tagged tweet streams. Specifically, in this paper we (1)examine the statistical characteristic for the time series of the number ofgeo-tagged tweets posted from specific regions during a short time interval,e.g., ten seconds or one minute; (2) verify from over thirty datasets thatwhile almost all such time series exhibit self-similarity, those thatcorrespond to events, especially short-term and unplanned outbursts, follow apower-law distribution; (3) demonstrate that these findings can be applied tofacilitate event detection from tweet streams. We propose twoalgorithms---Power-law basic and Power-law advanced, where Power-law basic onlychecks the existence of power-law distributions in the time series from tweetstreams at multi-spatial scales, without looking into the content of eachtweet, and Power-law advanced integrates power-law verification with semanticanalysis via word embedding. Our experiments on multiple datasets show that byconsidering spatio-temporal statistical distributions of tweets alone, theseemingly naive algorithm of Power-law basic achieves comparable results withmore advanced event detection methods, while the semantic analysis enhancedversion, Power-law advanced, can significantly increase both the precision andthe recall.

Multi-spatial Scale Event Detection from Geo-tagged Tweet Streams via Power-law Verification