A peak detection method to uncover events from social media

Social networking services like Twitter and Instagram are a valuable sources of information to find out what happened or what is happening in a geographic area. This paper presents a method to catch and understand relevant events and happenings from social geo-tagged data. The proposed method consists in two main phases: (i) extraction of space-time features from social data and their modelization as time series, (ii) peak detection from time series, for identifying deviation from user normal behavior. Results of the experimental evaluation, performed over a real-word dataset of tweets, show that the proposed approach is able to accurately detect several relevant events, bounded to a geographic location and of varying importance and character, like exhibitions, festivals, competitions, and terrorist attacks such as that done at the Charlie Hebdo offices.We achieve a space accuracy up to 90%, and a time accuracy up to 95%.