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In these highly interactive systems, the general public are able to post real-time reactions to \u201creal world\u201d events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale \u201cdisruptive events,\u201d smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases.<\/jats:p>","DOI":"10.1145\/2996183","type":"journal-article","created":{"date-parts":[[2017,3,27]],"date-time":"2017-03-27T12:25:10Z","timestamp":1490617510000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":92,"title":["Can We Predict a Riot? 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