In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook and YouTube. In these highly interactive systems the general public are able to post real-time reactions to "real world" 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

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... 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 which comprises five main components: data collection, preprocessing, classification, online clustering and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller scale "disruptive events" -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, even better in some cases.