Can we predict a riot? Disruptive event detection using Twitter

Alsaedi, Nasser, Burnap, Pete ORCID: https://orcid.org/0000-0003-0396-633X and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2017. Can we predict a riot? Disruptive event detection using Twitter. ACM Transactions on Internet Technology 17 (2) , 18. 10.1145/2996183

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Abstract

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 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 “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, and even better in some cases.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Data Innovation Research Institute (DIURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Association for Computing Machinery (ACM)
ISSN:	1533-5399
Date of First Compliant Deposit:	29 November 2017
Date of Acceptance:	1 September 2016
Last Modified:	04 Dec 2024 01:45
URI:	https://orca.cardiff.ac.uk/id/eprint/99582

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