research-article

Can We Predict a Riot? Disruptive Event Detection Using Twitter

Authors:

Nasser Alsaedi,

Omer RanaAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 17, Issue 2

Article No.: 18, Pages 1 - 26

https://doi.org/10.1145/2996183

Published: 27 March 2017 Publication History

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.

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Cited By

Han ZShi LLiu LJiang LFang JLin FZhang JPanneerselvam JAntonopoulos N(2024)A Survey on Event Tracking in Social Media Data StreamsBig Data Mining and Analytics10.26599/BDMA.2023.90200217:1(217-243)Online publication date: Mar-2024
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Index Terms

Can We Predict a Riot? Disruptive Event Detection Using Twitter
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 17, Issue 2

Special Issue on Advances in Social Computing and Regular Papers

May 2017

249 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3068849

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2017

Accepted: 01 September 2016

Revised: 01 July 2016

Received: 01 March 2016

Published in TOIT Volume 17, Issue 2

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https://doi.org/10.26599/BDMA.2023.9020021
Zanella AMadariaga DMishra SMartínez-Durive OSmoreda ZFiore MVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)Characterizing, Modeling and Exploiting the Mobile Demand Footprint of Large Public ProtestsProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688421(516-529)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3646547.3688421
Warnke SRunfola D(2024)Predicting Protests and Riots in Urban Environments With Satellite Imagery and Deep LearningTransactions in GIS10.1111/tgis.13236Online publication date: 30-Aug-2024
https://doi.org/10.1111/tgis.13236
Seetha AChouhan SPilli ERaychoudhury V(2024)D$i$E$v$D: Disruptive Event Detection From Dynamic Datastreams Using Continual Machine Learning: A Case Study With TwitterIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.327297312:3(727-738)Online publication date: Jul-2024
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