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Sensing real-world events using social media data and a classification-clustering framework

Alsaedi, Nasser, Burnap, Peter and Rana, Omer Farooq 2016. Sensing real-world events using social media data and a classification-clustering framework. Presented at: IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, USA, 13-16 October 2016. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, pp. 216-223. 10.1109/WI.2016.0039

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Abstract

In recent years, there has been increased interest in real-world event identification using data collected from social media, where the Web enables the general public to post real-time reactions to terrestrial events - thereby acting as social sensors of terrestrial activity. Automatically extracting and categorizing activity from streamed data is a non-trivial task. To address this task, we present a novel event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering allows events to be detected - including “disruptive” events - incidents that threaten social safety and security, or could disrupt the social order. We evaluate our framework on a large-scale, real-world dataset from Twitter. We also compare our results to other leading approaches using Flickr MediaEval Event Detection Benchmark

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISBN: 978-1-5090-4470-2
Last Modified: 28 Nov 2017 21:44
URI: http://orca-mwe.cf.ac.uk/id/eprint/97625

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