Arabic event detection in social media

Alsaedi, Nasser and Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X 2015. Arabic event detection in social media. Presented at: 16th International Conference on Intelligent Text Processing and Computational Linguistics, Cairo, Egypt, 14-20 April 2015. Published in: Gelbukh, Alexander ed. Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part I. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.9041 Springer Verlag, pp. 384-401. 10.1007/978-3-319-18111-0_29

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Abstract

Event detection is a concept that is crucial to the assurance of public safety surrounding real-world events. Decision makers use information from a range of terrestrial and online sources to help inform decisions that enable them to develop policies and react appropriately to events as they unfold. One such source of online information is social media. Twitter, as a form of social media, is a popular micro-blogging web application serving hundreds of millions of users. User-generated content can be utilized as a rich source of information to identify real-world events. In this paper, we present a novel detection framework for identifying such events, with a focus on ‘disruptive’ events using Twitter data. The approach is based on five steps; data collection, pre-processing, classification, clustering and summarization. We use a Naïve Bayes classification model and an Online Clustering method to validate our model over multiple real-world data sets. To the best of our knowledge, this study is the first effort to identify real-world events in Arabic from social media.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords:	Text mining, Information Extraction, Classification, Online Clustering, Machine Learning, Event detection.
Publisher:	Springer Verlag
ISBN:	9783319181103
ISSN:	0302-9743
Date of First Compliant Deposit:	30 March 2016
Last Modified:	27 Nov 2024 21:15
URI:	https://orca.cardiff.ac.uk/id/eprint/72980

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