A combined classification-clustering framework for identifying disruptive events

Alsaedi, Nasser, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X and Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 2014. A combined classification-clustering framework for identifying disruptive events. Presented at: ASE SocialCom Conference, Stanford University, CA., USA, May 27-21 2014.

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Abstract

Twitter is a popular micro-blogging web application serving hundreds of millions of users. Users publish short messages to communicate with friends and families, express their opinions and broadcast news and information about a variety of topics all in real-time. User-generated content can be utilized as a rich source of real-world event identification as well as extract useful knowledge about disruptive events for a given region. In this paper, we propose a novel detection framework for identifying real-time events, including a main event and associated disruptive events, from Twitter data. Theapproach is based on five steps:data collection, pre-processing,classification, online clustering and summarization. We use a Naïve Bayes classification model and an Online Clustering method to validate our model on a major real-world event (Formula 1 Abu Dhabi Grand Prix 2013).

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date of First Compliant Deposit:	30 March 2016
Last Modified:	27 Oct 2022 09:11
URI:	https://orca.cardiff.ac.uk/id/eprint/64701

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