A rule dynamics approach to event detection in Twitter with its application to sports and politics

Adedoyin-Olowe, M., Gaber, M. M., Dancausa, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 and Gomes, J. B. (2016) A rule dynamics approach to event detection in Twitter with its application to sports and politics. Expert Systems with Applications, 55. pp. 351-360. ISSN 0957-4174 doi: 10.1016/j.eswa.2016.02.028

Abstract/Summary

The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/78776
Identification Number/DOI	10.1016/j.eswa.2016.02.028
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Twitter; Hashtags; Associations rules; Rule matching; Rule mapping
Publisher	Elsevier
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