Title: Upgrading event and pattern detection to big data

Authors: Soumaya Cherichi; Rim Faiz

Addresses: IHEC Carthage, University of Carthage, Tunis 2016, Tunisia ' IHEC Carthage, University of Carthage, Tunis 2016, Tunisia

Abstract: One of the marvels of our time is the unprecedented development and use of technologies that support social interaction. Social mediating technologies have engendered radically new ways of information and communication, particularly during events; in case of natural disasters like earthquakes, tsunami, and American presidential election. This paper is based on data obtained from Twitter because of its popularity and sheer data volume. This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. Taking into account all the difficulties, we propose a new event detection approach combining linguistic features and Twitter features. Finally, we present our event detection system from microblogs that aims: 1) to detect new events; 2) to recognise temporal markers pattern of an event; 3) and to classify important events according to thematic pertinence, author pertinence and tweet volume.

Keywords: microblogs; event detection; temporal markers; patterns; social network analysis.

DOI: 10.1504/IJCSE.2019.099078

International Journal of Computational Science and Engineering, 2019 Vol.18 No.4, pp.404 - 412

Received: 20 Apr 2016
Accepted: 05 Sep 2016

Published online: 08 Apr 2019 *

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