New Information in Trending Topics of Tweets by Labelled Clusters

Amina Madani (), Omar Boussaid () and Djamel Eddine Zegour ()
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Amina Madani: LRDSI Laboratory, Blida 1 University, BP 270 Soumaa road, Blida, Algeria
Omar Boussaid: ERIC Laboratory, Lumière lyon 2 University, Lyon, France
Djamel Eddine Zegour: LCSI Laboratory, National High School of Computer Science, Algiers, Algeria

Journal of Information & Knowledge Management (JIKM), 2015, vol. 14, issue 03, 1-12

Abstract: Twitter is a popular micro-blogging service, and one of the main means of spreading ideas and information throughout the web. In this system, participants post short status messages called tweets that are often available publicly. Recently, the exponential growth of tweets has started to draw the attention of researchers from various disciplines. Numerous research approaches in the data mining field have examined Twitter. How to automatically extract useful information from tweets has therefore become an important research topic. The aim of this paper is to bring up what's up which is a new approach of tweets mining. It is a more general approach that discovers many different trending topics from tweets in real-time. Trending topics have generated big interest not only for the users of Twitter but also for information seekers. Our trending topics are detected for a specific geographic town and compared with the top trending topics shown on Twitter. They are presented by labelled clusters that constitute an accurate description of each trending topic. Each cluster is labelled by an emerging trending topic and is composed of keywords that represent the properties of the trending topic.

Keywords: Twitter; tweets mining; tweets trending topics; tweets clustering; real-time trending topics detection (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0219649215500197

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