Abstract
Twitter is one of the most popular social media services that allow users to share and spread information. Twitter monitors their users’ postings and detects the most discussed topics of the moment. Then, they publish these topics on the list, called ‘Trending Topics’. Trending Topics on Twitter shows the list of top 10 trending topics but each topic consists of short phrase or keyword, which does not contain any explanation of those meanings. It is almost impossible to identify what a trending topic is about unless you read all related tweets. The goal of this paper is finding the most successful method that uses to retrieve the representative contents of trending topics in order to disambiguate the meaning of topics. We first collected the trending topics and tweets related to them. Then, we applied four types of information retrieval approaches (key factor extraction, named entity recognition, topic modelling, and automatic summarization) for extracting the representative contents of trending topics. We conducted human experiments with 20 postgraduate students.
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© 2014 Springer International Publishing Switzerland
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Han, S.C., Chung, H., Kim, D.H., Lee, S., Kang, B.H. (2014). Twitter Trending Topics Meaning Disambiguation. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_11
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DOI: https://doi.org/10.1007/978-3-319-13332-4_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13331-7
Online ISBN: 978-3-319-13332-4
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