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Predicting the rank of trending topics


Kim, D and Han, SC and Lee, S and Kang, BH, Predicting the rank of trending topics, Proceedings of AI 2016: Advances in Artificial Intelligence, 05-08 December 2016, Hobart, Tasmania, pp. 636-647. ISSN 0302-9743 (2016) [Conference Extract]


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Copyright 2016 Springer

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DOI: doi:10.1007/978-3-319-50127-7_56


Trending topics is the most popular term list in the different web services, such as Twitter and Google. The changes in people's interest in a specific trending topic are reflected in the changes of its popularity rank (up, down, and unchanged). This paper proposes a temporal modelling framework for predicting rank change of trending topics, and delivers the real-time prediction service with only historical rank data. Historical rank data show that almost 70% of trending topics tend to disappear and reappear later. We handled those missing values, using deletion, dummy variable, mean substitution, and expectation maximization. On the other hand, it is necessary to select the optimal window size for the historical rank data. An optimal window size is selected based on the minimum length of topic disappearance in the same topic but with a different context. We examined our approach with four different machine-learning techniques using the twitter trending topics dataset, which is collected for 2 years. As an application, we implemented a trends prediction service, called TrendsForecast, applying our prediction model for Twitter trending topics in 10 different countries.

Item Details

Item Type:Conference Extract
Keywords:trending topic, temporal prediction, trends prediction
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Han, SC (Ms Caren Han)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:113455
Year Published:2016
Deposited By:Engineering
Deposited On:2017-01-03
Last Modified:2019-12-11
Downloads:179 View Download Statistics

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