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Trending topics rank prediction


Han, SC and Chung, H and Kang, BH, Trending topics rank prediction, Web Information Systems Engineering (WISE 2015) Part II, 1-3 November 2015, Miami, FL, pp. 316-323. ISBN 9783319261867 (2015) [Refereed Conference Paper]

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Copyright Statement

Copyright 2015 Springer International Publishing

Official URL: 29

DOI: doi:10.1007/978-3-319-26187-4_29


Many web services, such as Twitter and Google, provide a list of their most popular terms, called a trending topics list, in descending order of popularity ranking. 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 analyses the nature of trending topics and proposes a temporal modelling framework for predicting rank change of trending topics using historical rank data. Historical rank data show that almost 70 % of trending topics tend to disappear and reappear later. Therefore it is important to reflect this phenomenon in the prediction model, which is related to handling missing value and window size. Missing value handling approach was selected by using expectation maximization. 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 machine-learning techniques using the U.S. twitter trending topics collected from 30th June 2012 to 30th June 2014. Our model achieved the highest prediction accuracy (94.01 %) with C4.5 decision tree algorithm.

Item Details

Item Type:Refereed Conference Paper
Keywords:trending topic, temporal prediction, trends prediction
Research Division:Information and Computing Sciences
Research Group:Library and information studies
Research Field:Social and community informatics
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:Chung, H (Mr David Chung)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:106738
Year Published:2015
Deposited By:Information and Communication Technology
Deposited On:2016-02-18
Last Modified:2017-11-13

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