An Effective Approach for Modelling Time Features for Classifying Bursty Topics on Twitter

Fang, A., Ounis, I. , Macdonald, C. , Habel, P., Xiong, X. and Yu, H. (2018) An Effective Approach for Modelling Time Features for Classifying Bursty Topics on Twitter. In: 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), Torino, Italy, 22-26 Oct 2018, pp. 1547-1550. ISBN 9781450360142 (doi: 10.1145/3269206.3269253)

Abstract

Several previous approaches attempted to predict bursty topics on Twitter. Such approaches have usually reported that the time information (e.g. the topic popularity over time) of hashtag topics contribute the most to the prediction of bursty topics. In this paper, we propose a novel approach to use time features to predict bursty topics on Twitter. We model the popularity of topics as density curves described by the density function of a beta distribution with different parameters. We then propose various approaches to predict/classify the bursty topics by estimating the parameters of topics, using estimators such as Gradient Decent or Likelihood Maximization. In our experiments, we show that the estimated parameters of topics have a positive effect on classifying bursty topics. In particular, our estimators when combined together improve the bursty topic classification by 6.9 in terms of micro F1 compared to a baseline classifier using hashtag content features.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Xiong, Ms Xiaoyu and Macdonald, Professor Craig and Yu, Dr Haitao and Fang, Mr Anjie and Habel, Dr Philip and Ounis, Professor Iadh
• Authors: Fang, A., Ounis, I., Macdonald, C., Habel, P., Xiong, X., and Yu, H.
• College/School: College of Science and Engineering > School of Computing Science; College of Social Sciences > School of Social and Political Sciences > Politics
• ISBN: 9781450360142
• Copyright Holders: Copyright © 2018 Association for Computing Machinery
• First Published: First published in Proceedings of the 27th ACM International Conference on Information and Knowledge Management: 1547-1550
• Publisher Policy: Reproduced in accordance with the publisher copyright policy