Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data

Fang, A., Macdonald, C. , Ounis, I. , Habel, P. and Yang, X. (2017) Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data. In: 39th European Conference in Information Retrieval, Aberdeen, Scotland, 8-13 April 2017, pp. 252-265. ISBN 9783319566078 (doi: 10.1007/978-3-319-56608-5_20)

135028.pdf - Accepted Version



There is considerable interest among both researchers and the mass public in understanding the topics of discussion on social media as they occur over time. Scholars have thoroughly analysed sampling-based topic modelling approaches for various text corpora including social media; however, another LDA topic modelling implementation—Variational Bayesian (VB)—has not been well studied, despite its known efficiency and its adaptability to the volume and dynamics of social media data. In this paper, we examine the performance of the VB-based topic modelling approach for producing coherent topics, and further, we extend the VB approach by proposing a novel time-sensitive Variational Bayesian implementation, denoted as TVB. Our newly proposed TVB approach incorporates time so as to increase the quality of the generated topics. Using a Twitter dataset covering 8 events, our empirical results show that the coherence of the topics in our TVB model is improved by the integration of time. In particular, through a user study, we find that our TVB approach generates less mixed topics than state-of-the-art topic modelling approaches. Moreover, our proposed TVB approach can more accurately estimate topical trends, making it particularly suitable to assist end-users in tracking emerging topics on social media.

Item Type:Conference Proceedings
Additional Information:Published in Lecture Notes in Computer Science, vol 10193, pp. 252-265.
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Fang, Mr Anjie and Habel, Dr Philip and Yang, Dr Xiao and Ounis, Professor Iadh
Authors: Fang, A., Macdonald, C., Ounis, I., Habel, P., and Yang, X.
College/School:College of Science and Engineering > School of Computing Science
College of Social Sciences > School of Social and Political Sciences > Politics
Published Online:08 April 2017
Copyright Holders:Copyright © 2017 Springer International Publishing
First Published:First published in Lecture Notes in Computer Science 10193:252-265
Publisher Policy:Reproduced under a Creative Commons License

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