Topic detection and tracking on heterogeneous information

Chen, L., Zhang, H., Jose, J. M. , Yu, H., Moshfeghi, Y. and Triantafillou, P. (2018) Topic detection and tracking on heterogeneous information. Journal of Intelligent Information Systems, 51(1), pp. 115-137. (doi:10.1007/s10844-017-0487-y)

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Abstract

Given the proliferation of social media and the abundance of news feeds, a substantial amount of real-time content is distributed through disparate sources, which makes it increasingly difficult to glean and distill useful information. Although combining heterogeneous sources for topic detection has gained attention from several research communities, most of them fail to consider the interaction among different sources and their intertwined temporal dynamics. To address this concern, we studied the dynamics of topics from heterogeneous sources by exploiting both their individual properties (including temporal features) and their inter-relationships. We first implemented a heterogeneous topic model that enables topic–topic correspondence between the sources by iteratively updating its topic–word distribution. To capture temporal dynamics, the topics are then correlated with a time-dependent function that can characterise its social response and popularity over time. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on heterogeneous collection. Experimental results demonstrate that our approach can significantly outperform the existing ones.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Triantafillou, Professor Peter and Chen, Dr Long and Moshfeghi, Dr Yashar and Yu, Dr Haitao
Authors: Chen, L., Zhang, H., Jose, J. M., Yu, H., Moshfeghi, Y., and Triantafillou, P.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Journal of Intelligent Information Systems
Publisher:Springer
ISSN:0925-9902
ISSN (Online):1573-7675
Published Online:19 September 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Journal of Intelligent Information Systems 51(1): 115-137
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
643481A Situation-aware information infrastructureDimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/L026015/1COM - COMPUTING SCIENCE
651922Urban Big Data Research CentrePiyushimita ThakuriahEconomic and Social Research Council (ESRC)ES/L011921/1SPS - URBAN STUDIES