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)
|
Text
147900.pdf - Published Version Available under License Creative Commons Attribution. 2MB |
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 Chen, Dr Long and Triantafillou, Professor Peter 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 |
University Staff: Request a correction | Enlighten Editors: Update this record