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Topic detection and tracking on heterogeneous information
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Cheng, Long, Zhang, Huaizhi, Jose , Joemon M., Yu, Haitao, Moshfeghi, Yashar and Triantafillou, Peter (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 ISSN 0925-9902.
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Official URL: http://dx.doi.org/10.1007/s10844-017-0487-y
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: | Journal Article | ||||||||
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Subjects: | H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Social media, Twitter (Firm) , News Web sites, Computer science | ||||||||
Journal or Publication Title: | Journal of Intelligent Information Systems | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 0925-9902 | ||||||||
Official Date: | August 2018 | ||||||||
Dates: |
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Volume: | 51 | ||||||||
Number: | 1 | ||||||||
Page Range: | pp. 115-137 | ||||||||
DOI: | 10.1007/s10844-017-0487-y | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 1 November 2017 | ||||||||
Date of first compliant Open Access: | 2 November 2017 | ||||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council (Great Britain) (ESRC), National Science Foundation (U.S.) (NSF) | ||||||||
Grant number: | EP/L026015 (EPSRC), ES/L011921/1 (ESRC), #61572223 (NSF) | ||||||||
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