Topic detection and tracking on heterogeneous information

Long Chen, Huaizhi Zhang, Joemon M. Jose, Haitao Yu, Yashar Moshfeghi, Peter Triantafillou

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
47 Downloads (Pure)


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.
Original languageEnglish
Number of pages23
JournalJournal of Intelligent Information Systems
Early online date19 Sept 2017
Publication statusE-pub ahead of print - 19 Sept 2017


  • topic detection
  • heterogeneous sources
  • temporal dynamics
  • social response
  • topic importance


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