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Dynamic Topic Detection and Tracking: A Comparison of HDP, C-Word, and Cocitation Methods
Journal article

Dynamic Topic Detection and Tracking: A Comparison of HDP, C-Word, and Cocitation Methods

Wanying Ding and Chaomei Chen
Journal of the Association for Information Science and Technology, v 65(10), pp 2084-2097
01 Oct 2014
DOI: https://doi.org/10.1002/asi.23134

Abstract

Computer Science Computer Science, Information Systems Information Science & Library Science Science & Technology Technology
Cocitation and co-word methods have long been used to detect and track emerging topics in scientific literature, but both have weaknesses. Recently, while many researchers have adopted generative probabilistic models for topic detection and tracking, few have compared generative probabilistic models with traditional cocitation and co-word methods in terms of their overall performance. In this article, we compare the performance of hierarchical Dirichlet process (HDP), a promising generative probabilistic model, with that of the 2 traditional topic detecting and tracking methodscocitation analysis and co-word analysis. We visualize and explore the relationships between topics identified by the 3 methods in hierarchical edge bundling graphs and time flow graphs. Our result shows that HDP is more sensitive and reliable than the other 2 methods in both detecting and tracking emerging topics. Furthermore, we demonstrate the important topics and topic evolution trends in the literature of terrorism research with the HDP method.

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51 citations in Web of Science
69 citations in Scopus

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Web of Science research areas
Computer Science, Information Systems
Information Science & Library Science
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