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Updated: Sep 6, 2025

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Evolution analysis of online topics based on 'word-topic' coupling network.

Hengmin Zhu1, Li Qian1, Wang Qin2

  • 1School of Management, Nanjing University of Posts and Telecommunications, Nanjing, People's Republic of China.

Scientometrics
|June 27, 2022
PubMed
Summary

This study introduces a micro-perspective approach to analyze topic evolution, revealing topic status and directed paths. The method effectively tracks topic spreading and evolution using a novel word-topic network and topic drifting probability.

Keywords:
Evolutionary pathIntensity evolutionOnline topicsTopic status

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Area of Science:

  • Information Science
  • Computational Social Science

Background:

  • Topic evolution analysis is crucial for understanding information diffusion.
  • Existing methods often focus on topic intensity or literature paths, lacking micro-level detail.

Purpose of the Study:

  • To develop a micro-perspective method for analyzing topic evolution.
  • To capture topic timeline, status, and directed evolutionary paths.
  • To provide a comprehensive understanding of how topics spread and change over time.

Main Methods:

  • Constructing a word co-occurrence network.
  • Utilizing Latent Dirichlet Allocation (LDA) for topic extraction and word-topic mapping.
  • Building a word-topic coupling network to analyze topic intensity and status.
  • Introducing topic drifting probability to identify evolutionary paths.

Main Results:

  • The proposed method effectively describes topic intensity evolution.
  • Topic status is measured by considering feature word contributions.
  • The directed evolutionary paths among topics are identified.
  • Experimental validation on COVID-19 datasets confirms the method's effectiveness.

Conclusions:

  • The micro-perspective topic evolution analysis offers a deeper understanding of information spreading.
  • The novel approach provides insights into topic dynamics, status, and directional changes.
  • This method enhances the monitoring of topic evolution in scientific literature and real-world data.