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An opinion leader mining method based on text contents and network features

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Abstract   

Opinion leaders refer to users who provide information to other users in social networks and, at the same time, impact other users' ideological concepts and have an essential influence on the orientation of public social opinion. In the most research literature, opinion leader mining methods mainly include two types of methods based on text contents and network features. However, the above methods still have some problems: text contents and network features are not fully integrated, and the determination of the number of opinion leaders in social networks is subjective. This paper proposes an opinion leader mining method based on text contents and network features to solve the above problems. This method integrates text contents and network features. First, it uses the text content to construct a directed graph of social networks. Secondly, it uses the K-mean clustering algorithm to identify significant communities. Then, it uses network features to measure user leadership. Finally, it uses user coverage to select users with higher user leadership from significant communities to form a set of opinion leaders. The experiment results indicate that the proposed method improve the evaluation indicator compared with the other two opinion leader mining methods. In conclusion, the method proposed in this paper can more accurately and objectively mine the set of opinion leaders in social networks.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 61070015), Guangdong Provincial Key Technology Research Center for Object Digitization and Epidemic Intelligence Prevention and Control, Guangdong Provincial Engineering Technology Research Center for General Colleges and Universities, Guangdong Provincial Department of Education (Grant No. 2022GCZX013), the Intelligent Policing Key Laboratory of Sichuan Province (Grant No. ZNJW2024KFQN012), the Open Research Subject of Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology (Grant No. SDNY-2024001), and the College Student Innovation and Entrepreneurship Training Project of Sichuan Province (Grant No. S202210623042).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Wenhao Huang & Jianqing Xi

  2. School of Computer and Software Engineering, XiHua University, Chengdu, 610039, China

    Yiyao Wang, Zurui Gan, Tiejun Xi, Xiran Xu, Tang Liu & Wenjun Liu

  3. Guangdong University of Foreign Studies South China Business College, Guangzhou, 510545, China

    Wenhao Huang & Deyu Qi

  4. Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology, Chengdu, 610039, China

    Wenjun Liu

Authors
  1. Wenhao Huang
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Contributions

Wenhao Huang, Yiyao Wang, Xiran Xu, Tang Liu, Wenjun Liu: Conceptualization, Methodology. Zurui Gan, Tiejun Xi: Validation, Investigation. Wenhao Huang, Yiyao Wang, Wenjun Liu: Writing – Original Draft. Wenhao Huang, Yiyao Wang, Xiran Xu, Tang Liu, Wenjun Liu: Writing – Review & Editing. Wenhao Huang, Deyu Qi, Wenjun Liu: Funding Acquisition. Wenhao Huang, Wenjun Liu: Supervision. Jianqing Xi: Project Administration. All authors reviewed the manuscript.

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Correspondence to Jianqing Xi, Deyu Qi or Wenjun Liu.

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Huang, W., Wang, Y., Gan, Z. et al. An opinion leader mining method based on text contents and network features. World Wide Web 28, 21 (2025). https://doi.org/10.1007/s11280-025-01331-5

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  • DOI: https://doi.org/10.1007/s11280-025-01331-5

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