Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

User Information Collection of Weibo Network Public Opinion under Python

  • Changhua Liu (Research Institute of Journalism & Communication, Communication University of Zhejiang) ;
  • Yanlin Han (Dept. of Basic Education, Boya Whole-Person Education College, Jiangsu Vocational Institute of Commerce)
  • Received : 2022.04.29
  • Accepted : 2022.12.11
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Although the network environment is gradually improving, the virtual nature of the network is still the same fact, which has brought a great influence on the supervision of Weibo network public opinion dissemination. In order to reduce this influence, the user information of Weibo network public opinion dissemination is studied by using Python technology. Specifically, the 2019 "Ethiopian air crash" event was taken as the research subject, the relevant data were collected by using Python technology, and the data from March 10, 2019 to June 20, 2019 were constructed by using the implicit Dirichlet distribution topic model and the naive Bayes classifier. The Weibo network public opinion user identity graph model under the "Ethiopian air crash" on June 20 found that the public opinion users of ordinary netizens accounted for the highest proportion and were easily influenced by media public opinion users. This influence is not limited to ordinary netizens. Public opinion users have an influence on other types of public opinion users. That is to say, in the network public opinion space of the "Ethiopian air crash," media public opinion users play an important role in the dissemination of network public opinion information. This research can lay a foundation for the classification and identification of user identity information types under different public opinion life cycles. Future research can start from the supervision of public opinion and the type of user identity to improve the scientific management and control of user information dissemination through Weibo network public opinion.

Keywords

References

  1. Q. Zhao and X. Wei, "Analysis of user behavior influence in social network public opinion events: taking "Changchun Changsheng Vaccine Incident" as an example," Journal of Hubei University of Economics (Humanities and Social Sciences Edition), vol. 2020, no. 11, pp. 14-17, 2020.
  2. M. Huang and L. Yuan, "Audience involvement, comment interaction and public opinion: a case study of user comments on NetEase News based on Discourse analysis," Future Communication, vol. 28, no. 2, pp. 69-83, 2021.
  3. L. Jing and H. Su, "Research on the application of government micro-blog from the perspective of holistic governance," Frontier Economy and Culture, vol. 2022, no. 12, pp. 1-3, 2022.
  4. R. Tang, and C. Liu, "Integrity and differentiation of international communication in the context of great changes," Modern Communication, vol. 43, no. 4, pp. 75-79, 2021. https://doi.org/10.1016/j.jvcir.2021.103039
  5. Y. Zhang, X. Han, M. Yang, M. Wang, L. Zhang, P. Ye, and B. Xu, "Distributionally robust unit commitment based on imprecise Dirichlet model," Proceedings of the Chinese Society for Electrical Engineering, vol. 39, no. 17, pp. 5074-5084+5288, 2019.
  6. W. He, H. Xie, and G. Feng, "Review on latent Dirichlet allocation model," Journal of Information Resources Management, vol. 8, no. 1, pp. 55-64, 2018.
  7. H. J. Cui, R. Zhao, M. Q. Zhu, and X. Li, "Travel attributes analysis of passengers based on Naive Bayes classifier," Science Technology and Engineering, vol. 20, no. 11, pp. 4572-4576, 2020.
  8. G. Zhao, J. Lai, Y. Chen, H. Sun, and Y. Zhang, "Residents' travel origin and destination identification method based on naive Bayes classification," Journal of Computer Applications, vol. 40, no. 1, pp. 36-42, 2020.
  9. C. Li, "Design and analysis of web data crawler based on Python," China Computer & Communication, vol. 32, no. 24, pp. 130-132, 2020.
  10. P. Sun and G. Lv, "On the collection and analysis of COVID-19 data by using Python," Journal of Hebei Institute of Architecture and Civil Engineering, vol. 38, no. 4, pp. 155-160, 2020.