Research on Microblog Public Opinion Event Management Based on Logit Regression
- DOI
- 10.2991/978-94-6463-016-9_34How to use a DOI?
- Keywords
- Public opinion; government supervision; Logit
- Abstract
This paper is based on the data of social public opinion events that occurred on Weibo within 24 h of a certain day in 2017. Through the establishment of Logit model to explore the influencing factors that affect the popularity of online public opinion. The empirical results show that the number of fans and likes has a significant positive effect on the popularity of online public opinion. When the number of fans of a platform that publishes social public opinion events increases, the social public opinion events released by the platform are easier to warn. The more likes, the more netizens pay attention to the social public opinion event, and the content of the event can attract enough attention, and the public is more interested in the event. Appropriate control of the number of fans and likes on large-scale Weibo platforms has important implications for government supervision and protection of the healthy development of online public opinion.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shiyuan Zhang AU - Baiping Chen AU - Yajuan Deng AU - Xiaohong Deng PY - 2022 DA - 2022/12/07 TI - Research on Microblog Public Opinion Event Management Based on Logit Regression BT - Proceedings of the 2022 2nd International Conference on Public Management and Intelligent Society (PMIS 2022) PB - Atlantis Press SP - 323 EP - 332 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-016-9_34 DO - 10.2991/978-94-6463-016-9_34 ID - Zhang2022 ER -