Analysis of viewpoint evolution based on WeiBo data mining

Sichen Tang and Aili Fang

Applied Mathematics and Computation, 2025, vol. 491, issue C

Abstract: In the era of rapid development of the Internet, in order to reflect the evolution process of users' viewpoints on network relations, a Bayesian viewpoint evolution model based on Weibo data mining is proposed by studying the relationship between the viewpoints of the author and those of the forwarders on the Sina Weibo platform. Firstly, Python crawler technology was used to crawl the comments and forwarding data under the Weibo topic “#ChatGPT father says human-level AI is coming soon”. After data preprocessing and sentiment analysis, the user relationship network diagram was drawn with Gephi software. Secondly, the viewpoint evolution model is constructed and the viewpoint update formula based on Bayes rule is used to calculate the users' viewpoint evolution within the network relations of several kinds of different publication centers. The results show that: in the communication of public opinion, the evolution direction of the opinions of the media-centered network relations tends to be more consistent, which indicates the importance of the opinion guidance of the media in the communication of information. The analysis and technology provide a certain reference for the government and the media to control and guide the network public opinion.

Keywords: Python data mining; Viewpoint evolution; Bayes rule; Network relationships (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:491:y:2025:i:c:s0096300324006738

DOI: 10.1016/j.amc.2024.129212

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