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Mainstream Value Information Push Strategy on Chinese Aggregation News Platform: Evolution, Modelling and Analysis

Author

Listed:
  • Guohui Song

    (State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China)

  • Yongbin Wang

    (State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China)

Abstract

At present, most news aggregation platforms use personalized recommendation technology to push information in China, which is likely to cause the phenomenon of information cocoons. In order to alleviate the occurrence of this phenomenon, this paper studies the issue of mainstream value information push from different perspectives, which can be used as a supplement for personalized recommendation technology to promote the diffusion of mainstream value information. First, we constructed an evolutionary game model to simulate the game process between news aggregation platforms and users. Through the results of evolutionary analysis, the news platform can be guided at a macro level to formulate mainstream value information push strategies by adjusting model parameters. Second, we conducted research on user behavior, and the results show that different user groups have different demands for mainstream value information. Third, we constructed two models from the perspective of user demands and platform revenue. Experiments show that user sensitivity to mainstream value information σ and platform evaluation factors v a l are important for finding the number of mainstream information pushes on each page. Finally, we investigated the effect of the mainstream value information from Toutiao. The survey results are consistent with the viewpoints presented in this paper.

Suggested Citation

  • Guohui Song & Yongbin Wang, 2021. "Mainstream Value Information Push Strategy on Chinese Aggregation News Platform: Evolution, Modelling and Analysis," Sustainability, MDPI, vol. 13(19), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:11121-:d:651833
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    References listed on IDEAS

    as
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