Communications in Humanities Research ISSN: 2753-7072
Home> Volumes> Volume CHR Vol.124> Article
Research Article

Research on the Mechanism of Social Platform Algorithms Influencing Public Information Cognition

Yifei Song*
International Department, The High School Affiliated to Beijing Jiaotong University, No.12 Zaojunmiao, Haidian District, Beijing 100081, China, The High School Affiliated to Beijing Jiaotong University

* Corresponding author: [email protected]

Abstract

With the rapid development of social platforms, algorithm recommendation has gradually become an important way for users to obtain online information. When changing the user's information exposure range, it will also have an impact on their understanding and judgment of information. This article takes social platform users as the research object to explore the internal mechanism of algorithmic factors affecting public information cognition. This article verifies the validity of 377 questionnaires through reliability and validity analysis, regression analysis, and mediation effect test. The research results indicate that algorithmic perception has a significant negative impact on information cognitive bias, while algorithmic transparency and algorithmic control significantly affect users' information cognitive bias. Further analysis reveals that algorithmic perception and algorithmic control generate partial mediating effects through platform trust, while algorithmic transparency generates complete mediating effects through platform trust. Research shows that social platform algorithms not only affect the way users receive information, but also influence their information judgment process through their trust in the platform.

Keywords: Social media platforms; Recommendation algorithm; Information cognitive bias
Published: October 27, 2026
DOI: 10.54254/2753-7064/2026.38319
Volume: CHR Vol.124
pp. 99-105
Download PDF

References

  1. Zhu, X. (2022). The impact of the spread of epidemic rumors on audience cognition under the algorithm recommendation.Communication & Education Review, 3(9). DOI: 10.37420/J.CER.2022.078.
  2. Chen, L., Sun, R., Yuan, Y., & Zhan, X. (2024). The influence of recommendation algorithm's information flow on targeted advertising audience's coping behavior.Acta Psychologica, 243, 104168. DOI: 10.1016/j.actpsy.2024.104168.
  3. Ibrahim, H., AlDahoul, N., & Lee, S. (2023). YouTube's recommendation algorithm is left-leaning in the United States.PNAS Nexus, 2(8), pgad264. DOI: 10.1093/pnasnexus/pgad264.
  4. Ognibene, D., Wilkens, R., Taibi, D., et al. (2023). Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion.Frontiers in Artificial Intelligence, 5, 654930. DOI: 10.3389/frai.2022.654930.
  5. Haroon, M., Wojcieszak, M., & Chhabra, A. (2023). Auditing YouTube's recommendation system for ideologically congenial, extreme, and problematic recommendations.Proceedings of the National Academy of Sciences of the United States of America, 120(50), e2213020120. DOI: 10.1073/pnas.2213020120.