The Positive and Negative Impacts of Social Media Algorithms on Consumer Behavior and Optimization Strategies
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The Positive and Negative Impacts of Social Media Algorithms on Consumer Behavior and Optimization Strategies

Jiayi Jin 1, Jianrui Liu 2* Haoting Ying 3
1 Macduffie School
2 University of Nottingham
3 Andrews Osborne Academy
*Corresponding author: Biyjl93@nottingham.edu.cn
Published on 5 November 2025
Journal Cover
AEMPS Vol.237
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-507-3
ISBN (Online): 978-1-80590-508-0
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Abstract

The effects of social media algorithms on consumer behavior have received considerable attention, but the strategies to assuage the negative effects of it remain insufficient. This paper analyzes the dual influences of algorithmic mechanisms, which is on the one hand, enhance efficiency meanwhile inducing information cocoons and polarization and on the other hand, cause the influence on psychological and behavioral outcomes. The study reveals that while algorithms improve information matching and reduce decision costs, the system, in the meantime, concurrently diminishes content diversity and increases political polarization rates, alongside pervasive privacy risks. To address these issues, this paper proposes: First, developing explainable recommendation systems as well as real-time user fatigue monitoring to enhance transparency and responsiveness. Second, cultivating algorithmic literacy education as well as active information-seeking behavior for the empowerment of users. These recommendations seek to strike a balance between personalized services and responsible design for the promotion of an equitable digital ecosystem.

Keywords:

Social Media Algorithms, Consumer Behavior, Psychological and Behavioral Outcomes

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Jin,J.;Liu,J.;Ying,H. (2025). The Positive and Negative Impacts of Social Media Algorithms on Consumer Behavior and Optimization Strategies. Advances in Economics, Management and Political Sciences,237,27-32.

References

[1]. Groh, G., & Ehmig, C. (2007, November). Recommendations in taste related domains: collaborative filtering vs. social filtering. In Proceedings of the 2007 ACM international conference on supporting group Work (pp. 127-136).

[2]. Diakopoulos, N., & Koliska, M. (2017). Algorithmic transparency in the news media. Digital journalism, 5(7), 809-828.

[3]. Shin, D. (2020). How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Computers in human behavior, 109, 106344.

[4]. Guo, Y. X., & Zhang, X. (2025). The Impact Mechanism of Algorithmic Transparency on User Trust in Intelligent Recommendation Systems of Internet Platforms. Journal of Computers, 36(3), 335-348.

[5]. Lambrecht, A. & Tucker, C. (2021). The Impact of Recommender Systems on Consumer Decision-Making. Journal of Marketing Research, 58(3), 447-468.

[6]. Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The welfare effects of social media. American economic review, 110(3), 629-676.

[7]. Andersson, S. and de Vries, P. (2023). The filter bubble: how algorithms limit cognitive diversity. Nature Human Behaviour, 7(5), 682-694.

[8]. Garimella, K. et al. (2022). Echo chambers in social media. Proceedings of the ACM Web Science Conference (WebSci), 1-10.

[9]. Mathur, A. et al. (2023). Dark patterns in personalization. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW), 1-25.

[10]. Zhao, Y., Wang, Y., Liu, Y., Cheng, X., Aggarwal, C., & Derr, T. (2023). Feedback Loop and Bias Amplification in Recommender Systems: A Survey. arXiv.

[11]. Yang, X., Wu, X., & Zhang, L. (2025). An Exploratory Study on Information Cocoon in Recommender Systems. Frontiers of Computer Science.

[12]. Sonboli, N., Smith, J. J., Cabral Berenfus, F., Burke, R., & Fiesler, C. (2021, June). Fairness and transparency in recommendation: The users' perspective. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 274-279).

[13]. Romero-Meza, R., & D'Urso, V. (2024). Choice overload and user fatigue in recommender systems: Behavioral evidence and mitigation strategies. Journal of Behavioral Decision Making.

[14]. Ahmed, S., & Rasul, M. (2023). Examining the association between social media fatigue, cognitive ability, narcissism, and misinformation sharing. Scientific Reports, 13, 15416.

References

[1]. Groh, G., & Ehmig, C. (2007, November). Recommendations in taste related domains: collaborative filtering vs. social filtering. In Proceedings of the 2007 ACM international conference on supporting group Work (pp. 127-136).

[2]. Diakopoulos, N., & Koliska, M. (2017). Algorithmic transparency in the news media. Digital journalism, 5(7), 809-828.

[3]. Shin, D. (2020). How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Computers in human behavior, 109, 106344.

[4]. Guo, Y. X., & Zhang, X. (2025). The Impact Mechanism of Algorithmic Transparency on User Trust in Intelligent Recommendation Systems of Internet Platforms. Journal of Computers, 36(3), 335-348.

[5]. Lambrecht, A. & Tucker, C. (2021). The Impact of Recommender Systems on Consumer Decision-Making. Journal of Marketing Research, 58(3), 447-468.

[6]. Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The welfare effects of social media. American economic review, 110(3), 629-676.

[7]. Andersson, S. and de Vries, P. (2023). The filter bubble: how algorithms limit cognitive diversity. Nature Human Behaviour, 7(5), 682-694.

[8]. Garimella, K. et al. (2022). Echo chambers in social media. Proceedings of the ACM Web Science Conference (WebSci), 1-10.

[9]. Mathur, A. et al. (2023). Dark patterns in personalization. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW), 1-25.

[10]. Zhao, Y., Wang, Y., Liu, Y., Cheng, X., Aggarwal, C., & Derr, T. (2023). Feedback Loop and Bias Amplification in Recommender Systems: A Survey. arXiv.

[11]. Yang, X., Wu, X., & Zhang, L. (2025). An Exploratory Study on Information Cocoon in Recommender Systems. Frontiers of Computer Science.

[12]. Sonboli, N., Smith, J. J., Cabral Berenfus, F., Burke, R., & Fiesler, C. (2021, June). Fairness and transparency in recommendation: The users' perspective. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 274-279).

[13]. Romero-Meza, R., & D'Urso, V. (2024). Choice overload and user fatigue in recommender systems: Behavioral evidence and mitigation strategies. Journal of Behavioral Decision Making.

[14]. Ahmed, S., & Rasul, M. (2023). Examining the association between social media fatigue, cognitive ability, narcissism, and misinformation sharing. Scientific Reports, 13, 15416.

Cite this article

Jin,J.;Liu,J.;Ying,H. (2025). The Positive and Negative Impacts of Social Media Algorithms on Consumer Behavior and Optimization Strategies. Advances in Economics, Management and Political Sciences,237,27-32.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Strategic Human Capital Management in the Era of AI

ISBN: 978-1-80590-507-3(Print) / 978-1-80590-508-0(Online)
Editor: Lukáš Vartiak, Anil Nguyen
Conference website: https://www.icftba.org/London.html
Conference date: 4 November 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.237
ISSN: 2754-1169(Print) / 2754-1177(Online)

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