The Role of Social Media Algorithms in Shaping Public Opinion

  • Authors

    • Iris Calderón Centre for Postcolonial Studies, Universidad del Mar, Mexico. Author

    Published 2026-01-05

  • Social Media Algorithms, Public Opinion, Algorithmic Bias, Echo Chambers, Information Diffusion, Computational Social Science

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    I. Calderón, “The Role of Social Media Algorithms in Shaping Public Opinion”, IJIRHT, vol. 1, no. 1, pp. 26–37, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijirht/article/view/73

  • Abstract

    The social media platforms have turned out to be the fields of massive public discussion, distributing information and also inclusive of opinion-making. At the heart of the impact are algorithmic processes that filter, rank, and suggest the content to users per the data on behaviors, measurements of engagement, and predictive modeling. Such algorithms influence a lot what people see, thus influencing opinion, faith, and shared stories. This paper evaluates how social media algorithms form the opinion of the population discussing their mechanism of operation, socio-political effects, and ethical issues. Drawing on a comprehensive literature review, a systematic methodological approach, and discussion of the analysis, this paper has identified the impact of algorithmic personalization, echo chambers, and amplification of misinformation to democratic processes and generally polarizing society. The results highlight the necessity to avoid negative impacts and enhance the advantages of digital interaction through the use of algorithmic transparency, regulatory intervention, and ethical design. The study is a contribution to the interdisciplinary scholarship at the boundary point between computer science, communication studies and social sciences.

  • References

    1. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132.

    2. Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.

    3. Sunstein, C. R. (2017). #Republic: Divided Democracy in the Age of Social Media. Princeton University Press.

    4. Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13, 203–218.

    5. Gillespie, T. (2014). The relevance of algorithms. In Media Technologies: Essays on Communication, Materiality, and Society (pp. 167–194). MIT Press.

    6. Lazer, D. M. J., et al. (2018). The science of fake news. Science, 359(6380), 1094–1096.

    7. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.

    8. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing.

    9. Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.

    10. Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

    11. Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298–320.

    12. Helberger, N., Karppinen, K., & D’Acunto, L. (2018). Exposure diversity as a design principle for recommender systems. Information, Communication & Society, 21(2), 191–207.

    13. Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62.

    14. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. Proceedings of Innovations in Theoretical Computer Science (ITCS).

    15. European Commission High-Level Expert Group on AI. (2019). Ethics Guidelines for Trustworthy AI. European Union.

    16. H. Janardhanan, "A Reinforcement Learning Approach to Cybersecurity: Deep Q-Networks for Threat Modeling," 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS), Prawet, Thailand, 2025, pp. 265-270, doi: 10.1109/ICMLAS64557.2025.10968270.

  • Downloads

    • ga