Algorithmic Society: Social Control, Surveillance, and Human Freedom in the Digital Age

Dr. Eleanor Whitmore , Department of Digital Humanities and Social Policy, Faculty of Arts and Social Sciences, Aotearoa Centre for Technology and Society, Wellington, New Zealand
Articles | Open Access

Abstract

emergence of algorithmic societies represents a fundamental transformation in the relationship between technology, institutions, and human autonomy. In contemporary digital environments, algorithms increasingly influence how individuals communicate, access information, participate in social processes, and make decisions. This research paper examines the expanding role of algorithmic systems as mechanisms of social control, surveillance, and behavioural regulation while critically analysing their implications for human freedom in the digital age. The study investigates how artificial intelligence (AI), machine learning, social media analytics, and data-driven governance reshape social interactions and public decision-making processes. Drawing from existing research on social media analytics, public health surveillance, sentiment analysis, and computational methods, the paper explores the dual nature of algorithmic systems as both tools for societal improvement and instruments capable of reinforcing control structures.

The research adopts a conceptual review methodology based exclusively on the provided literature, synthesizing studies related to AI-driven social media monitoring, disease surveillance, online public sentiment analysis, and computational language technologies. The analysis identifies that algorithmic systems operate through three interconnected dimensions: data extraction, predictive classification, and behavioural influence. Social media platforms generate large-scale behavioural data that can be processed to identify public opinions, predict social trends, and monitor collective responses during crises. Research on social media-based disease surveillance demonstrates the potential benefits of algorithmic systems in improving public health responses, yet it also reveals concerns regarding privacy, transparency, and possible misuse of personal information (Wang et al., 2023; Terry et al., 2023).

The findings indicate that algorithmic societies create a complex relationship between technological efficiency and individual freedom. While algorithms improve decision-making capacity and enable faster responses to social challenges, they may also introduce invisible forms of surveillance and influence through automated ranking, recommendation systems, and predictive analytics.

 

Keywords

Algorithmic Society, Artificial Intelligence, Digital Surveillance, Social Control, Human Freedom, Social Media Analytics, ; Data Governance, Algorithmic Governance, Predictive Systems

References

“(PDF) Philippine Twitter Sentiments during Covid-19 Pandemic using Multinomial Naïve-Bayes. ” Accessed: Aug.21,2025.[Online].Available: https://www.researchgate.net/publication/343062592_Philippine_Twitter_Sentiments_during_Covid-19_Pandemic_using_Multinomial_Naive-Bayes

“DOH attributes spike in respiratory illnesses to better monitoring vert Philippine News Agency. ” Accessed: Aug.21,2025.[Online].Available:https://www.pna.gov.ph/articles/1215604

“Global Tuberculosis Report 2024. ” Accessed: Aug. 21, 2025.[Online].Available:https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024

A. Wang, R. Dara, S. Yousefinaghani, E. Maier, and S. Sharif, “A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception,” Big Data and Cognitive Computing 2023, Vol. 7, Page 72, vol. 7, no. 2, p. 72, Apr. 2023, doi: 10.3390/BDCC7020072.

K. Terry, F. Yang, Q. Yao, and C. Liu, “The role of social media in public health crises caused by infectious disease: a scoping review,” BMJ Glob Health, vol. 8, no. 12, p. 13515, Dec. 2023, doi: 10.1136/BMJGH-2023-013515.

L. C. Cruz, J. N. Dela Cruz, S. F. Maglangit, M. Magtira, J. M. Imperial, and R. Rodriguez, “Is Twitter an Echo Chamber? Connecting Online Public Sentiments to Actual Results From the 2019 Philippine Midterm Elections,” 2022 International Conference on Asian Language Processing, IALP 2022, pp. 57–62, 2022, doi: 10.1109/IALP57159.2022.9961305.

L. L. Maceda, J. L. Llovido, M. B. Artiaga, and M. B. Abisado, “Classifying Sentiments on Social Media Texts: A GPT-4 Preliminary Study,” ACM International Conference Proceeding Series, pp. 19–24, Dec. 2023, doi: 10.1145/3639233.3639353;CSUBTYPE:STRING.

Lee Jooyoung, Rajtmajer Sarah, Srivatsavaya Eesha, and Wilson Shomir, “Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic,” ACM Transactions on Social Computing, vol. 6, no. 3-4, pp. 1–31, Dec. 2023, doi: 10.1145/3617654.

M. Abisado, A. Trillanes, A. Lacasandile, and A. De La Cruz, “Using Low-Resourced Language in Social Media Platforms Towards Disease Surveillance for Public Health Monitoring using Artificial Intelligence,” ACM International Conference Proceeding Series, pp. 77–85, Oct. 2022, doi: 10.1145/3571513.3571527.

M. Bordier, C. Delavenne, D. T. T. Nguyen, F. L. Goutard, and P. Hendrikx, “One health surveillance: A matrix to evaluate multisectoral collaboration,” Front Vet Sci, vol. 6, Apr. 2019, doi: 10.3389/fvets.2019.00109.

Matthews, D. E. J., Adeyemi, D. S. K., & Prof. Rachel M. Lin. (2023). Adolescent Perspectives on Experiences within the Youth Justice Secure Estate: A Systematic Literature Review. International Journal of Social Sciences, Language and Linguistics, 3(06), 01-07. https://doi.org/10.55640/ijssll-03-06-01

Mehra, D. R., & Kapoor, P. N. (2023). Towards a Regenerative Tourism Model: Embracing Circularity in Delhi’s Visitor Economy. International Journal of Social Sciences, Language and Linguistics, 3(04), 01-06. https://doi.org/10.55640/ijssll-03-04-01

Mokoena, D. T. S., & Rensburg, D. P. van. (2023). Transformations in Peer Feedback for Learning-Oriented Language Assessment. International Journal of Social Sciences, Language and Linguistics, 3(05), 01-05. https://doi.org/10.55640/ijssll-03-05-01

P. Dufter and H. Schütze, “Identifying Elements Essential for BERT's Multilinguality,” EMNLP 2020-2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 4423–4437, 2020, doi: 10.18653/V1/2020.EMNLP-MAIN.358.

R. R. Tobias, R. E. Roxas, and M. Abisado, “Science Mapping of Social Media Analytics in Health Through Artificial Intelligence,” in IEEE Region 10 Annual International Conference, Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 750–755. doi: 10.1109/TENCON54134.2021.9707362.

R. Z. D. Cruz and R. A. O. Dela Cruz, “Management of public healthcare facilities in the Philippines: issues and concerns,” British Journal of Healthcare Management, vol. 25, no. 10, pp. 1–17, Oct. 2019, doi: 10.12968/BJHC.2019.0018.

S. Charlebois and J. Pawa, “Tackling communicable disease surveillance and misinformation in Canada,” CMAJ, vol. 197, no. 24, pp. E694–E695, Jul. 2025, doi: 10.1503/CMAJ.250916.

V. C. F. Pepito et al., “Health workforce issues and recommended practices in the implementation of Universal Health Coverage in the Philippines: a qualitative study,” Hum Resour Health, vol. 23, no. 1, pp. 1–11, Dec. 2025, doi: 10.1186/S12960-025-00988-3/TABLES/2.

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Dr. Eleanor Whitmore. (2026). Algorithmic Society: Social Control, Surveillance, and Human Freedom in the Digital Age. Frontline Social Sciences and History Journal, 6(07), 9–16. Retrieved from https://frontlinejournals.org/journals/index.php/fsshj/article/view/985