PLATFORM GOVERNANCE AND ALGORITHMIC ACCOUNTABILITY IN SHAPING PUBLIC DISCOURSE

Arpita Sneh; Dr. Bhavna  Upadhyaya; Prakash Mishra; Sarthak Kumar; Mayank Jain

doi:10.29121/shodhkosh.v7.i7s.2026.7652

Authors

Dr. Arpita Sneh Research Scholar, Veer Bahadur Singh Purvanchal University, Jaunpur, India
Dr. Bhavna Upadhyaya Assistant Professor, Jagran School of Journalism, Jagran Lakecity University, Bhopal, India
Dr. Prakash Mishra Faculty, Makhanlal Chaturvedi National University of Journalism and Communication, Bhopal, India
Sarthak Kumar Assistant Professor, Mody University of Science and Technology, Lakshmangarh, India
Mayank Jain Assistant Professor, Mangalayatan University, Aligarh, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i7s.2026.7652

Keywords:

Platform Governance, Algorithmic Accountability, Public Discourse, Digital Public Sphere, Content Moderation, Media Literacy, Platform Society

Abstract [English]

The rise of algorithm-driven digital platforms has changed how public discussions happen, are mediated, and contested. This study looks at how platform governance structures and accountability practices affect the quality, diversity, and inclusivity of public discourse on major social media platforms. Using a mixed-method approach, which combines a structured survey (N = 412) with an analysis of platform transparency reports, this study applies Habermas's Public Sphere Theory and Van Dijck's Platform Society framework. It examines the conflicts between the principles of commercial platforms and democratic communication ideals. Statistical tests, including Cronbach's Alpha reliability testing (α = 0.84), confirmatory factor analysis, chi-square tests, and independent-sample t-tests, show significant differences in how various demographic groups perceive algorithmic fairness (p < 0.001). Notably, users with lower digital literacy scores have much less trust in platform governance mechanisms (t = 4.67, p < 0.001). Additionally, content moderation practices are viewed as unfairly targeting marginalised communities (χ² = 24.31, df = 4, p < 0.001). These results challenge the common belief that algorithmic neutrality can exist within profit-driven platforms. This paper adds to the discussion on algorithmic accountability by suggesting a Governance-Discourse Alignment Index (GDAI) as both a theoretical and practical tool for assessing how well platforms follow democratic communication standards. The policy implications include required algorithmic impact assessments, independent oversight, and government-supported media literacy programs. The study also addresses its limitations and suggests directions for future research across different platforms and over longer periods.

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