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THE SOCIAL MEDIA INTELLIGENCE FRAMEWORK: EARLY SIGNALING OF ALGORITHMIC COORDINATION
Keywords:
algorithmic coordination, revealed preferences, social media intelligence, voting pattern analysis, survey research methodology
Abstract:
This paper introduces a novel framework for competition authorities to detect algorithmic coordination through social media voting pattern analysis, treating coordination allegations as implicit survey questions. Using community validation mechanisms via upvoting behavior, the pilot study demonstrates that coordination allegations achieve 85.2% validation rates among 169 engaged users (p < 0.001), significantly exceeding random engagement patterns. The framework combines survey research methodology with automated screening capabilities, enabling systematic measurement of consumer consensus about coordination concerns through revealed preference mechanisms. Competition authorities can immediately deploy this cost-effective early warning system (estimated EUR 1,000-5,000 vs. EUR 500,000+ for traditional investigations) for proactive digital market enforcement, transforming social media platforms into continuous regulatory surveillance tools.
CITATION:
IEEE format
O. Shedrack Agbebiyi, “The Social Media Intelligence Framework: Early Signaling Of Algorithmic Coordination,” in FINIZ 2025 - The Importance of Business Agility in the Modern Business Environment, Belgrade, Singidunum University, Serbia, 2025, pp. 104-113. doi: 10.15308/finiz-2025-104-113
APA format
Shedrack Agbebiyi, O. (2025). The Social Media Intelligence Framework: Early Signaling Of Algorithmic Coordination. Paper presented at FINIZ 2025 - The Importance of Business Agility in the Modern Business Environment. doi:10.15308/finiz-2025-104-113
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