Modeling Persuasion in Social Media: A Theoretical Approach to Algorithmic Content Distribution and Manipulation - Terenzi, Massimo (Journal of Sociocybernetics, 2024)
This paper introduces a theoretical model that integrates insights from sociocybernetics and persuasive technology to deepen the understanding of content dissemination and manipulation within social media platforms. Algorithms, while central to curating content and guiding user engagement, are vulnerable to exploitation by external actors with differing objectives. The study examines how recommendation algorithms operate as active agents in communication processes, rather than mere facilitators, and how these systems can unintentionally amplify manipulative strategies. A case study of coordinated cryptocurrency-related activities on Facebook and Telegram is used to demonstrate how such manipulative efforts can disrupt the communicative functions intended by platform algorithms. The findings contribute to the broader discourse on the systemic dynamics of digital communication and the influence of algorithms on social interactions.