Synthetic Social Alienation: The Role of Algorithm-Driven Content in Shaping Digital Discourse and User Perspectives

This study investigates how algorithm-driven content curation impacts mediated discourse, amplifies ideological echo chambers and alters linguistic structures in online communication. While these platforms promise connectivity, their engagement-driven mechanisms reinforce biases and fragment discourse spaces, leading to Synthetic Social Alienation (SSA). By combining discourse analysis with in-depth interviews, this study examines the algorithmic mediation of language and meaning in digital spaces, revealing how algorithms commodify attention and shape conversational patterns. In this study, four SSA patterns were identified: Algorithmic Manipulation, Digital Alienation, Platform Dependency, and Echo Chamber Effects. A hybrid dataset (180 training, 30 test samples) was used to train classification models. Among four algorithms, Support Vector Machine (SVM) achieved the highest performance (90.0% accuracy, 90.4% F1-score). Sentiment analysis revealed distinct language structures for positive (AUC = 0.994), neutral (AUC = 0.933), and negative (AUC = 0.919) expressions. SHAP and LIME analyses highlighted key features driving model decisions. The findings expose how digital platforms commodify attention and shape user discourse, underscoring the need for ethical algorithm design and regulatory oversight.