Making Sense of Responsibility: A Semio-Ethic Perspective on TikTok’s Algorithmic Pluralism

Abstract

Utilizing the data collected from 40 in-depth interviews, this study explores: How do users perceive social media platforms’ responsibility in designing algorithms? What do users perceive as diverse or similar in the content generated by algorithmic recommendation systems? The analysis discusses and evaluates the tension between (a) how the platform’s algorithm feeds users similar videos that they highly appreciate and, inversely, (b) how the recommendation of similar videos might limit the diversity of content to which the user is exposed. The analysis adopts a semio-ethic framework to understand why algorithmic platforms like TikTok are perceived to be so efficient in promoting an apparent perception of inclusivity while deliberately erasing alterity and promoting universal sameness. Although videos recommended by TikTok might appear to satisfy computational criteria of diversity, the outcome masks the absence of algorithmic pluralism. The algorithm generates socially desirable videos to allow users to feel comfortable in their in-group. In other words, recommended videos perpetuate a digital form of conformism in a conscious attempt to create the illusion of a more plural community. Advancing the study of algorithmic pluralism is therefore crucial to evaluate the extent to which plurality is understood by users, and what assumptions and ethics underpin the cultures that foster algorithmic recommendation design.