Resilience and disempowerment in algorithmic systems

Many social media platforms now employ adaptive recommendation algorithms to present content to users, raising concerns about how these systems reduce user agency. Accordingly, scholars have begun investigating how users develop awareness of these algorithms and how they use their understanding to engage with these systems effectively. Taking a focused experimental, mixed-methods approach, this study investigates the choices users ( N = 263) make when interacting with algorithmically mediated feeds of social media content and their perceptions of these systems. We found that people made more homogeneous selections when interacting with an adaptive algorithm compared to an algorithm that maintained content diversity. In addition, we found themes of resilience, disempowerment, and distress in participants’ experiences with our algorithmically mediated feeds. Findings call attention to the complex interplay between individual-level differences and algorithmic influences on decision-making when engaging with social media content.