Algorithmic Agency: How Users Subvert or Game Political Content Algorithms on Social Media

Much research frames users as passive subjects to algorithmic curation (Lin & Tsai, 2022; Wang, 2025). But what if users are actively learning to manipulate these systems for political ends—e.g., through coordinated hashtag campaigns, engagement farming, or platform-switching strategies? This research would employ ethnographic interviews, digital trace analysis, and perhaps even participatory action research to document and theorize these user-driven algorithmic hacks. The goal: to reframe agency in platform politics, showing how users (and organized groups) can sometimes outsmart black-box curation for advocacy or disruption. This flips the deterministic technological framework (Paragas & Lin, 2016) and offers a nuanced perspective on the co-evolution of user practices and platform design. The results could reshape debates about algorithmic accountability, political manipulation, and the future of digital activism.

References:

1. Taking stock of social-political polarization in Asia: political communication, social media and digital governance. T. Lin, Chia-hung Tsai (2022). Asian Journal of Communication.
2. Dialogue pathways and narrative analysis in health communication within the social media environment: an empirical study based on user behavior—a case study of China. Xinke Wang, Xinchen Leng (2025). Frontiers in Public Health.
3. Organizing and reframing technological determinism. F. Paragas, T. Lin (2016). New Media & Society.