Algorithmic Opportunity Structures and the Dynamics of Online Activism: Far-Right Mobilization on Facebook

This paper introduces the concept of algorithmic opportunity structures to explore how the efficacy of online activism is contingent on the interaction between algorithms, activists, and audiences. In particular, I examine how far-right actors have gamed ranking and recommendation algorithms by producing content designed to generate high engagement rates. This tactic attracts algorithmic amplification, increasing their visibility and reach on social media. I consider the case of Britain First, a far-right, anti-Muslim movement that used Facebook to rapidly build the largest audience of any political organization in the United Kingdom. I use digital trace data, time series analysis, and topic modeling to study Britain First’s activity, recruitment, and support on Facebook. I identify dynamic equilibria indicative of algorithmically-mediated feedback loops, highlighting how variation in these processes is largely a function of user engagement. The content of the group’s posts and exogenous events, including elections and terrorist attacks, are also associated with short-term fluctuations in online mobilization. The results suggest that Britain First’s success is attributable to its exploitation of Facebook’s algorithms, demonstrating how technological assemblages designed and controlled by corporations can structure political competition and moderate opportunities for activism.