Emerging Consensus: Framing Dynamics during the 2019 Hong Kong Anti-Extradition Protests

• In Event: 21066 - Advances in Data and Methods for Collective Behavior/Social Movements Research

Abstract

How do activist groups reach a consensus on their demands? This paper examines how activists collectively develop a shared understanding of key issues such as who to target, what claims to make, and how to adapt to contextual changes. While movement scholars have long been focused on frame alignment, they depend on scholarly hindsight and subjective interpretation to identify frames developed by political actors, on the one hand. On the other, they typically focus on framings initiated by a single organization or a small group of homogeneous organizations. It is unclear how some framings become dominant and how activist groups converge and diverge on certain framings. This paper addresses these limitations with large-scale digital trace data and state-of-art semantic networks and machine learning methods. Using text data extracted from more than 700,000 Telegram posts, I construct semantic networks to capture a diverse range of framings created by different activist groups that change over time during the 2019 Hong Kong Anti-Extradition Protests. By applying kernel Principal Component Analysis (kernelPCA) methods to induced subgraphs of semantic networks, I track the emergence and diffusion of different political framings. While existing literature highlights the hegemony of large organizations (the diffusion model) and the rise of radical groups mediated by mainstream media (the fringe effects model), this project presents an organizational learning model of consensus formation. It demonstrates that consensus emerges as legitimate organizations incorporate the frames of radical groups through communications and positioning. In this process, activist groups go through a series of “tests and trials,” weighing factors such as media attention, opposition to the movement, and their positions within the movement.

Authors

• Weijun Yuan, University of Chicago

• Saber Khani, Boston College