Affiliation:
1. Lingnan University, Hong Kong
2. The Chinese University of Hong Kong, Hong Kong
Abstract
Central to algorithmically mediated activism is the politics of algorithmic (in)visibility. Activists must consider how they can increase the visibility of their claims. Bringing together critical data studies and social movement studies, this study introduces the concept of human-automated collectives to capture how activists strategically make collective claims about and through algorithms and mobilize algorithmic tactics on social media. Using Hong Kong’s Anti-Extradition Bill Movement as a case study, this study explores how activists interpreted social media algorithms, mobilized others to use various tactics to amplify their voice to gain algorithmic visibility, and contested counter-movement algorithmic strategies. We primarily drew upon a qualitative analysis of two pro-movement Facebook pages that shared algorithmic tactics published between July 2019 and January 2020 ( n = 694). The study contributes to theorizing the role of activists in decoding and contesting automated media for their practical purposes in connective action.