Future Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Products
Grill, Gabriel
Social media has both been hailed for enabling social movements and critiqued for its affordances as a
surveillance infrastructure. In this work, I focus on the latter by analyzing research, products, and discourses around the
recent history of civil unrest prediction based on social media data and other public data sources, thereby giving insights
into current and often opaque protest surveillance and forecasting practices. Technologies to monitor individuals and
groups online have been developed for instance to predict US protests following the election of President Trump in 2016
and labor strikes across global supply chains. These works are part of an emerging computer science research field
focused on “civil unrest prediction” dedicated to forecasting protests across the globe (e.g., Indonesia, Brazil, and
Australia). Foremost I focus on scholarly literature as my unit of analysis, but also other artifacts discussing or detailing
applications for companies, organizations or governments are examined. I provide a conceptualization of civil unrest
prediction technology by illustrating data sources, features and methods used, and how prediction and detection are
necessarily entangled. Then I show how various kinds of unrest activity are framed as risks to be fixed or averted for
various actors with differing interests such as the military, law enforcement, and various industries. Finally, I critically
unpack justifications and ascribed benefits of the technology and point to how the perspectives of protestors are almost
completely absent. My analysis shows a critical need for regulation centering activists and workers, and reflection within
academia, particularly in the fields of computer and data science, on the ethics and politics of protest research and
ensuing technological applications.