RESEARCHING ONLINE LABOR STRIKE AND PROTEST PREDICTION TECHNOLOGIES - Grill, Gabriel (AoIR Selected Papers of Internet Research, 2020)
Efforts to surveil social media platforms at scale using big data techniques have recently manifested in government-funded research to predict protests following the election of President Trump. This work is part of a computer science research field focused on online “civil unrest prediction” dedicated to forecasting protests across the globe (e.g. Indonesia, Brazil and Australia). Researchers draw upon established data science techniques such as event detection/prediction, but also specific approaches for surveilling social movements are conceived. Besides furthering the academic knowledge-base on civil unrest and protests, the works in this field envision to support a variety of stakeholders with different interests such as governments, the military, law enforcement, human rights organizations and industries such as insurance and supply chain management. I analyze the recent history of civil unrest prediction on social media platforms through examining discourses, implicated actors and technological affordances as encountered in publications and other public online artifacts. In this paper I discuss different risk frames employed by researchers, concerning politics of the technology and argue for a needed public debate on the role of online protest surveillance in democratic societies.