The Netherlands witnesses an annual surge in registered protests, occasionally shrouded in violence, exemplified by the Avondklokrellen, farmers’ protests, and the Extinction Rebellion’s actions. Understanding the tipping point at which these demonstrations descend into violence is paramount for societal well-being, empowering authorities to safeguard citizens and maintain order. How can we predict these tipping points? Recent qualitative research reveals the existence of an emotional energy “turning point” known as ‘collective effervescence’, akin to a symphony of applause reverberating through a stadium when a musician takes the stage for an encore. Today’s digital landscape presents an opportunity; street cameras capture protests. The question looms: Can we automatically detect these crucial turning points?

In this project, we tackle the daunting task of automatically detecting turning points in group emotional energy using state-of-the-art computer vision techniques. Our primary objective is to forge a codebook and develop an automated classification system capable of identifying collective effervescence within protests. From a social science vantage point, this breakthrough will enable us to a) discern and decode group emotions—a vital component in understanding collective behaviour, and b) uncover how complex social dynamics can be researched computationally. Meanwhile, from a legal and criminological perspective, we gain crucial insights into the conditions that propel peaceful protests toward violent upheavals. Leveraging the wealth of video material from PI Rosenkrantz Lindegaard‘s collaboration with the NCTV on a previous project, encompassing both peaceful and violent protests spanning from 2020 to 2023, we stand poised to unlock the enigma concealed within societal unrest.

Researchers: