Future Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Products

dc.contributor.authorGrill, Gabriel
dc.date.accessioned2022-04-13T08:20:48Z
dc.date.available2022-04-13T08:20:48Z
dc.date.issued2021
dc.date.issued2021
dc.description.abstractSocial 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.de
dc.identifier.doi10.1007/s10606-021-09409-0
dc.identifier.pissn1573-7551
dc.identifier.urihttp://dx.doi.org/10.1007/s10606-021-09409-0
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4274
dc.publisherSpringer
dc.relation.ispartofComputer Supported Cooperative Work (CSCW): Vol. 30, No. 0
dc.relation.ispartofseriesComputer Supported Cooperative Work (CSCW)
dc.subjectCivil unrest prediction
dc.subjectLabor strike
dc.subjectPreemption
dc.subjectProtest
dc.subjectRisk assessment
dc.subjectSocial movements
dc.subjectSurveillance
dc.titleFuture Protest Made Risky: Examining Social Media Based Civil Unrest Prediction Research and Productsde
dc.typeText/Journal Article
gi.citation.endPage839
gi.citation.startPage811
gi.citations.count8
gi.citations.elementmichelle corinne liu, Jaime R. Brenes Reyes, Sananda Sahoo, Nick Dyer‐Witheford (2022): Riot Platforms: Protest, Police, Planet, In: Antipode 6(54), doi:10.1111/anti.12861
gi.citations.elementHieu Nguyen, Swapna Gokhale (2022): An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests, In: PeerJ Computer Science, doi:10.7717/peerj-cs.1127
gi.citations.elementÖzgür YILMAZ (2023): SOCIAL MOVEMENTS, SURVEILLANCE AND ARTIFICIAL INTELLIGENCE: ANATOMY OF STRUGGLE IN THE DIGITAL AGE, In: The Journal of Social Science 14(7), doi:10.30520/tjsosci.1322116
gi.citations.elementTereza Østbø Kuldova (2022): Artificial Intelligence, Algorithmic Governance, and the Manufacturing of Suspicion and Risk, In: Compliance-Industrial Complex, doi:10.1007/978-3-031-19224-1_7
gi.citations.elementSolon Bevilacqua, John Edward Neira-Villena, Marcos Valverde (2022): La tecnología al servicio de la vigilancia y de la defensa de la vida, In: Estudios en Seguridad y Defensa 33(17), doi:10.25062/1900-8325.325
gi.citations.elementAndré Felipe Zanella, Diego Madariaga, Sachit Mishra, Orlando E. Martínez-Durive, Zbigniew Smoreda, Marco Fiore (2024): Characterizing, Modeling and Exploiting the Mobile Demand Footprint of Large Public Protests, In: Proceedings of the 2024 ACM on Internet Measurement Conference, doi:10.1145/3646547.3688421
gi.citations.elementJane Im, Ruiyi Wang, Weikun Lyu, Nick Cook, Hana Habib, Lorrie Faith Cranor, Nikola Banovic, Florian Schaub (2023): Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral Advertising, In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3544548.3580773
gi.citations.elementHieu Nguyen, Swapna S. Gokhale (2022): Analyzing extremist social media content: a case study of Proud Boys, In: Social Network Analysis and Mining 1(12), doi:10.1007/s13278-022-00940-6

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