Protest event analysis is the most common approach for social movement scholars as it is an unobtrusive and context-sensitive technique that can convert unstructured matter into large volumes of data, in a cross-national, cross-time and cross-issue comparative character. The use of digitized new sources and automated approaches to collect protest events information has accelerated in the 2000s and several very large projects were formed to apply fully or semi-automated methods to collect contentious political event data from news sources, such as GDELT, ICEWS, EMBERS, SPEED, POLCON, and MMAD. In this short presentation, we will describe the potentials and challenges of using artificial intelligence to understand social movements and then present the new bottom-up methodological approach that has been adopted by the GLOCON (Global Contentious Politics Database) Project, which is part of our European Research Council (ERC) funded project Emerging Welfare (emw.ku.edu.tr). We look for the most optimal way of creating a gold standard corpus for training a deep learning system that is designed for automatically collecting protest information in a cross-country context. We show that creating a gold standard corpus for training and testing machine learning models on the basis of randomly chosen news articles from news archives yields better performance than selecting news articles on the basis of keyword filtering, which is the most prevalent method currently used in automated event coding.

Presenters: Dr. Erdem Yörük, Assoc. Prof. of Sociology, Koç University Dr. Çağrı Yoltar, Postdoctoral Researcher, Koç University Dr. Ali Hürriyetoğlu, Postdoctoral Researcher, Koç University Fırat Duruşan, PhD Candidate, Ankara University

Moderator: Dr. Merih Angın, Director of MA-CSSL, Assistant Professor of International Relations, Koç University