Multilingual Protest

Multilingual protest research focuses on automatically identifying and analyzing protest events across multiple languages using computational methods. Current research heavily utilizes deep learning models, such as transformer-based architectures (e.g., BERT, RoBERTa, XLM-RoBERTa), to classify protest-related text and images from news sources, often employing techniques like fine-tuning pre-trained models on multilingual datasets. This work is significant for enabling large-scale cross-lingual analysis of social movements, improving our understanding of global protest dynamics and potentially informing strategies for monitoring and responding to social unrest.