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Large language and vision models have transformed how social movementsscholars identify protest and extract key protest attributes from multi-modaldata such as texts, images, and videos. This article documents how wefine-tuned two large pretrained transformer models, including longformer andswin-transformer v2, to infer potential protests in news articles using textualand imagery data. First, the longformer model was fine-tuned using the Dynamicof Collective Action (DoCA) Corpus. We matched the New York Times articles withthe DoCA database to obtain a training dataset for downstream tasks. Second,the swin-transformer v2 models was trained on UCLA-protest imagery data.UCLA-protest project contains labeled imagery data with information such asprotest, violence, and sign. Both fine-tuned models will be available via\url{https://github.com/Joshzyj/llvms4protest}. We release this short technicalreport for social movement scholars who are interested in using LLVMs to inferprotests in textual and imagery data.

LLVMs4Protest: Harnessing the Power of Large Language and Vision Models for Deciphering Protests in the News