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Gun violence is a pressing and growing human rights issue that affects nearlyevery dimension of the social fabric, from healthcare and education topsychology and the economy. Reliable data on firearm events is paramount todeveloping more effective public policy and emergency responses. However, thelack of comprehensive databases and the risks of in-person surveys preventhuman rights organizations from collecting needed data in most countries. Here,we partner with a Brazilian human rights organization to conduct a systematicevaluation of language models to assist with monitoring real-world firearmevents from social media data. We propose a fine-tuned BERT-based model trainedon Twitter (now X) texts to distinguish gun violence reports from ordinaryPortuguese texts. Our model achieves a high AUC score of 0.97. We thenincorporate our model into a web application and test it in a liveintervention. We study and interview Brazilian analysts who continuouslyfact-check social media texts to identify new gun violence events. Qualitativeassessments show that our solution helped all analysts use their time moreefficiently and expanded their search capacities. Quantitative assessments showthat the use of our model was associated with more analysts' interactions withonline users reporting gun violence. Taken together, our findings suggest thatmodern Natural Language Processing techniques can help support the work ofhuman rights organizations.

Into the crossfire: evaluating the use of a language model to crowdsource gun violence reports