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

arXiv: https://arxiv.org/abs/2401.12989 · HTML: https://mlsanity.com/paper/2401.12989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate](https://mlsanity.com/paper/2108.05921/) (2021)
- [A New Task and Dataset on Detecting Attacks on Human Rights Defenders](https://mlsanity.com/paper/2306.17695/) (2023)
- [Detecting Human Rights Violations on Social Media during Russia-Ukraine War](https://mlsanity.com/paper/2306.05370/) (2023)
- [The Gun Violence Database](https://mlsanity.com/paper/1610.01670/) (2016)

## Abstract (arXiv)

Gun violence is a pressing human rights issue that affects nearly every dimension of the social fabric, from healthcare and education to psychology and the economy. Reliable data on firearm events is paramount to developing more effective public policy and emergency responses. However, the lack of comprehensive databases and the risks of in-person surveys prevent human rights organizations from collecting needed data in most countries. Here, we partner with a Brazilian human rights organization to conduct a systematic evaluation of language models to assist with monitoring real-world firearm events from social media data. We propose a fine-tuned BERT-based model trained on Twitter (now X) texts to distinguish gun violence reports from ordinary Portuguese texts. We then incorporate our model into a web application and test it in a live intervention. We study and interview Brazilian analysts who continuously check social media texts to identify new gun violence events. Qualitative assessments show that our solution helped all analysts use their time more efficiently and expanded their search capacities. Quantitative assessments show that the use of our model was associated with analysts having further interactions with online users reporting gun violence. Our findings suggest that human-centered interventions using language models can help support the work of human rights organizations.