# Detecting Gang-Involved Escalation on Social Media Using Context

arXiv: https://arxiv.org/abs/1809.03632 · HTML: https://mlsanity.com/paper/1809.03632/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [SafeText: A Benchmark for Exploring Physical Safety in Language Models](https://mlsanity.com/paper/2210.10045/) (2022)
- [Mitigating Covertly Unsafe Text within Natural Language Systems](https://mlsanity.com/paper/2210.09306/) (2022)
- [Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI](https://mlsanity.com/paper/2212.09667/) (2022)
- [Learning production functions for supply chains with graph neural networks](https://mlsanity.com/paper/2407.18772/) (2024)
- [Understanding Lexical Biases when Identifying Gang-related Social Media Communications](https://mlsanity.com/paper/2304.11485/) (2023)

## Abstract (arXiv)

Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user's recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.