"EBK" : Leveraging Crowd-Sourced Social Media Data to Quantify How Hyperlocal Gang Affiliations Shape Personal Networks and Violence in Chicago's Contemporary Southside

TL;DR

This study uses NLP on social media data to map gang networks in Chicago's Southside, revealing how local affiliations influence violence and victimization risk.

Contribution

It introduces a novel NLP-based method to analyze crowd-sourced social media data for understanding gang dynamics and violence in urban settings.

Findings

Gang network structure correlates with physical proximity.

Individuals connected to homicide victims face higher victimization risk.

Online crowd-sourced data can reveal inaccessible social phenomena.

Abstract

Recent ethnographic research reveals that gang dynamics in Chicago's Southside have evolved with decentralized micro-gang "set" factions and cross-gang interpersonal networks marking the contemporary landscape. However, standard police datasets lack the depth to analyze gang violence with such granularity. To address this, we employed a natural language processing strategy to analyze text from a Chicago gangs message board. By identifying proper nouns, probabilistically linking them to gang sets, and assuming social connections among names mentioned together, we created a social network dataset of 271 individuals across 11 gang sets. Using Louvain community detection, we found that these individuals often connect with gang-affiliated peers from various gang sets that are physically proximal. Hierarchical logistic regression revealed that individuals with ties to homicide victims and central positions in the overall gang network were at increased risk of victimization, regardless of gang affiliation. This research demonstrates that utilizing crowd-sourced information online can enable the study of otherwise inaccessible topics and populations.