  The U.S. has the highest rate of firearm-related deaths when compared to
other industrialized countries. Violence particularly affects low-income, urban
neighborhoods in cities like Chicago, which saw a 40% increase in firearm
violence from 2014 to 2015 to more than 3,000 shooting victims. While recent
studies have found that urban, gang-involved individuals curate a unique and
complex communication style within and between social media platforms,
organizations focused on reducing gang violence are struggling to keep up with
the growing complexity of social media platforms and the sheer volume of data
they present. In this paper, describe the Digital Urban Violence Analysis
Approach (DUVVA), a collaborative qualitative analysis method used in a
collaboration between data scientists and social work researchers to develop a
suite of systems for decoding the high- stress language of urban, gang-involved
youth. Our approach leverages principles of grounded theory when analyzing
approximately 800 tweets posted by Chicago gang members and participation of
youth from Chicago neighborhoods to create a language resource for natural
language processing (NLP) methods. In uncovering the unique language and
communication style, we developed automated tools with the potential to detect
aggressive language on social media and aid individuals and groups in
performing violence prevention and interruption.
