  Individuals involved in gang-related activity use mainstream social media
including Facebook and Twitter to express taunts and threats as well as grief
and memorializing. However, identifying the impact of gang-related activity in
order to serve community member needs through social media sources has a unique
set of challenges. This includes the difficulty of ethically identifying
training data of individuals impacted by gang activity and the need to account
for a non-standard language style commonly used in the tweets from these
individuals. Our study provides evidence of methods where natural language
processing tools can be helpful in efficiently identifying individuals who may
be in need of community care resources such as counselors, conflict mediators,
or academic/professional training programs. We demonstrate that our binary
logistic classifier outperforms baseline standards in identifying individuals
impacted by gang-related violence using a sample of gang-related tweets
associated with Chicago. We ultimately found that the language of a tweet is
highly relevant and that uses of ``big data'' methods or machine learning
models need to better understand how language impacts the model's performance
and how it discriminates among populations.
