Understanding Lexical Biases when Identifying Gang-related Social Media Communications
This work addresses the challenge of efficiently identifying individuals in need of community support, such as counselors or training programs, from social media data, though it is incremental in applying existing methods to a specific domain.
The study tackled the problem of identifying gang-related social media communications to connect individuals with community care resources, and found that a binary logistic classifier outperformed baseline standards in identifying individuals impacted by gang-related violence using a sample of tweets from Chicago.
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.