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.

翻译：参与帮派活动的个体使用包括Facebook和Twitter在内的主流社交媒体来表达嘲讽、威胁以及哀悼和纪念。然而，通过社交媒体来源识别帮派相关活动的影响以服务社区成员需求，面临一系列独特挑战。这包括如何合乎道德地识别受帮派活动影响的个体的训练数据，以及需要解释这些个体推文中常用的非标准语言风格。我们的研究提供了证据，表明自然语言处理工具可在高效识别可能需要社区关怀资源（如心理咨询师、冲突调解员或学术/职业培训项目）的个体方面发挥辅助作用。我们证明了二元逻辑回归分类器在使用与芝加哥相关的帮派推文样本识别受帮派暴力影响的个体时，优于基线标准。我们最终发现，推文的语言高度相关，“大数据”方法或机器学习模型需要更深入地理解语言如何影响模型性能，以及它如何在人群中进行区分。