使用自然语言处理和定性分析干预帮派暴力：社会工作研究人员和数据科学家之间的合作
Using Natural Language Processing and Qualitative Analysis to Intervene in Gang Violence: A Collaboration Between Social Work Researchers and Data Scientists
摘要：
与其他工业化国家相比，美国与枪支有关的死亡率最高。暴力尤其影响到芝加哥等城市的低收入城市社区，2014年至2015年，枪支暴力增加了40%，枪击受害者超过3，000人。虽然最近的研究发现，城市，帮派参与的个人策划一个独特的和复杂的沟通风格内和社交媒体平台之间，组织专注于减少帮派暴力正在努力跟上日益复杂的社交媒体平台和庞大的数据量。在本文中，描述了数字城市暴力分析方法（DUVVA），这是一种合作定性分析方法，用于数据科学家和社会工作研究人员之间的合作，以开发一套系统，用于解码城市，帮派参与青年的高压语言。我们的方法利用扎根理论的原则时，分析约800条推文张贴的芝加哥帮派成员和参与的青年从芝加哥社区创建一个语言资源的自然语言处理（NLP）方法。在揭示独特的语言和沟通风格的过程中，我们开发了自动化工具，这些工具有可能检测社交媒体上的攻击性语言，并帮助个人和团体进行暴力预防和中断。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
