III: Small: Collaborative Research: Modeling and Managing Extremist Group Influence in Massive Social Media Networks

社交媒体使人们交流和消费内容的方式发生了前所未有的积极变化。另一方面，极端组织可以利用社交媒体传播负面/暴力意识形态。该项目旨在建立数学和数据驱动的模型，以了解极端主义团体在规模上的动态及其影响的模式，并综合微观(个人一级)和宏观(集团或系统一级)数据驱动的模型，以指导未来的干预行动。该项目提供了对用户与网络极端主义相关的行为模式和社会动态的更多了解。这将与一套技术解决方案相结合，以检测和应对自我孤立和反社会行为的发作。这个项目可能会带来最终影响社会科学的新结果，通过新的或改进的网络行为理论，以及令人兴奋的、新颖的数学模型和在线话语的自然语言处理方法。该项目将提高K-12学生和大学生的认识和规范行为，并将对学生进行数学和计算机科学方面的培训和教育。鉴于社交网络使用的异常增加以及它在极端组织中的关键作用，该项目专注于开发建模和管理反规范行为的方法和算法。其目标是减轻负面行为对用户社交互动的影响，同时管理社交网络可能鼓励的意识形态自我隔离效应。这些方法将涉及宏观和微观层面的统计行为模型，并结合创新的自然语言处理和深度学习方法。需要研究的一种方法是极端主义影响的行为模型，它在个人层面上创建数学上严格的社会资本行为模型，可以捕捉模仿行为。第二种方法是数据驱动的话语层面的传染性，用于识别代表意识形态有效负载的极端主义话语结构，并揭示极端主义意识形态传染性的总体模式。第三种方法，轻推反正常行为，建立在其他方法的基础上，通过模拟和离线实验来设计和验证新的干预措施，以减轻极端组织影响的影响。实验结果、数据集和项目软件将通过公共项目网站和代码共享平台(例如GitHub)向研究社区开放。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Social media enables an unprecedented positive shift in how people communicate and consume content. In the other direction, extremist groups can embrace social media to spread negative/violent ideologies. This project aims to build mathematical and data-driven models to understand the dynamics of extremist groups at scale, the patterns of their influence, and integrated micro (individual-level) and macro (group-level or system-level) data-driven models that can guide future interventions. This project provides a greater understanding of users' behavioral patterns and social dynamics related to online extremism. This will be coupled with a set of technical solutions to detect and counter episodes of self-isolation and anti-social behavior. This project may lead to new results eventually impacting social sciences, by means of new or refined cyber-behavior theories, and in exciting, novel mathematical models and natural language processing methods for online discourse. This project will promote awareness and normative behavior to both K-12 students and college students, and will train and educate students in mathematics and computer science.Given the exceptional increase in social network use and the critical role it is now playing among extremist groups, this project focuses on developing methods and algorithms for modeling and managing anti-normative behaviors. The goal is to mitigate the effects of negative behavior on user social interactions while simultaneously managing the ideological self-isolating effect social networks can encourage. The approaches will involve both macro and micro level statistical behavior models incorporated with innovative natural language processing and deep learning methods. One approach to be investigated is behavioral models of extremist influence, which are creating mathematically rigorous behavioral models of social capital at the individual level that can capture imitative behaviors. A second approach is data-driven discourse-level contagiousness, use to identify extremist discourse structures representing ideological payloads, and uncover overall patterns of extremist ideology infectiousness. The third approach, nudging antinormative behaviors, builds on the others to design and validate new interventions that could lessen the impact of extremist group influence through simulation and off-line experiments. Experimental results, datasets, and project software will be made accessible to the research community via public project websites and code sharing platforms (e.g., GitHub).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.