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

III：小型：协作研究：在大规模社交媒体网络中建模和管理极端主义团体的影响力

• 批准号: 1909252
• 负责人: James Caverlee
• 金额: $ 25万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909252&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Modeling

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.

社交媒体使人们的沟通和消费方式发生了前所未有的积极转变。另一方面，极端主义团体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模型，以了解极端主义团体的规模动态及其影响模式，并建立可指导未来干预措施的综合微观（个人层面）和宏观（团体层面或系统层面）数据驱动模型。该项目使人们更好地了解用户与网上极端主义有关的行为模式和社会动态。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。这个项目可能会导致新的结果，最终影响社会科学，通过新的或完善的网络行为理论，并在令人兴奋的，新颖的数学模型和自然语言处理方法的在线话语。该项目将提高K-12学生和大学生的意识和规范行为，并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长及其在极端组织中的关键作用，该项目侧重于开发建模和管理反规范行为的方法和算法。 我们的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孤立效应。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。 其中一种研究方法是极端主义影响的行为模型，这种模型在个人层面上建立了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载荷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，轻推反规范行为，建立在其他设计和验证新的干预措施，可以通过模拟和离线实验减少极端主义团体的影响。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，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words

• 发表时间: 2020-05
• 期刊: Proceedings of the 8th International Conference on Computing and Artificial Intelligence
• 作者: Prafulla Kumar Choubey;Ruihong Huang

PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge

• DOI: 10.18653/v1/2020.emnlp-main.611
• 发表时间: 2020-10
• 期刊: ArXiv
• 作者: Yun He;Zhuoer Wang;Yin Zhang;Ruihong Huang;James Caverlee

Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition

• DOI: 10.18653/v1/2021.acl-short.93
• 发表时间: 2021
• 作者: Pei Chen;Haibo Ding;J. Araki;Ruihong Huang