Frameworks: Infrastructure For Political And Social Event Data using Machine Learning

该项目旨在为冲突学者、安全分析师和从业人员带来革命性的计算机化数据提取，他们几十年来投入了大量资源来监测、了解和预测世界各地的武装暴力、社会抗议和其他政治相关事件。目前，绝大多数冲突事件数据都是由人类从越来越多的大量新闻报道中进行昂贵的编码。该项目使用人工智能和大型语言模型的最新进展来解决冲突研究的这一根本问题。它建立在NSF早先的努力基础上，创建了一个可公开使用的大型语言模型，以研究国家间和国家内部的冲突和武装暴力，称为ConfliBERT。该项目将ConfliBERT模型扩展到多语言环境，包括阿拉伯语和西班牙语。这将帮助研究人员和政策制定者更好地了解当地事件的背景，并通过提供当前新闻故事来创建持续的数据分析过程，以实时识别新的政治行为者和事件。随着该项目的网络基础设施的发展，研究界将通过培训、教育以及与包括学者和政府在内的地方、国家和国际团体的接触来增强能力。在过去的五年中，最先进的语言模型已经彻底改变了自然语言处理(NLP)领域。特别是，在使用特定领域的模型来理解社会过程方面取得了重大进展。我们和该领域其他专家的研究表明，ConfliBERT在编码和从原始文本中理解冲突和暴力方面的表现优于以前的模型(Hu，等人)。2022年，哈夫纳等人。2023年)。该项目还支持NLP在冲突研究方面的新发展，并扩大他们接触学术界和政策界的机会。具体地说，它建立在NSF早期的努力基础上，这些努力导致了ConfliBERT的开发，这是一种特定于领域的语言模型，可在拥抱面孔上公开获得，并在专家策划的关于冲突和政治暴力的语料库上进行培训(Hu等人。2022年)。该项目将ConfliBERT和我们的相关创新(例如，用于网络建设的参与者检测)整合、扩展和应用到一个可持续的生态系统中，以从文本中工程数据。它将把ConfliBERT扩展到包括阿拉伯语和西班牙语在内的多语言环境，以可持续的方式更新语料库并不断对ConfliBERT进行再培训，提供新的政治网络数据，并为用户开发语言模型，以创建定制的数据集和应用程序。所有已开发的网络基础设施现在和将来都将继续向研究人员、分析师和其他对冲突动力学、安全研究和国际关系感兴趣的人广泛开放。该项目由NSF高级网络基础设施办公室资助，由社会、行为和经济科学局和STEM教育局联合支持。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。

This project intends to revolutionize computerized data extraction for conflict scholars, security analysts, and practitioners who for decades have devoted significant resources to monitor, understand, and predict armed violence, social protests, and other politically relevant events worldwide. Currently, the vast majority of conflict event data are  expensively coded by humans from increasingly large volumes of news reports. This project uses  recent advances in artificial intelligence and large language models to address this fundamental issue for conflict research. It builds on earlier NSF efforts that created a publicly available large language model to study inter- and intra-state conflict and armed violence, called ConfliBERT. This project expands the ConfliBERT model to multilingual settings, including Arabic and Spanish. This will help researchers and policymakers better understand the context of local events and create a continuous data analysis process by feeding in current news stories to identify new political actors and events in real time. As the project's cyberinfrastructure develops, the research community will be empowered through training, education, and outreach with groups at local, national, and international levels, including academics and government.In the last five years, state-of-the-art language models have revolutionized the field of natural language processing (NLP). In particular, there have been significant advances in the use of domain-specific models for understanding social processes. Our research and that of other experts in this field demonstrate how ConfliBERT outperforms prior  models for coding and understanding conflict and violence from raw text (Hu, et al. 2022, Haffner, et al. 2023). This project  supports new NLP developments for conflict research and expands their access to the academic and policy communities. Specifically, it builds on earlier NSF efforts that led to the development of ConfliBERT, a domain-specific language model, publicly available at Hugging Face, trained on an expert-curated corpus about conflict and political violence (Hu et al. 2022). This project will integrate, extend, and apply ConfliBERT and our related innovations (e.g., actor detection for network construction) into a sustainable ecosystem to engineer data from text. It will expand ConfliBERT to multilingual settings including Arabic and Spanish, update the corpora in sustainable ways and retrain ConfliBERT on a continuous basis, provide new political network data, and develop language models for users to create customized datasets and applications. All developed cyberinfrastructure is and will continue to be broadly accessible for the community of researchers, analysts, and others with interests in conflict dynamics, security studies, and international relations. This project funded by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Social, Behavioral, and Economic Sciences, and the Directorate for STEM Education.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.