SBIR Phase II: A Controversy Detection Signal for Finance

小企业创新研究（SBIR）第二阶段项目的更广泛影响将来自自动检测争议和虚假信息的技术开发，为金融机构提供降低风险敞口的手段。最近，争议和虚假信息引起了公众的关注和政治关注。该技术在金融行业的应用是依赖人工智能和机器学习的“替代数据”产品日益增长的趋势的一部分，可以节省分析师的时间，并能够更快地对新闻报道和社交媒体做出反应。通过在金融等可量化的应用中展示创新，可以让用户对铺天盖地的在线内容流进行批判性的定量评估，从而产生更深远的社会影响。这一小型企业创新研究（SBIR）第二阶段项目推进了自动检测社交媒体、新闻和其他渠道中争议的新型算法的开发。该项目将使用语言模型和机器学习等方法，将实时争议检测信号应用于金融数据。此外，该项目将：1）基于现有的争议检测技术构建核心能力; 2）构建具有广泛适用性的新颖算法。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。

The broader impact of this Small Business Innovation Research (SBIR) Phase II project will result from development of a technology to automatically detect controversy and disinformation, providing a means for financial institutions to reduce risk exposure. Controversies and disinformation have received public attention and political concern recently. Application of the technology in the finance industry is part of a growing trend toward "alternative data" products relying on artificial intelligence and machine learning, saving analyst time and enabling faster reactions to news stories and social media. Demonstrating the innovation in a quantifiable application, such as finance, is expected to lead to more far-reaching societal impact by enabling users to critically and quantitatively evaluate the often-overwhelming stream of online content.This Small Business Innovation Research (SBIR) Phase II project advances the development of novel algorithms that automatically detect controversy in social media, news, and other outlets. The proposed project will apply a real-time controversy detection signal to financial data, using methods such as language models and machine learning. Additionally, this project will: 1) build core capabilities based on existing controversy detection technology; and 2) construct novel algorithms with broad applicability.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.