SBIR Phase II: A Controversy Detection Signal for Finance

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.