SBIR Phase I: Identifying and Countering Misinformation on Closed Messaging Platforms (COVID-19)

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to advance the state-of-the-art for detecting, prioritizing, and responding to misinformation online. As more platforms are moving to end-to-end encryption, it is critical that new tools and algorithms are developed to respond to misinformation on these platforms. Encryption on platforms such as WhatsApp, Viber, LINE, Telegram, and Signal protects the communications of millions of Americans but also potentially allows rumors, misinformation, disinformation, and other threats to spread. This project builds ‘misinformation tiplines’: online accounts that allow users to check potential misinformation with leading fact-checking organizations. The proposed algorithms will allow Americans to check potential misinformation with fact-checking organizations allowing them to identify dangerous—and for health misinformation sometimes life-threatening—misinformation online. The project empowers users of online communication platforms and improves their safety while maintaining the benefits of end-to-end encryption. This Small Business Innovation Research (SBIR) Phase I project will overcome key challenges in order to allow tiplines to scale efficiently to millions of users. Tiplines require a thoughtful combination of artificial intelligence and human knowledge. The same misleading claims are often repeated and restated in many different ways online. In order for tiplines to scale, it is necessary to identify the claims being made in messages submitted to tiplines and match them against the extensive library of existing fact-checks. Claims can be in the form of text, image, video, or audio messages and expressed in many different human languages. In this project the company is building the algorithms needed to match these claims across languages and across formats. The results break new ground in natural language processing, computer vision, and machine learning. 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.