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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响将是推进最先进的检测，优先排序和响应在线错误信息。随着越来越多的平台转向端到端加密，开发新的工具和算法来应对这些平台上的错误信息至关重要。WhatsApp、Viber、LINE、Telegram和Signal等平台上的加密保护了数百万美国人的通信，但也可能导致谣言、错误信息、虚假信息和其他威胁传播。该项目建立了“错误信息热线”：允许用户与领先的事实核查组织一起检查潜在错误信息的在线帐户。拟议的算法将允许美国人与事实核查组织一起检查潜在的错误信息，使他们能够识别有害的-以及健康错误信息，有时危及生命-在线错误信息。该项目为在线通信平台的用户提供支持，并提高其安全性，同时保持端到端加密的优势。这个小型企业创新研究（SBIR）第一阶段项目将克服关键挑战，以使tiplines能够有效地扩展到数百万用户。Tiplines需要人工智能和人类知识的深思熟虑的结合。同样的误导性声明经常在网上以许多不同的方式重复和重述。为了使tiplines规模化，有必要识别提交给tiplines的消息中的声明，并将其与现有事实检查的广泛库进行匹配。声明可以是文本、图像、视频或音频消息的形式，并以许多不同的人类语言表达。在这个项目中，该公司正在构建跨语言和跨格式匹配这些声明所需的算法。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 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.