Misinformation Fingerprints

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Continuous data about provably false narratives spreading online—purpose built for misinformation detection at scale.

NewsGuard’s global team of misinformation analysts maintains the internet’s most complete, machine-readable catalog of top false claims circulating online.

The Misinformation Fingerprints can be used:

  • By AI tools to safeguard against misinformation in responses or retrieval-augmented generation processes.
  • By technology platforms to detect content that may violate their policies against harmful misinformation.
  • By human analysts to understand mis- and disinformation risks, and track emerging narratives before they enter the mainstream.
  • By government agencies and intelligence teams tasked with defending against information operations from malign actors.
  • By corporate communicators to keep abreast of the evolving landscape of brand-related misinformation that could harm client reputation.
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What makes NewsGuard’s Misinformation Fingerprints different from fact-checking:

  • Machine-readable formats enable the Fingerprints to be ingested by large language models
  • Fingerprints are each designated with a “risk of harm” level to contextualize the relative urgency or impact of each false claim
  • Information about where and when a narrative has spread increases understanding of how the false claim is performing
  • A navigable catalog of false narratives across different sectors enables users to surface the claims related to a specific geography, company, global issue, industry, or time frame
  • Fingerprints are all researched and written by trained journalists conducting thorough research to cite provably false or egregiously misleading claims

Purpose-built for use by both human analysts and AI tools, NewsGuard’s Misinformation Fingerprints™ provide a continuously updated view of the digital information environment—and a powerful way to track narratives that are emerging and spreading online.

Data contained in each Fingerprint include:

  • Example language used to advance the false claim
  • Variations of the false claim
  • Detailed debunks citing trustworthy sources
  • An explanation of where and when the narrative emerged
  • Links containing the false claim
  • Related keywords and hashtags
  • A “risk of harm” designation
Insights Post

How NewsGuard’s Misinformation Fingerprints Alert Companies and Governments to the Threats from Falsehoods Spreading Online

NewsGuard’s constantly updated catalog of machine-readable false narratives helps teams in the AI, digital-platform, national defense, and reputation-management sectors stay abreast of emerging falsehoods circulating online to track and mitigate the spread of misinformation. Read on to learn more about the use cases of our Misinformation Fingerprints, and how they can help your team.

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Case Study

How the State Department and US Cyber Command used Misinformation Fingerprints™ to track state-sponsored disinformation

In September 2020, NewsGuard won a contest from the National Security Innovation Network, a joint U.S. State Department and U.S. Department of Defense program seeking solutions that would help the agencies “evaluate disinformation narrative themes in near real time.”

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Case Study

How The Ethos Network integrated Misinformation Fingerprints as fact-checks for social media users

ETHOS was a platform for Gen Z to connect, educate, and share information on the topics that matter to them the most. ETHOS integrated NewsGuard’s Misinformation Fingerprints into its user interface, enabling users to fact check information before they publish, and attach NewsGuard’s Misinformation Fingerprint entries to their posts, increasing trust and transparency in online discourse.

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White Paper

How we operationalized our Misinformation Fingerprints using AI tools

NewsGuard partnered with Blackbird.ai, an artificial intelligence company that monitors online discourse around harmful narratives, to test how NewsGuard’s Fingerprints could be paired with AI to detect misinformation at scale across the web. The test “surfaced a wide range of instances of both hoaxes on Twitter, YouTube, and the open web,” including large pages and accounts that had not been flagged by social media company fact-checkers.

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