How Journalists and Fact-Checkers Verify Claims and Spot Misinformation
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In brief
Journalists and fact-checkers verify claims by tracing them to primary sources, cross-checking multiple independent witnesses or documents, and applying specific digital techniques such as reverse image search and geolocation. Certified fact-checking organizations follow a published code requiring non-partisanship, source transparency and open corrections. The rise of AI-generated images, video and text has made this verification work slower and more contested even as fabricated content becomes cheaper to produce.
Verification is the step between a claim circulating online or from an official and a claim being reported as fact. It is not a single technique but a set of overlapping checks that journalists and dedicated fact-checking organizations apply before publishing.
Starting with primary sources and documents
The basic method taught in newsrooms is to get as close as possible to the original record rather than relying on a secondhand account. According to the Granite State News Collaborative, reporters consult original files, public records, laws and spending documents directly, and request copies of underlying data from authorities rather than accepting a summary.
Where no document exists, such as in reporting on a disputed event, journalists are advised to interview multiple people independently and use cautious language like 'all evidence suggests' rather than asserting certainty. Editors then review names, figures and attributions before publication, and some newsrooms show sensitive sections of a story to sources beforehand to catch errors.
The rules certified fact-checking organizations follow
Organizations that want to be recognized as fact-checkers by the International Fact-Checking Network (IFCN), a unit of the Poynter Institute, must sign a Code of Principles built on five commitments, assessed annually by independent reviewers against 31 criteria.
Non-partisanship and fairness: the same standard is applied to every claim, regardless of who makes it, and staff do not take policy positions.
Transparency of sources: evidence is detailed enough for readers to replicate the check, with primary sources prioritized over secondary ones.
Transparency of funding and organization: any funding source providing 5% or more of revenue must be disclosed, along with editorial structure and staff biographies.
Transparency of methodology: the process used to select, research and publish fact checks is made public.
Open and honest corrections: a published corrections policy is followed, and significant errors are visibly corrected.
Verifying images and video: OSINT techniques
A large share of misinformation spreads through photos and videos taken out of context rather than outright fabrication. Bellingcat, an open-source investigation group, describes checking five things before treating visual content as reliable: whether the image is original or has circulated before, who posted it and on what platform, where it was actually filmed (geolocation), when it was filmed (chronolocation), and why it was shared.
In practice this involves running a file through several reverse-image-search engines such as Google Images, Yandex and TinEye in parallel, since each indexes different parts of the web. Investigators also search distinctive visible details — a sign, a building, a uniform — rather than the whole image, and cross-check visible clues like shadows or landmarks against maps and known timestamps to see if the claimed time and place hold up.
The growing challenge of AI-generated content
Generative AI has changed the economics of this work. Tech Policy Press describes a core asymmetry: producing a convincing fake image, video or document has become cheap and fast, while verifying authenticity remains slow and resource-intensive, and detection tools become outdated every time generative models improve.
The same report notes that confidence scores from AI-detection tools — for example, a tool stating it is 70% confident an image is not AI-generated — do not translate into the yes-or-no judgment that publication requires, and that many such tools were built without input from journalists, limiting their adoption in newsrooms. A separate industry effort, the Coalition for Content Provenance and Authenticity (C2PA), has developed cryptographic labels meant to track content from the moment it is captured, but Tech Policy Press reports that these labels are frequently stripped by editing software, content management systems or social platforms before an image or video even reaches audiences.
Faking things is getting cheaper and quicker. Meanwhile, verifying things can be expensive and slow.
— Tech Policy Press
What readers can check themselves
The same core questions fact-checkers ask — who is the original source, where and when was this really captured, and does it appear anywhere else first — are the ones media literacy guides recommend to the public before sharing a claim. None of the organizations and guides reviewed here suggest a single tool is sufficient; all describe triangulation, cross-checking a claim against several independent primary sources, as the common thread.
Questions
What is the IFCN Code of Principles?
It is a set of five commitments — non-partisanship, transparency of sources, transparency of funding and organization, transparency of methodology, and open corrections — that organizations must follow to be certified as fact-checkers by the International Fact-Checking Network, a unit of the Poynter Institute.
What does 'triangulation' mean in fact-checking?
It means confirming a claim through multiple independent sources — such as documents, named witnesses, and original footage — rather than relying on a single account, a method described by both newsroom verification guides and OSINT groups like Bellingcat.
How do investigators verify a photo or video's location and date?
Bellingcat describes checking whether the content has appeared online before (via reverse image search), identifying who posted it and where, and comparing visible details such as landmarks, signage or shadows against maps and known timestamps to confirm or disprove the claimed location and time.
Can AI detect whether an image or video is AI-generated?
Detection tools exist but have limits: Tech Policy Press reports that confidence scores from such tools often do not give a clear yes-or-no answer, and that detection methods become outdated as generative AI models improve, making this an ongoing challenge rather than a solved problem.
Do fact-checking organizations rate claims the same way?
Organizations certified under the IFCN Code of Principles are required to apply the same evaluation standard to every claim regardless of who made it, and to publish their methodology, though the specific rating scales they use can differ between organizations.
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