How TruthLens Checks Facts
Effective date: 28 July 2026 · Methodology version 1.1
This page explains, in plain language, how TruthLens turns a piece of text, a video, or an image into a verdict backed by sources. It is written for readers, journalists, and researchers who want to know what is actually happening behind a result — not marketing language. Where a claim below can be independently checked in our Terms of Use or Privacy Policy, we link to the exact clause rather than restate it differently.
An analysis tool, not a final verdict. Every result TruthLens produces — verdicts, confidence scores, explanations, source lists — is generated automatically by AI models reasoning over public evidence. It is a research and information tool, not an established fact and not a final verdict (see Terms of Use, clause 6.1). TruthLens is not affiliated with, and is not a verified signatory of, the International Fact-Checking Network (IFCN); the principles on this page are informed by IFCN's published Code of Principles, applied to an automated system.
1. How a check works
A check runs in stages. Every stage is designed around one rule: no verdict without supporting evidence.
1.1. Extraction. The submitted text (or the transcript of a video, or the text read from an image) is broken into individual, checkable factual statements — named events, statistics, and claims presented as fact. Opinions, predictions, and rhetorical questions are not extracted. The overall piece is also classified as news, opinion, or satire.
1.2. Deduplication and prioritization. Repeated or near-identical claims are merged. For long material, the claims judged most consequential to a reader are checked first; a fixed number of the most significant claims per submission are checked in full — this keeps checks fast and keeps the checking budget on what matters, not on incidental detail.
1.3. Matching against published fact-checks. Before running our own search, we check whether a fact-checking organization that is a verified signatory of the IFCN Code of Principles has already published a review of the same claim. A match is accepted only when the matched claim is confirmed — by exact wording or by a dedicated verification step — to be about the same assertion, not merely the same topic. When it matches, that organization's own conclusion is used instead of ours.
1.4. Evidence search. For claims without an existing fact-check, TruthLens searches the open web for supporting or contradicting evidence, excluding social-media posts as primary sources. Claims about scientific or health topics are additionally checked against scholarly and biomedical literature databases (OpenAlex, Semantic Scholar, and PubMed — these index published research broadly, not only work that has completed peer review); claims about a country-level statistic (GDP, inflation, population, unemployment, life expectancy, and similar indicators) are checked against official figures from the World Bank. If a page has been removed or is temporarily unreachable, we attempt to read the last publicly archived copy via the Internet Archive's Wayback Machine before giving up.
1.5. Grounded verdict. An AI model judges the claim strictly against the evidence collected in the previous step — never against its own general knowledge. If the evidence is insufficient or contradictory, the verdict is Unverifiable, not a guess.
1.6. Source-trust weighting. Each evidence source is weighted by trust in a graph of known media and publishers. The model performing the grounded verdict (1.5) is instructed to never call a claim False or Misleading on the strength of low-trust sources alone, and to defer to a high-trust source when a high-trust and a low-trust source disagree. Separately — and enforced in code, not left to the model's judgment — a False or Misleading verdict is not shown with high confidence unless it is backed by at least two independent sources; reprints of the same wire story on different domains count as one.
1.7. Independent second opinion. Most False or Misleading verdicts are separately re-examined by a second AI model given the same evidence, up to a fixed budget per check. If the two disagree, the verdict is kept but shown with reduced confidence rather than presented as certain. A verdict that already matches a published fact-check (1.3) skips this step — a human fact-checking organization's own review already stands behind it.
1.8. Aggregation. When a submission contains several claims, the overall result reflects all of them: a single false statement is not enough to brand an otherwise accurate piece as fake, and a single true statement does not redeem a piece built mainly on false ones.
2. Verdict scale
Verdicts are the same across the app, the browser extension, and this website. Two scales apply: one for the overall submission, one for each individual claim inside it.
Overall result
| Label | Meaning |
|---|---|
| Reliable | Well-supported by independent, trustworthy evidence. |
| Mostly reliable | Supported overall, with minor gaps in coverage. |
| Partially confirmed, leans towards the truth | Only part of the material could be checked; what was checked leans true. |
| Disputed | A minority of claims are false or misleading; the rest hold up. |
| Doubtful | Enough inaccuracy to warrant real caution. |
| Fake | A large enough share of the claims are false or misleading, and confidently so, that the piece as a whole cannot be trusted. |
| Satire | Identified as satire or parody, without a strong false claim of fact underneath it. |
| Unverifiable | Not enough public evidence exists either way. |
Individual claims
| Label | Meaning |
|---|---|
| True | Confirmed by trustworthy, independent evidence. |
| Mostly true | Correct in substance, with a minor caveat. |
| Misleading | Built on a real fact but framed in a way that misleads. See nuances below. |
| False | Contradicted by trustworthy evidence. |
| Unverifiable | No sufficient public evidence was found either way. |
Two claims are the author's own statement about themselves (for example, "I started this channel a month ago"). These are not checked against outside sources at all and are always marked Unverifiable, on principle — no external source could confirm or deny them.
Misleading, more precisely
Where the underlying reason fits one of two well-known patterns, we say so rather than leaving a bare "Misleading" label:
| Nuance | Meaning |
|---|---|
| Real media, false caption | The photo, video, or quote itself is authentic — the caption, context, or framing around it is what misleads. |
| Was true, no longer | The claim was accurate in the past; the underlying situation has since changed. |
3. How we judge a source
Not all evidence is weighted equally. Every source is considered on four criteria: authority (the organization's standing and role on the topic), transparency (open methodology, clear data), recency (freshness relative to the claim being checked), and independence (absence of conflicts of interest). In practice this becomes a three-tier trust level — high, normal, or low — built from a graph of known media outlets and a short list of known low-quality domains (content farms, unmoderated blogs). The model is instructed to treat a contradiction found only on a low-trust source as insufficient, by itself, grounds for a False or Misleading verdict, and to follow the high-trust side when a high-trust and a low-trust source disagree — see 1.6 for the one part of this weighting that is enforced in code rather than left to the model.
4. What TruthLens does not do
- It does not produce a legal finding, a medical or financial recommendation, or an official determination of any kind (Terms of Use, clause 6.4).
- It does not check every claim in a long submission — by design, it prioritizes the most significant ones (see 1.2).
- It depends on evidence that is public, indexed, and available online at the time of the check; claims about purely private matters, or about events with no public trace yet, will typically come back Unverifiable rather than resolved.
- It relies in part on free, third-party infrastructure (open web search, public archives, open scientific and statistical databases). When one of these is temporarily unavailable, TruthLens degrades to the next available source rather than failing outright — but a temporary outage can occasionally mean less evidence than usual for a given check.
- Explanations and free-text output are currently generated in five languages (English, Russian, Chinese, French, Hindi); a submission in another language is still checked, but written back to you in one of these five.
5. Correcting a result
If you believe a specific result is wrong, or a result concerns you or your organization and you consider it incorrect or harmful, email support@truthlens.wiki with the exact claim checked and the substance of your objection. We review reasoned notices within a reasonable time — as a rule, within 14 days — and, where justified, correct or remove the disputed result and take it into account going forward (Terms of Use, clause 6.6).
6. Changes to this methodology
Material changes to how checks are performed will be dated here, in plain language, at the same time they ship.
- Version 1.1 — precision pass on 1.4 (literature databases are not exclusively peer-reviewed; the statistics list is not open-ended), 1.6 (separated the AI-instructed source-trust weighting from the code-enforced independent-sources rule), 1.7 (the second opinion applies to most, not every, False/Misleading verdict, and is skipped for matched fact-checks), and the Fake row in the verdict table (replaced "predominantly" with a description matching the actual threshold).
- Initial publication (version 1.0).