أفضل ذكاء اصطناعي لـ Fact-check a claim
Verify a specific factual claim — statistic, quote, news story, scientific finding — against credible sources with traceable citations.
Perplexity
For verifying specific claims against credible sources, Perplexity is purpose-built — every answer ships with inline citations to primary sources. In benchmark testing, 94% of Perplexity Pro's citations accurately supported the specific claims they were attached to. Academic focus mode prioritizes peer-reviewed sources for scientific claims. For health/medical claims specifically, Consensus.app is the specialist — searches 200M+ scientific papers with a "Consensus Meter" showing scientific agreement.
افتح PerplexityIn Perplexity (or Consensus.app for medical claims): Frame the claim as a verification request, not a question: Bad: "Is the Earth heating up?" Good: "Verify or refute this specific claim: 'Global average temperature in 2024 was 1.5°C above pre-industrial baseline.' Cite primary sources (NOAA, NASA, Met Office, IPCC). Note any disagreement between sources." Components of a strong fact-check prompt: - Quote the EXACT claim word-for-word (including any numbers) - Specify what counts as authoritative (government data, peer-reviewed papers, official statements) - Ask for sources that AGREE and DISAGREE - Request the date each source was published - Ask for confidence level (consensus / contested / unclear) Output structure to request: 1. VERDICT: True / Mostly True / Misleading / Mostly False / False / Unverifiable 2. EVIDENCE FOR (with citations) 3. EVIDENCE AGAINST (with citations) 4. CONTEXT MISSING (what the claim doesn't tell you) 5. CONFIDENCE: How sure can I be in this verdict? Always click through 2-3 citations to verify they actually say what's claimed.
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Fact-check: "Studies show that 75% of new businesses fail in the first year." Verdict: Mostly True This is a commonly cited statistic. Many sources reference high failure rates for new businesses, with various studies showing similar numbers. While exact percentages vary, the general claim that most new businesses fail early is supported by data. Sources: 4 (mix of business advice blogs and aggregator articles)
Fact-check: "Studies show that 75% of new businesses fail in the first year." Verdict: FALSE (and the way it's framed misleads even if you correct the number). Evidence against: - US Bureau of Labor Statistics, Business Employment Dynamics (data through 2024): about 20% of new US businesses fail in their FIRST year, not 75%. About 50% fail by year 5. About 65% by year 10. (BLS Table 7, "Survival of private sector establishments by opening year") - The 75% number appears nowhere in BLS data. Tracing the claim's origin: it appears in roughly 2010-onwards business-advice blog posts, often citing each other circularly without primary sourcing. - Statistics Canada (May 2024 report) shows similar 5-year survival pattern: ~50% of businesses founded in 2018 still operating at year 5. What the claim is technically pointing at but misleadingly frames: - It's true that MOST new businesses don't survive a decade. But "most fail in the first year" is wrong by a factor of ~3.5. - The first-year vs five-year framing matters because it changes the practical advice — the first year is statistically the easiest, not the hardest. Missing context: - "Failure" definition varies. BLS counts an "exit" — which includes voluntary closures, sales, and conversions, not just bankruptcy. - Failure rates vary dramatically by industry. Restaurants: 60% by year 3. Tech startups (VC-backed): 90% by year 10 but year 1 is rarely fatal. - Survivorship bias: this stat is typically deployed in pep-talk content; the data shows the actual pattern is more gradual and survivable than the framing suggests. Likely origin: a misreading of a 2009 Small Business Administration report that used a 5-year window, restated as "first year" in a popular blog post around 2012-2013. Confidence: high. BLS data is the gold standard for US business demographics; the 75% claim has no primary source.
Claude
When the claim involves nuanced reasoning rather than just sourcing — Claude's 94% low-hallucination rate via Constitutional AI. Best for "is this argument logically valid?" rather than "is this number correct?" For medical fact-checking specifically, Consensus.app outperforms general LLMs.
افتح Claudeالأسئلة الشائعة
Can I trust AI to fact-check politically charged claims?
Be cautious. AI tools can inherit framing biases from the sources they cite. For politically charged claims, always run the same fact-check through 2 different tools (Perplexity + Grok, or Perplexity + Gemini) and compare which sources each cites. Disagreement on sources is more informative than agreement on conclusions.
What if AI says a claim is "true" but my gut says it's wrong?
Trust your gut and dig deeper. Either (1) the claim is technically true but missing context, (2) the AI cited a source that's been updated, or (3) the original claim was framed in a way that gets a misleading "true" answer. Reframe the question and try again with a more specific phrasing.
How do I fact-check a quote attributed to a famous person?
Quote investigation is one of AI's weakest areas — many famous quotes are misattributed. Specific tools to try: Quote Investigator (humans curate it), Wikiquote (community-vetted), or ask Perplexity "verify this quote attributed to [PERSON] — find the earliest documented source." Misattributed quotes often trace back to a 1990s email forward.