How We Work
Honest answers about how we track stories and evaluate sources
The Simple Idea
News happens over time. A story breaks, more sources report it, facts get clarified, sometimes things turn out to be wrong.
We watch this happen continuously, so you can see the full picture instead of a single snapshot.
Our Core Principles
1. Time is truth's filter. Stories that hold up over hours and days are more reliable than breaking reports.
2. Track record matters more than brand. We care about what sources do, not just what they're called.
3. Multiple sources validate claims. One source could be wrong; ten sources rarely are.
4. Behavior tells the truth. Corrections, retractions, and original reporting reveal credibility.
What You Get
See Who Reported First
When a story breaks, we timestamp every article. You can see which source published first and who followed hours or days later. No guessing, just facts.
Track Stories Over Time
Stories evolve. New details emerge. Facts get corrected. We show you the timeline so you can understand how the story developed, not just the latest version.
Find Similar Stories Grouped Together
Instead of reading the same story 50 times from 50 sources, we group similar articles together. You see the story once with all the sources that covered it.
Know Which Sources to Trust
We track how sources behave over time: Do they break stories first? Do they issue corrections? Do they retract false stories? This gives you real data instead of brand names.
See Multiple Perspectives
We monitor sources from different regions, political leanings, and types (newspapers, tech blogs, wire services). You get a broader view, not just one bubble.
How We Track Stories
Continuous Monitoring
We check news sources every 15 minutes, 24/7. When articles are published, we timestamp them precisely. This lets us see who published first and how stories spread over time.
Automatic Grouping
Articles about the same event get grouped together automatically based on content similarity—not just keywords. This prevents pollution where unrelated stories share similar words.
Time-Based Confidence
We adjust confidence based on story maturity:
- ↓ Breaking news (first few hours) gets lower confidence—facts are still emerging
- → Developing stories (hours old, multiple sources) get moderate confidence
- ↑ Confirmed stories (day+ old, many sources) get high confidence
Multi-Source Validation
A story reported by one source is less reliable than one reported by ten independent sources. We track source count and diversity, adjusting confidence accordingly. Even a highly-trusted source publishing alone gets flagged as "unconfirmed" until others verify.
How We Evaluate Sources
Track Record Over Time
We watch how sources behave across hundreds or thousands of articles. Do they break stories first? Do they issue corrections when wrong? Do they retract false claims?
This behavioral data builds a pattern over time. A source with a consistent track record earns trust, regardless of brand recognition.
Multiple Factors Combined
No single metric determines trust. We look at multiple signals:
- • How often they publish original reporting vs. copying others
- • How frequently they need to issue corrections
- • Whether they break stories before competitors
- • If they've published stories that were later proven false
- • How long they've been operating with good behavior
We combine these signals mathematically, but the exact formula is proprietary. The key is: behavior patterns matter more than any single article.
Brand Reputation Still Counts
We're not naive. Established outlets like Reuters or The New York Times have earned their reputation through decades of work. But brand isn't everything—it's one factor among many. A well-behaved newer source can rise; a careless established source can fall.
Detecting False Stories
Pattern Recognition
Stories that are likely false or unimportant follow predictable patterns:
- • Quick abandonment: Multiple sources published it, then stopped covering it within hours
- • Low-tier only: Only unreliable sources covered it; established outlets ignored it
- • Retractions: Some sources that published later retracted or heavily corrected
- • Original removed: The first publisher deleted their article
When we detect these patterns, we flag the story as potentially false or "dead"—worth viewing with extreme skepticism.
What We Don't Do
- ❌ We don't tell you what to think. We show you the information and let you decide.
- ❌ We don't use AI to "detect" truth. We use timestamps, source tracking, and behavioral patterns.
- ❌ We don't pick sides politically. We track sources from all perspectives and let you see them.
- ❌ We don't rank by popularity. A source with a better track record ranks higher, even if it's less famous.
- ❌ We don't censor sources. Even sources with poor track records are shown, just with appropriate warnings.
- ❌ We don't share exact formulas. But we're transparent about the principles behind them.
Our Limitations (Being Honest)
- • Coverage is focused. We monitor around 900 active RSS feeds from major news outlets. We focus on quality over quantity.
- • We can't detect all false information. If multiple sources report the same false story, we'll show consensus—but consensus isn't always truth.
- • Breaking news is messy. Early reports often have errors. We flag this, but you still need to be cautious.
- • New sources need time. Sources with limited publishing history don't have reliable patterns yet.
- • Exclusive scoops get flagged. When a trusted source breaks a story alone, we initially mark it "unconfirmed" until others verify—even if it's true.
- • We're constantly improving. This is a work in progress. We'll get better over time as we collect more data.
Who This Is For
Journalists & Researchers
Track how stories develop, find original sources, verify claims across multiple outlets.
News Readers
See the full context of stories, avoid reading duplicates, understand which sources to trust.
Fact-Checkers
Find original reporting, see contradictions between sources, track corrections and retractions.
Anyone Skeptical of News
Get transparency. See the data. Make your own judgments based on facts, not brands.
Why Not Share Exact Formulas?
Two reasons:
1. Gaming the system. If we publish exact formulas, bad actors can optimize their behavior to appear trustworthy while still misleading readers. Keeping some details private makes manipulation harder.
2. Competitive edge. We've spent significant time developing these methods. While we're committed to transparency about principles, the specific implementation is part of what makes TruthTime unique.
That said, we're exploring open-sourcing our algorithms in the future, once the system is mature and we've figured out how to balance transparency with manipulation-resistance.
Questions or Feedback?
We're committed to being as transparent as possible without enabling manipulation. If you have questions about how we work, we're happy to discuss principles and approach.