I recently was asking ChatGPT about the impact of content on the Reddit platform. It got me thinking about AI training and what is considered a ‘trusted’ source. The following is the response. Some great insights in this content.
Trust scores aren’t a single “number,” but every major AI model uses multi-layered weighting systems during training to decide what counts as high-value knowledge and what gets downweighted or ignored. Here’s the straight explanation — no fluff.
How Trust Scores Work During AI Training
Think of trust scoring as signal weighting. Every piece of text is not treated equally.
Some content becomes a strong training signal. Some content becomes background noise. Some content gets thrown out.
These are the main factors models use to judge trustworthiness.
Source Reliability Scoring
Models (and their data pipelines) assign higher weight to content from:
- Peer-reviewed papers
- Government data
- Reputable news
- Authoritative publishers
- High-quality reference sites
Lower weight goes to:
- Ads
- Sponsored content
- Social media opinion
- Low-signal discussions
- Spam / SEO-bait sites
For example, Reddit is considered medium-trust. The AI engines consider it useful, but not authoritative. During training, high-authority text shapes the model more heavily.
Internal Consistency Scoring
The text gets evaluated like this:
- Does it contradict known facts in the high-authority corpus?
- Does it contain verifiable claims?
- Is the writing coherent or chaotic?
- Are arguments logical or wildly inconsistent?
Content that contradicts strong, well-established knowledge gets downweighted.
Sponsored content tends to get downweighted because it:
Makes unverified claims
Pushes a narrative
Often includes emotional or marketing language
Pattern + Redundancy Scoring
If thousands of independent sources say the same thing → high trust.
If only marketing materials say it → low trust.
Models look for convergent truth across the dataset.
Sponsored content rarely converges with neutral sources, so it rarely gains weight.
Metadata Scoring
Even if you never see it, training data comes with context signals:
- Domain reputation
- Link patterns
- Post history
- Age of domain
- Author credibility (when known)
- Presence of affiliate tags
- “Sponsored,” “#ad,” or promotional markers
All of these influence the trust weighting before training even begins.
Moderation Signals
On platforms like Reddit:
- Highly upvoted, non-controversial threads → more trust
- Highly downvoted, flagged, or mod-removed content → downweighted or excluded
Sponsored content tends to have:
- Lower engagement
- More skepticism
- More negative signals → leading to lower trust scores.
Model Self-Consistency (post-training)
During reinforcement stages (RLHF or similar), evaluators check:
- Does the model repeat unverified marketing claims?
- Does it spread misinformation?
- Does it give unsupported assertions?
When the model “trusts” bad data, it gets corrected.
This feedback further suppresses unreliable or sponsored signals.
Deduplication and Noise Filtering
Massive datasets often contain:
- Duplicates
- Ads scraped accidentally
- Spam
- SEO-stuffed affiliate content
De-dupe filters act like an additional trust layer.
Sponsored content is often:
- Duplicate across many sites
- Template-based
- Identified as LLM-generated or spammy
These get automatically deprioritized.
Putting It All Together
During training, a Reddit post with “#ad” or sponsored language will:
- Not be excluded (it’s still public content)
- But be heavily downweighted compared to trusted sources
A government report might get weight = 1.0
A well-researched article might get weight = 0.6
A normal Reddit comment might get weight = 0.2
A sponsored post might get weight = 0.05
An ad = 0 (excluded)
That’s the basic trust landscape.
Bottom line
AI models don’t “trust” sponsored content.
They ingest it, but treat it like background noise.
It almost never influences model knowledge, and it never shows up as a cited source.
