# TikTok FYP Distribution
## A High-Confidence Behavioral Model

Cross-platform analysis indicates TikTok’s For You Page relies on a materially different early-stage ranking logic than Instagram or YouTube. Rather than distributing influence across many weak signals, TikTok concentrates early distribution power in a small number of viewing-behavior metrics, with completion behavior dominating initial tests.

This document presents a modeled approximation based on observed outcomes, not TikTok’s internal production logic.

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## Model Scope and Interpretation

The following weights represent **relative explanatory influence** derived from regression and outcome correlation across a large post sample. They are **normalized importance values within our model**, not literal coefficients used by TikTok’s ranking system.

TikTok does not operate a single global “FYP score.” Content is evaluated through **staged testing across small cohorts**, with signal weighting evolving as confidence increases.

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## Observed Signal Influence (Normalized)

Approximate relative influence during early FYP testing:

- Completion behavior: ~40%
- Rewatch behavior: ~30%
- Engagement velocity: ~20%
- Profile actions and downstream interest signals: ~10%

The defining characteristic is **concentration**. No other major platform places comparable early influence on a single behavioral class.

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## How Completion Is Actually Evaluated

Completion is not treated as a smooth, continuous percentage during early distribution.

Observed behavior suggests TikTok evaluates completion in **coarse bands** relative to video length and cohort norms. Crossing a threshold matters more than incremental gains above it, particularly in first-pass testing.

**Implications:**
- A 7-second video that reliably crosses its completion threshold often outperforms a 60-second video with higher absolute watch time.
- Differences between near-complete and fully complete views appear to have diminishing returns early.
- Completion evaluation likely becomes more granular in later expansion phases.

Binary framing is a useful mental model, but the system is best understood as **discretized**, not truly binary.

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## Rewatch and Loop Signals

Rewatch behavior is consistently one of the strongest secondary predictors of expanded distribution.

Observed rewatch patterns include:
- Immediate replay shortly after completion
- Short-term return within the same session
- Saved or bookmarked content re-opened later

Immediate replays appear to carry the strongest signal weight. This explains why **loop-friendly content**, visual satisfaction, and non-linear endings distribute more efficiently than linear narratives.

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## Engagement Velocity and Time Sensitivity

Engagement velocity functions as a **momentum signal**, not a cumulative score.

Early engagement disproportionately affects outcomes. Posts that concentrate interaction early tend to receive longer distribution windows and broader interest-graph testing.

A conceptual proxy is **engagement per impression adjusted for time decay**. The exact decay function is unknown and likely adaptive, but observed behavior indicates strong early compounding.

The takeaway is directional: **early traction matters far more than late accumulation**.

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## Initial Account and Post Testing

New or low-history accounts typically receive an initial distribution test regardless of follower count.

This test is not guaranteed reach, but a consistent seeding pattern is observed, often in the low hundreds of impressions depending on region, category, and timing.

Early tests appear to evaluate:
- Completion consistency
- Rewatch presence
- Engagement shape rather than volume
- Category classification confidence

Accounts that repeatedly underperform on completion tend to see reduced future test sizes. Accounts that outperform tend to receive larger and faster follow-up tests.

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## Viral Expansion Thresholds (Observed Correlations)

Posts that enter sustained viral distribution often show:

- Strong completion behavior across early cohorts
- Meaningful rewatch presence at low view counts
- Engagement that arrives quickly rather than eventually

Once expansion triggers, several structural changes are observed:
- Distribution half-life extends from hours to days
- Testing expands into adjacent interest clusters
- Geographic constraints loosen
- Secondary discovery surfaces activate

These are **probabilistic breakpoints**, not fixed rules.

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## Content Half-Life Patterns

**Non-viral content**
- Majority of impressions in the first several hours
- Rapid taper after early tests
- Minimal long-tail discovery

**Viral content**
- Impressions spread over one to two days
- Secondary peaks as new cohorts are tested
- Ongoing discovery through related content surfaces

The difference is not gradual. It is a **step-change** once expansion confidence is reached.

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## Cross-Platform Context (Directional, Not Formulaic)

Key structural differences:

**TikTok**
- Early emphasis on completion and rewatch behavior
- Minimal reliance on follower count in first-pass testing
- High momentum sensitivity

**Instagram Reels**
- Greater emphasis on continuous watch-time ratios
- Higher weight on saves and external shares
- Creator history influences distribution earlier

**YouTube Shorts**
- Stronger incorporation of channel authority
- Higher sensitivity to swipe-away behavior
- Persistent performance memory across posts

These are comparative emphases, not weighted formulas.

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## Practical Optimization Implications

Based on observed behavior:

- Opening seconds matter disproportionately. Losses early cannot be recovered.
- Short formats structurally outperform long formats unless long formats are exceptional.
- Loop design materially improves rewatch signals.
- Posting during audience activity improves test cohort quality.
- Early interaction matters, but engagement bait appears to degrade signal quality.

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## Where the Model Breaks Down

Prediction error remains high relative to other platforms.

Primary contributors:
- Continuous platform-level A/B testing
- Hyper-personalized interest graphs
- Category-specific signal weighting
- Regional supply and demand effects

Modeled weights should be treated as **directional ranges**, not constants.

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## Methodology Summary

- Sample: ~890K TikTok posts
- Window: Jan 2024 – Dec 2025
- Accounts: 400+ creators across multiple follower tiers
- Metrics: views, completion behavior, rewatch patterns, engagement timing
- Controls: posting time, category, account size

Regression and outcome correlation analysis used. Statistical significance achieved. Confidence intervals available in the full dataset.

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## Open Questions

Consistently observed but not yet isolated:
- Detection and penalization of artificial completion
- Interest-graph containment versus expansion triggers
- Long-term impact of negative feedback signals
- Role of sentiment versus volume in comments

These remain active research areas.