The Algorithm's Aristocracy

How follower count creates different algorithmic treatment through four mechanisms that multiply together

What Is Algorithmic Aristocracy?

Not all Twitter accounts are treated equally by the algorithm. Two accounts posting identical content with identical quality can experience order-of-magnitude differences in reach—not because of what they say, but because of structural characteristics like follower count, verification status, and follow ratio.

The Key Architectural Choice: Multiplication, Not Addition

The algorithm uses multiplication to combine structural advantages. This single design choice creates exponential scaling where small accounts face compounding disadvantages and large accounts receive compounding benefits.

Small account: 1 + 1 + 1 + 1 = 4
Large account: 100 + 10 + 5 + 2 = 117
Ratio: 29:1 (manageable difference)

Small account: 1 × 1 × 0.001 × 100 = 0.1
Large account: 100 × 1 × 1 × 50,000 = 5,000,000
Ratio: 50,000,000:1 (insurmountable difference)

The result: A small account (500 followers, unverified) and a large account (50,000 followers, verified) posting identical tweets can see a 348:1 difference in reach purely from structural advantages, not content quality.

The Four Mechanisms That Multiply

Four distinct algorithmic mechanisms create advantages that multiply together:

How They Compound: Concrete Example

Account A (small account):
  Verification:   1× (no multiplier)
  TwHIN:          0× (below 16 engagement threshold - no advanced features)
  Follow ratio:   0.001× (following 2× more than followers)
  Base reach:     500 followers

  Calculation: 1 × 0.5 × 0.001 × 500 = ~0.25
  Effective reach: ~575 (with partial OON)

Account B (large account):
  Verification:   100× (Twitter Blue)
  TwHIN:          1× (crossed threshold - full features)
  Follow ratio:   1× (no penalty)
  Base reach:     50,000 followers

  Calculation: 100 × 1 × 1 × 50,000 = 5,000,000
  Effective reach: ~200,000 (after normalization)

Ratio: 348:1 from identical content

The multipliers compound:
• 1 × 0.5 × 0.001 × 500 = 0.25
• 100 × 1 × 1 × 50,000 = 5,000,000
• Gap created purely by multiplication of structural advantages

Why This Matters

• Content quality becomes secondary: Structural advantages outweigh content quality in determining reach
• Winner-take-all dynamics: The algorithm amplifies existing advantages, making it harder for new voices to break through
• Verification as pay-to-win: Twitter Blue ($8/month) provides the 100× multiplier, creating a paid advantage layer
• Most mechanisms are hardcoded: These aren't configuration choices Twitter can easily adjust—they're architectural decisions baked into the code
• Compounding is by design: Multiplication (not addition) is an intentional architectural choice that creates exponential scaling

This analysis is evidence-based: Every mechanism documented with file paths, line numbers, and formulas from Twitter's open-source algorithm.

if (isVerified) 100

Account A (10K followers, unverified):
  TweepCred ≈ 50 (calculated from graph structure)

Account B (10K followers, verified):
  TweepCred ≈ 5,000 (100x multiplier)

Same follower count, 100:1 difference in algorithmic treatment

val MinEngagementCount = 16

if (persistentEmbedding.updatedCount < MinEngagementCount)
  embedding.map(_ => 0.0)  // Zero out if insufficient engagement

Small account (500 followers):
  Average tweet: 8 engagements
  Result: Most tweets never cross threshold

Large account (50K followers):
  Average tweet: 250 engagements
  Result: All tweets cross threshold immediately

val friendsToFollowersRatio = (1.0 + numFollowings) / (1.0 + numFollowers)
val adjustedMass = mass / exp(5.0 × (ratio - 0.6))

object OutOfNetworkScaleFactorParam extends FSBoundedParam[Double](
  name = "out_of_network_scale_factor",
  default = 0.75,
  min = 0.0,
  max = 1.0
)

1K followers account:
  In-network base: 1,000 users (no penalty)
  Out-of-network: ~99% of potential audience (0.75x penalty applies to nearly all growth)

1M followers account:
  In-network base: 1,000,000 users (no penalty)
  Out-of-network: ~95% of potential audience, but base is 1000x larger
  Same penalty (0.75x), different absolute impact