🔌 Adapter Integration

Adapters do not interpret content, sentiment, ideology, or topics. Their job is to extract structural signals that map cleanly onto the five MSM axes.

🧱 Role of Adapters in the MSM Ecosystem#

Adapters serve three core functions:

• Translation — convert raw platform signals into MSM‑aligned primitives
• Normalization — ensure all values fall within [0.0, 1.0]
• Contextualization — provide optional metadata that refines invariant and mode evaluation

The Analyzer expects a consistent input shape regardless of the platform or data source.

📐 Required Output: MediaVector#

Every adapter must produce a normalized MediaVector:

{
  S: number,  // Signal Integrity
  D: number,  // Distribution Topology
  A: number,  // Attention Dynamics
  N: number,  // Narrative Coherence
  T: number   // Temporal Cadence
}

Each axis must be in the range:

0.0 = minimum expression
1.0 = maximum expression

Adapters may compute these values using any platform‑appropriate method, as long as the mapping is consistent.

🧩 Optional Output: Metadata#

Adapters may also provide metadata that helps the Analyzer refine its interpretation:

• Volatility indicators (attention spikes, churn signatures)
• Narrative conflict markers (semantic divergence, contradiction density)
• Cadence hints (posting frequency, cycle compression)
• Signal quality markers (noise ratio, distortion, missing data)
• Distribution structure hints (cluster maps, centrality, fragmentation)

Metadata is optional but improves accuracy, especially in mode and transition detection.

🛰 Mapping External Signals to MSM Axes#

Adapters must translate platform‑specific signals into the five MSM axes. Examples:

Signal Integrity (S)#

• Noise ratio
• Verification density
• Redundancy and cross‑validation
• Data completeness

Distribution Topology (D)#

• Network centrality
• Fragmentation index
• Cross‑cluster connectivity
• Broadcast vs networked flow

Attention Dynamics (A)#

• Engagement volatility
• Spike frequency
• Saturation and burnout patterns
• Temporal clustering

Narrative Coherence (N)#

• Semantic similarity
• Topic alignment
• Conflict markers
• Narrative half‑life

Temporal Cadence (T)#

• Posting frequency
• Cycle acceleration
• Compression of update intervals
• Burstiness

Adapters may use any computational method—statistical, graph‑based, semantic, or heuristic—as long as the mapping is consistent.

🧭 Normalization Requirements#

All values must be normalized to [0.0, 1.0].
Normalization ensures:

• Cross‑platform comparability
• Stable invariant evaluation
• Consistent drift measurement
• Reliable transition detection

Adapters may use min‑max scaling, logistic transforms, or domain‑specific normalization.

🔄 How the Analyzer Uses Adapter Output#

Once the adapter produces a MediaVector (and optional metadata), the Analyzer:

1. Validates and normalizes the vector
2. Computes invariant strain
3. Classifies basin membership
4. Determines behavioral mode
5. Measures drift
6. Detects transitions

The adapter’s job ends once the vector is produced.
The Analyzer handles all structural interpretation.

📦 Example Adapter Output#

{
  S: 0.62,
  D: 0.48,
  A: 0.71,
  N: 0.39,
  T: 0.83,
  metadata: {
    volatility: 0.77,
    narrativeConflict: 0.52,
    cadenceAcceleration: 0.81
  }
}

The Analyzer will use the vector directly and incorporate metadata where relevant.

🧬 Adapter Philosophy#

Adapters should be:

• Minimal — only extract what is structurally necessary
• Consistent — use stable mappings across time
• Transparent — document how each axis is computed
• Platform‑agnostic — avoid assumptions about content or ideology

The MSM Analyzer is designed to work with any media environment as long as the adapter respects these principles.