Methodology
Index version v1.1
What this measures
Measures the reach-weighted emotional valence of public discourse about AI across a defined platform basket. This is NOT a measure of public opinion: people who post about AI are not a random sample of the population.
How the text is analysed
This snapshot uses TypeSafe Jev (jev-latest). Each text is assessed for stance toward AI, concern category and irony. The published formula, not the language model, calculates the final ranking.
The four concern categories are existential risk, jobs and the economy, deception, and privacy. Texts with no category evidence are not forced into one. Model judgments can be wrong; these are discourse estimates, not calibrated probabilities of harm. History is recomputed using the current scorer and should not be read as a record of past forecasts.
The formula
I_raw(t) = 50 · ( 1 + tanh( κ · [ α·ES(t) + β·VV(t) + γ·MV(t) ] ) ) I(t) = λ · I_raw(t) + (1 − λ) · I(t−Δ) ES = engagement-weighted mean valence, shrunk toward 0 on small samples VV = seasonally-adjusted, robust-z-scored log volume growth MV = outlet-prestige-weighted media alarm, normalised to trailing P95
- alpha_emotional_severity
- 0.5
- beta_volume_velocity
- 0.25
- gamma_media_virality
- 0.25
- kappa_gain
- 1.5
- shrinkage_k
- 200
- author_weight_cap
- 0.005
- min_authors
- 150
Every component is normalised to [−1, 1] before the weighted sum. This matters: without it, the media term sits on a different scale from the others and silently dominates, making the weights decorative.
Where the data comes from
| Source | Class | Weight |
|---|---|---|
| Bluesky | public social | 0.22 |
| Threads | public social | 0.10 |
| Hacker News | public social | 0.08 |
| GDELT | news | 0.20 |
| News RSS | news | 0.15 |
| YouTube | video audio | 0.09 |
| Podcasts | video audio | 0.06 |
| X · curated | curated elite | 0.10 |
| X · search | public social | 0.10 |
These are per-document source coefficients, not caps on a source's final share. Actual sample shares depend on volume, reach and confidence; a source may dominate the collected sample. The source feed shows those weighted shares and links to the original material.
Bands
- 0–24 Extreme Trust
- 25–44 Trust
- 45–55 Neutral
- 56–74 Fear
- 75–100 Extreme Fear
0 is extreme trust and 100 is extreme fear. Note this is inverted relative to the Crypto Fear & Greed Index, which confuses people who know that one.
Known limitations
- Discourse is not opinion. The sample over-represents technical, Anglophone, and highly-online populations.
- Emotion classification accuracy is bounded; sarcasm is the dominant failure mode and biases the index toward under-reporting fear.
- Confidence intervals reflect sampling uncertainty only, not model error.
- The index is not a forecast and has no established predictive validity.
We publish these because the first thing a competent critic does is look for them. Stating the limitations before anyone else does is the only version of this that survives scrutiny.
Data mode: live
When this deployment reports data_mode: synthetic, the underlying documents are generated rather than collected. The formula is the real one — the same code that runs against live data — but the corpus it consumes is invented.
Two consequences worth stating plainly. The numbers describe nothing about any real company, model or person. And the quotes are generated from templates with fictional handles: this platform does not fabricate statements and attribute them to real, named people, and running a demo is not an exemption from that.
Machine-readable
The full definition — parameters, basket, outlet weights, bands — is available on the backend at /v1/meta/methodology. This static website publishes the explanation above; it does not expose the private backend or its API credentials.