Crypto Token Social Signals
A structured view of attention, sentiment, influencer signal, and community health for crypto tokens.
TL;DR
The Social Signals Lens turns aggregated social data into a clear, repeatable snapshot of whether a token has real, improving attention or fading, low-quality noise. It scores five pillars (Influencer & KOL, Sentiment, Community, Social Momentum, Social Volume), applies quality penalties for spam and engagement concentration, and pairs the score with plain-language interpretation to highlight tailwinds and risks.
How to use it (Web UI)
The Social Signals Lens provides a structured workflow: pick a token, generate a report, and read the analysis top-down.
1) Pick a token
Search or paste a contract address and select the chain/network.
2) Run the analysis
Click "Go" to generate a report. The lens pulls aggregated topic snapshots and time-series social metrics and computes the latest signal.
3) Share or revisit
Each run produces a shareable analysis link so you can return later or compare changes over time.
4) Read the report top-down
- Summary: Plain-English view of the current signal, confidence, and bias.
- Social Snapshot: Attention level, share of voice, and recent momentum.
- Sentiment: Current tone and whether it is improving or deteriorating.
- Influencer & KOL Signal: Quality of attention and concentration risk.
- Community Health: Breadth and persistence of participants.
- Signal Interpretation: Which social components are improving, weakening, or mixed.
What the Social Signals Lens does (high level)
The lens uses aggregated social metrics (not post-level scraping) to assess attention quality, then converts that into a structured report.
1) Data ingestion (topic snapshots + time-series)
We ingest best-effort social metrics from sources like LunarCrush, including:
- Interaction volume and activity counts
- Contributor breadth and post creation
- Share of voice (social dominance)
- Sentiment and spam indicators
2) KPI computation & quality gating
We normalize metrics into a consistent schema and score primarily on a 7-day rolling window, then compare that to a 30-day "usual activity" baseline. The lens also checks whether there is enough real activity to treat the signal as meaningful.
If a token is not "alive" (minimum recent activity), the report returns a low-confidence no_social_signal state and treats social scores as unavailable rather than forcing a misleading low score.
3) Scoring across five pillars
Each pillar is scored 0-100 using deviation-from-normal logic, then adjusted by a quality factor that penalizes spam and engagement concentration:
Share-of-voice (social dominance) is still shown in the report for context and trend reading, but it does not currently add a direct deterministic pillar score.
4) Signal interpretation (context, not scored)
We summarize what is improving versus weakening across attention, sentiment, influencer quality, and community breadth. This context explains the score but does not directly change deterministic scoring.
5) How we summarize it (without black box magic)
The report combines deterministic scoring with a concise narrative that explains what is improving, what is weakening, and what could change the outlook.
When coverage is thin (for example, short history or missing inputs), confidence is downgraded so users can separate strong signals from noisy ones.
Disclaimer
The Social Signals Lens uses best-effort data retrieval and automated analysis. Data may be incomplete or delayed, and interpretations can be wrong. This is for educational purposes only and is not financial advice.