CryptoSocials

Methodology

How CryptoSocials filters crypto attention.

CryptoSocials starts with public X activity, filters for cleaner source quality, then ranks coins by social momentum across multiple timeframes.

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Source layer

Not every post gets treated the same.

The goal is to surface real attention, not raw noise. A curated source list helps reduce obvious bots, spam loops, and generic AI engagement farming.

Algod@AlgodTrading · 199.9K followers

With $ZEC running i think private AI will become a big narrative the next few months

Aur.ron@AxieAur · 92.3K followers

combine $axs and $ron drive all value to ONE token

Rendoshi AI@Rendoshi1 · 21K followers

$ZEC - told you it was the most bullish chart in crypto

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Dirk Crypto Diggy@dirkcryptodiggy · 10.8K followers

$BTC $ETH $SOL $ZEC $DOGE $PEPE $WIF $BONK #crypto #memecoin #100x #airdrop #gem #pump

Signals

How a trend becomes readable.

Mentions show volume. Authors show breadth. Rank movement shows whether attention is accelerating. Market context helps separate social interest from price movement.

MentionsTotal tracked postsAuthorsUnique X accountsRank movementAttention changeMarket contextPrice, volume, cap

Timeframes

Different windows show different signals.

1HBreaking attention
6HShort-term formation
24HDaily conviction
3DMulti-day persistence
7DBroader narrative

Noise control

Built to avoid chasing every mention.

Crypto moves fast, but not every post is a useful signal. CryptoSocials uses multiple checks so trend discovery stays focused.

Curated sourcesTracked accounts are selected to reduce obvious spam and low-quality noise.
Unique authorsBroad participation matters more than one account posting repeatedly.
Asset matchingCashtags, contracts, and known identities are resolved before a coin is ranked.
Cooldown logicAlerts use thresholds and cooldowns so repeated noise is harder to trigger.