Checkr
$checkrdocs

attention is the signal, price is the reaction

attention moves first, price follows

markets move on attention. what starts as a post becomes a chart; culture turns into capital

we don't buy from charts; we buy from feeds. we scroll, we see who's posting, we check if it's legit - then we swap. checkr tracks token attention across farcaster and twitter to spot early market shifts, turning cultural movement into signal

attention was the missing chart.
now it isn't.

key metrics

feeds lead token movement; charts reflect it. the algo blends farcaster and twitter data using quality-weighted methods to produce more reliable metrics.

PRIMARYattention

your token's share of total social attention across all tracked tokens. this is the main signal - a composite metric that combines quality signals with momentum indicators, normalized daily so all tokens sum to 100%.

quality component
weighted blend of engagement, influence, mindshare, and momentum signals
momentum component
growth velocity and RSI-based trend detection
score levels
≥2.0%
dominant
1.0-2.0%
high
0.3-1.0%
medium
<0.3%
low

mindshare

how much of the conversation you own. measures the quality and reach of social posts mentioning your token relative to the entire ecosystem.

engagement
total interactions on posts mentioning your token (likes, reposts, quotes, replies)
reach
audience size of creators posting about your token
activity
volume of qualified posts in the time window

influence

who's actually moving the narrative. measures the impact of individual creators on a token's attention, factoring in their reach and how effectively they drive engagement.

creator attribution
tracks which creators are driving attention for each token with contribution scores
quality filter
bot detection, spam removal, and disambiguation scoring filters noise

velocity

how fast attention is changing. measures acceleration or deceleration in social interest by comparing recent activity to historical baselines. like attention, velocity is normalized across all tokens so you can compare momentum between any two tokens.

momentum levels
rising
accelerating attention
stable
steady state
cooling
decelerating attention

cross-platform blending

twitter and farcaster have vastly different engagement scales. we use precision-weighted share blending to combine them fairly - normalizing each platform separately, then blending based on activity confidence and freshness.

scale-free
larger raw counts on one platform don't dominate; blending uses platform-relative shares
freshness-aware
recent activity weighted higher; stale data decays gracefully

attention ranking

turning social activity into unified attention metrics.

input
I1
social posts
farcaster & twitter
→ raw data
processing
P1
quality filter
bot detection, spam removal
P2
engagement
likes, reposts, reach, activity
P3
normalize
per-platform shares
P4
blend
precision-weighted fusion
P5
assemble
quality + momentum
P6
smooth
ema noise reduction
output
O1
dashboard & api
→ attention %

creator attribution

identifies creators driving token attention.

input
I1
creator posts
token mentions
→ post data
processing
C1
track
activity monitoring
C2
calculate
engagement, reach, frequency
C3
weight
impact combination
C4
filter
quality check
C5
score
attribution
C6
timing
temporal analysis
C7
influence
market impact
C8
rank
sort by impact
output
O1
top creators
→ rankings

api access

programmatic access to attention metrics and creator data.

attention
{
"symbol": "CLANKER",
"address": "0x1bc...",
"attention": 1.35,
"change24h": 0.52,
"change24hPercent": 62.65,
"mentions24h": 47,
"rank": 3
}
hot-now
{
"trending": [
{ "symbol": "ANON",
"mentions": 142,
"velocity": "+4.5%",
"momentum": "rising",
"driver": "@vitalik" },
...
]
}
attentionhot-nowcreatorsmentionsmindsharehistory

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plug into your project our social data.

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