How to Track Kanye West's Tweets Using the Twitter (X) API
Kanye West (legally Ye, X handle @kanyewest) runs one of the most volatile high-profile X accounts: long quiet stretches punctuated by burst-posting sessions where the account drops 50-200 tweets in a few hours, then often deletes large portions of them within the next 24-72 hours. For analytics teams covering culture, music, advertising, or politics, that volatility is the entire data signal — what he posts during a burst can carry signal that the survivors don't — earlier tweets are often the more controversial; the surviving subset reflects what was later considered acceptable for the public record.
Programmatically capturing those bursts before deletions land — and snapshotting the full burst sequence with engagement counts at multiple time points — is what separates a real Kanye-watching pipeline from a basic timeline scrape. The official X API can do it, but burst-cadence polling at the rate needed to catch every tweet is expensive on the per-call pricing; a third-party API path is dramatically cheaper.
This guide walks through the methods (timeline polling, real-time filter stream, webhook), gives a runnable Python pipeline tuned for Kanye's specific posting pattern, and covers the deletion-archive layer that turns ephemeral bursts into queryable history.
Who is Kanye West and why @kanyewest is unusually hard to track
Kanye West (Ye) is a rapper, record producer, fashion designer, and cultural figure with a Grammy-winning music catalog and a long list of high-profile collaborations and controversies. His X account, @kanyewest, has been suspended, reinstated, deactivated, and re-activated multiple times since 2020 — each reactivation tends to be followed by an intense burst-posting period.
What makes this account a difficult tracking target:
Implications for the tracker design: you can't poll once per minute and call it done. You need (a) a fast poll loop during active bursts, (b) a slow heartbeat during quiet stretches, (c) a deletion-archive layer that periodically re-fetches old tweet IDs and flags 404s as deletions, and (d) account-state monitoring so the pipeline doesn't silently fail when the handle goes away.
Twitter / X API methods for tracking @kanyewest
The three architectural patterns for following one account on the X platform map differently to Kanye-style burst output.
1. Adaptive timeline polling (pull) — Fetch the account's recent posts at a variable interval: every 10-15 minutes during quiet stretches, every 30-60 seconds when a burst is detected (a burst = >5 new tweets in the last hour). Cheapest by total call count; the dominant pattern in this guide.
2. Real-time filter stream (push) — Subscribe to from:kanyewest via WebSocket. Sub-second freshness; you never miss a burst tweet. Operational cost: you need a persistent worker (not serverless). On TwitterAPI.io, this is oapi/tweet_filter/add_rule + a wss:// connection.
3. Webhook callbacks — Same push pattern but stateless on your side: each new tweet hits your HTTP endpoint. Operationally easiest for serverless deploys; slightly higher per-event cost than the filter stream.
For most teams: option 1 with burst detection is the right starting point — option 2 only earns its keep when you're shipping an alerting/notification product where being 30 seconds late to a Kanye tweet is a product-breaking miss.
TwitterAPI.io quickstart — pulling @kanyewest's timeline
TwitterAPI.io is a third-party X API offering pay-per-call pricing at $0.00015 per read — roughly 33× cheaper than the official X API's $0.005 per post read. Setup is a Google sign-in + an X-API-Key header; no OAuth flow, no project approval, no monthly minimum.
Endpoint chosen for this guide: GET /twitter/user/last_tweets — returns the most recent ~20 tweets for a given handle in a single call, with engagement metrics and full tweet text. A 30-second poll cadence during bursts catches the full sequence; a 15-minute cadence during quiet stretches keeps costs negligible.
Auth: put your API key in the X-API-Key header. That's the entire auth surface — no OAuth signing, no consumer/access token pair.
Per-call gap economics for Kanye's burst pattern: assume 6 burst days/year × 5 hours × 120 polls/hour + 359 quiet days × 96 polls/day = 38,064 polls/year. At TwitterAPI.io: $5.71/year. At official X API: $190.32/year. The gap matters once you're tracking dozens of accounts.
Code example — a burst-aware Python pipeline
The Python script below implements the adaptive-polling pattern: it tracks how many new tweets it sees per poll, treats a sudden uptick as a burst, and shortens its sleep interval until the burst dies down. It also re-fetches the last ~50 tweet IDs every 6 hours to detect deletions, and persists everything to JSONL.
Implementation notes:
- BURST_THRESHOLD = 5 — five new tweets in a single poll triggers burst mode (poll interval drops to 30 seconds).
- QUIET_INTERVAL = 900, BURST_INTERVAL = 30 — the two cadence modes (15 min vs 30 sec).
- Re-fetch loop runs every 6 hours: pulls the IDs we've seen recently and re-queries each via tweet/info; any 404 is a deletion, logged as a {tweet_id, deleted_detected_at} event.
- A simple state file persists which mode we're in across restarts.
Total cost at this cadence, conservatively: $0.50/month per tracker. Linear when you scale to N accounts.
Burst, engagement, and deletion patterns in the data
A week or two of @kanyewest data, captured with the burst-aware pipeline, surfaces patterns you can't see in a static social-listening dashboard:
1. Deletion rate is bimodal. Tweets posted during the first 30 minutes of a burst tend to have a noticeably higher deletion rate than tweets posted later in the burst (measure against your specific account's captured data; the exact ratio varies by burst and topic). The earliest tweets are often the most controversial; the survivors are typically the more measured follow-ups.
2. Engagement velocity beats final engagement. A tweet's like count in the first few minutes can carry signal that the final 24-hour count alone obscures — partly because Kanye tweets sometimes get deleted before they fully spread, and partly because the platform's amplification feedback loop has typically settled within minutes. Calibrate the threshold against your own captured data.
3. Burst topic-clusters predict next-burst content with surprising accuracy. Run TF-IDF clustering on the surviving tweets from burst N, and the dominant theme is more likely than chance to recur in burst N+1 within the same season — useful if you're trying to anticipate which collaborations/products/positions the next burst will discuss.
Operational notes — deletion archives, account states, cost scaling
Because @kanyewest is one of the more-deleted accounts on X, your archive layer is what makes the dataset useful. Three operational notes:
- Capture before fetch-time deletion. Polls happen on a delay; tweets sometimes vanish before your first fetch. The tweet/info re-fetch loop catches anything you saw once; for the small subset that never appears in any poll, you need a real-time filter stream (option 2 above) — that's the only way to beat sub-second deletions.
- Handle account-deactivation gracefully. When @kanyewest's handle resolves to nothing, your user/info call will 404. Treat this as state, not error — flag your pipeline as account_inactive and back off on poll frequency until the handle resolves again.
- Cost scales linearly. At the adaptive cadence above, one account ≈ $0.50/month on TwitterAPI.io vs. $16/month on the official X API at $0.005/read. Tracking the top 50 burst-posting US cultural accounts ≈ $25/month vs $800/month. See [the per-call rate breakdown for the official X API](/blog/x-api-cost-breakdown-2026) and [the broader cost-vs-cap discussion in our rate-limit explainer](/blog/twitter-rate-limit-exceeded).
What this data unlocks for downstream products
Several concrete applications composes on top of a @kanyewest burst-aware pipeline:
- Brand-safety alerting — Notify a brand's PR team within 60 seconds of a Kanye tweet that mentions their product/category, with the tweet's engagement velocity attached as a severity score. Saves hours of manual monitoring during bursts.
- Cultural-moment correlation — Join with music streaming charts, Google Trends, and fashion-resale data (StockX, Goat) to test whether Kanye tweets predict spikes in those signals or just follow them. Most pop-culture analytics gets this causality backwards.
- Deletion-as-signal datasets — Train a classifier on (deleted vs surviving) burst tweets with text + engagement_velocity + reply_sentiment features. The deleted-tweet signal is often more informative than the surviving-tweet signal because deletions correlate with what the poster considered too far.
- High-engagement quote-network analysis — Pull retweets + quote-tweets for each burst tweet; cluster the quoting users to map adjacent commentator networks. Useful for building targeted audience lists for related products/content.
# pip install requests
import json
import time
import pathlib
import requests
API_KEY = "YOUR_TWITTERAPI_IO_KEY"
BASE = "https://api.twitterapi.io"
HANDLE = "kanyewest"
BURST_THRESHOLD = 5
QUIET_INTERVAL = 900 # 15 min
BURST_INTERVAL = 30 # 30 s
STATE_DIR = pathlib.Path(".state")
STATE_DIR.mkdir(exist_ok=True)
IDS_FILE = STATE_DIR / "kanyewest_seen_ids.json"
OUT_FILE = STATE_DIR / "kanyewest_tweets.jsonl"
DEL_FILE = STATE_DIR / "kanyewest_deletions.jsonl"
headers = {"X-API-Key": API_KEY}
def load_seen():
return set(json.loads(IDS_FILE.read_text())) if IDS_FILE.exists() else set()
def save_seen(ids):
IDS_FILE.write_text(json.dumps(sorted(ids)))
def resolve_uid(handle):
r = requests.get(f"{BASE}/twitter/user/info",
params={"userName": handle}, headers=headers, timeout=10)
if r.status_code == 404:
return None # account deactivated
r.raise_for_status()
return str(r.json()["data"]["id"])
def poll_once(uid):
r = requests.get(f"{BASE}/twitter/user/last_tweets",
params={"userId": uid}, headers=headers, timeout=10)
r.raise_for_status()
tweets = r.json().get("data", {}).get("tweets", [])
s = load_seen()
new = [t for t in tweets if t["id"] not in s]
with OUT_FILE.open("a") as f:
for t in new:
f.write(json.dumps({
"id": t["id"],
"created_at": t.get("createdAt"),
"text": t.get("text"),
"likes": t.get("likeCount", 0),
"retweets": t.get("retweetCount", 0),
"replies": t.get("replyCount", 0),
"first_seen_at": time.time(),
}) + "\n")
s.update(t["id"] for t in new)
save_seen(s)
return len(new)
def recheck_for_deletions(uid, recent_n=50):
s = load_seen()
recent = sorted(s)[-recent_n:]
for tid in recent:
r = requests.get(f"{BASE}/twitter/tweet/info",
params={"tweetId": tid}, headers=headers, timeout=10)
if r.status_code == 404:
with DEL_FILE.open("a") as f:
f.write(json.dumps({"id": tid, "deleted_at": time.time()}) + "\n")
if __name__ == "__main__":
last_delcheck = 0
while True:
uid = resolve_uid(HANDLE)
if uid is None:
print("account inactive — backing off 30 min")
time.sleep(1800)
continue
try:
n_new = poll_once(uid)
except Exception as e:
print("poll err:", e); n_new = 0
if time.time() - last_delcheck > 6 * 3600:
recheck_for_deletions(uid)
last_delcheck = time.time()
interval = BURST_INTERVAL if n_new >= BURST_THRESHOLD else QUIET_INTERVAL
print(f"n_new={n_new} sleeping {interval}s")
time.sleep(interval)
Questions readers ask
What is Kanye West's Twitter / X handle?
@kanyewest is the primary handle. He sometimes posts under the legal name Ye but the account handle hasn't changed. The account has been deactivated and reactivated multiple times since 2020; when active, it's @kanyewest.
How often does Kanye West tweet?
His posting is bursty: long quiet stretches (sometimes weeks) followed by intense burst sessions (50-200 tweets over 2-6 hours). A static 1-minute poll wastes calls during quiet stretches and misses bursts; adaptive polling (15 min quiet → 30 sec burst) is the right pattern.
How do I capture Kanye West tweets that get deleted within hours?
Two layers. First, poll frequently during bursts (the runnable code above does this automatically when it detects a burst). Second, run a periodic re-fetch loop (every 6 hours) that re-queries the tweet IDs you've seen and flags 404s as deletions. For sub-minute deletions, only the real-time filter stream (WebSocket on from:kanyewest) catches every tweet.
What happens to my tracker when @kanyewest's account is deactivated?
user/info returns 404. Treat that as state (not error): flag your pipeline as account_inactive and back off polling (e.g., to once every 30-60 minutes) until the handle resolves again. The script in the code section handles this branch automatically.
Can I get historical Kanye West tweets via the API?
Yes — use TwitterAPI.io's advanced_search with from:kanyewest since:YYYY-MM-DD until:YYYY-MM-DD. The catch: tweets already deleted at the time of your search will not be returned by any X-API-compatible surface (official or third-party). Third-party archives (Wayback Machine, snapshot sites) are the only path for already-deleted content, with their own legal and accuracy caveats.
Is it legal to programmatically track a public X account?
Programmatic access to public tweets via the X API (official or third-party) is governed by the API's Terms of Service, not by per-account consent — public posts are available for public consumption and analysis under those terms. Note: this is access to public data only; protected/private accounts are excluded. For downstream commercial or journalistic use, consult your jurisdiction's data-protection regulations.
Continue
- X API — TwitterAPI.io coverage hub
- How to track Acyn's tweets — the same pattern applied to a political-journalism account
- How to track Ben Shapiro's tweets — high-volume political commentary pattern
- Deleted tweet search — the broader pattern for capturing ephemeral content
- X API cost breakdown — per-call pricing at scale
- TwitterAPI.io pricing — per-call rates and volume tiers
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