How to Track Kanye West's Tweets Using the Twitter (X) API

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 the IDs that no longer come back as deletions, and (d) account-state monitoring so the pipeline doesn't silently fail when the handle goes away.

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 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 X developer account, no monthly minimum.

Endpoint chosen for this guide: GET /twitter/tweet/advanced_search with query=from:kanyewest since_time:<unix seconds of the newest tweet you have seen> and queryType=Latest. It returns only tweets newer than that timestamp (up to about 20 per 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. We poll search rather than /twitter/user/last_tweets because a poll with nothing new returns 0 tweets and costs the 15-credit minimum ($0.00015), whereas a last_tweets poll bills every tweet on the page (about 20 tweets, so about $130 a month at 60 seconds).

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 (38,064 × $0.00015, the minimum charge for a poll that finds nothing new), plus $0.00015 per new tweet returned. At official X API: $190.32/year (38,064 × $0.005). The gap matters once you're tracking dozens of accounts.

The Python script below implements the adaptive-polling pattern: it asks search for tweets newer than the last one it saw (the first run starts from the current time), 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 /twitter/tweets; any ID missing from the response (or returned with an empty id) is a deletion, logged as a {tweet_id, deleted_detected_at} event.

A small state file stores the next since_time (one second after the newest tweet seen), so a restart picks up where the last run stopped. The account state is checked via user/info at startup and then every 6 hours, not on every poll.

Polling cost at this cadence: about $0.48/month per tracker (38,064 polls a year ÷ 12 × $0.00015), plus $0.00015 per new tweet returned. The deletion re-check is billed separately: 50 lookups every 6 hours is 200 a day at $0.00015 each, about $0.90 a month. Linear when you scale to N accounts.

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.

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 /twitter/tweets 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 still returns HTTP 200; the body says either status: "error" (user not found) or data.unavailable: true (suspended or otherwise unavailable). 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, polling one account ≈ $0.48/month on TwitterAPI.io vs. about $15.86/month for the same number of calls on the official X API at $0.005/read. Tracking the top 50 burst-posting US cultural accounts ≈ $24/month vs about $793/month. See the per-call rate breakdown for the official X API and the broader cost-vs-cap discussion in our rate-limit explainer.

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.