How to Track Andrew Tate's Tweets Using the Twitter (X) API

Andrew Tate is a former professional kickboxer who became one of the most-discussed influencers on X starting in 2022. His content centers on masculinity, lifestyle, business, and political commentary. His current handle is @cobratate; the original @TateConfidential account is banned. His account has cycled through banned → reinstated states; on X, that cycle is part of what your tracker has to handle.

What makes this account distinctive from a tracking standpoint:

Implications for the tracker design: you need (a) a suspension-aware state machine so the pipeline doesn't crash when the handle comes back as unavailable, (b) frequent polling immediately after each new tweet to capture engagement velocity at multiple time points (T+5m, T+30m, T+2h, T+24h), and (c) a quote-tweet collection pass on viral tweets so the reaction network is in your dataset, not just the original posts.

The three architectural patterns for following one account map to Tate-style usage as follows.

1. Timeline polling with velocity-capture (pull) — Standard polling for new tweets, plus an engagement-revisit pass for tweets younger than 24 hours. Cheapest by total call volume; gives you the velocity profile (which is the signal that matters for this account).

2. Real-time filter stream (push) — Subscribe to from:cobratate via WebSocket. Sub-second freshness; useful if you're building an alerting product but expensive in operational complexity (persistent worker required).

3. Webhook callbacks — Each new tweet hits your HTTP endpoint. Operationally simple for serverless deploys; slightly higher per-event cost than the filter stream.

For most analytics use cases: option 1 with the velocity-capture pass is the right starting point. Add option 2 only if your product depends on beating other commentators to publication by 30-60 seconds.

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.

Endpoints chosen for this guide:

GET /twitter/user/info — resolves @cobratate → user_id; also returns the current banned/active state (a suspended account comes back as HTTP 200 with unavailable: true in the body).

GET /twitter/tweet/advanced_search — with query=from:cobratate since_time:<unix seconds of the newest tweet seen> and queryType=Latest it returns only tweets newer than that timestamp; for the main polling loop. 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).

GET /twitter/tweets?tweet_ids=<id> — re-fetch a specific tweet to capture engagement velocity over time.

GET /twitter/tweet/quotes — pull the quote-tweet thread for a viral tweet (the reaction network).

Authentication: put your API key in the X-API-Key header.

The script below polls search for @cobratate's new tweets (the first run starts from the current time, and each poll then asks for tweets newer than the last one seen), schedules a velocity-revisit pass for each new tweet at T+30m, T+2h, T+24h, and handles the suspension state explicitly: if user/info (checked at startup and then once an hour) reports the account as unavailable (data.unavailable is true in an HTTP 200 response), we flag the account as suspended and back off to a once-per-hour heartbeat check until the handle resolves again.

Implementation notes:

POLL_INTERVAL = 120 — 2-minute base poll. Fine for most analytics uses.

VELOCITY_CHECKPOINTS = [30, 120, 1440] — minutes after first-seen at which we re-fetch each tweet for engagement counts.

SUSPENSION_HEARTBEAT = 3600 — when account is suspended, check hourly via user/info and resume the normal loop when it comes back.

Both the timeline poll and the velocity-revisit pass write to the same JSONL with a kind field (new_tweet vs velocity_checkpoint) so downstream consumers can join cleanly.

Three patterns emerge consistently across a few weeks of @cobratate data:

1. Viral velocity has a fast-decay shape. Viral Tate tweets tend to accumulate the majority of their 24-hour engagement within the first 90 minutes — the precise fraction depends on the tweet and the moment, but the rule that engagement concentrates early holds across most viral cases. The slope of likes/minute in the first 15 minutes is commonly used as a leading indicator of final reach, and is usually a stronger signal than text features alone for high-velocity accounts. Calibrate the specific threshold against your own observed data. Capture engagement at T+15m as your headline velocity metric.

2. Reply sentiment is sharply bimodal. Run an off-the-shelf sentiment classifier on the first 500 replies to each viral Tate tweet; the distribution is bimodal (strongly positive + strongly negative) rather than gaussian. The ratio of pro/anti replies is a stable signal that's stable across his viral tweets — and useful for testing whether your sentiment classifier is calibrated, since this is a clean test case.

3. Quote-tweet networks cluster by political topic, not tweet content. Even when a Tate tweet is about lifestyle or business, the quote-tweet commentary clusters by the commentator's political identity — left-wing critics, right-wing endorsers, center-skeptics. Useful for audience-mapping: pull the quote-tweets for 50 Tate tweets, cluster the quoter user_ids, and you have a high-leverage map of the political-commentary network on X.

Because @cobratate has a history of going from active to suspended and back, three operational notes matter more than they would for a stable account:

Suspension is an event, not an error. Your alerting should distinguish 'API error during fetch' from 'account is suspended.' Suspensions are useful state for downstream products (sentiment analysts, brand-safety teams) and worth surfacing as an explicit event in your data.

Archive your own data assertively. Suspensions can take recent tweets out of public-facing view (re-fetches come back empty even for tweets that existed before suspension). If you want a permanent record, your JSONL is the archive — there's no second-chance API path after the fact.

Cost scales linearly per-account. At the 2-minute base cadence, one Tate tracker makes 720 polls a day = 21,600/month, which is $3.24/month on TwitterAPI.io (21,600 × $0.00015, the minimum charge for a poll that finds nothing new). New tweets add $0.00015 each, each of the 3 velocity checkpoints per new tweet adds another $0.00015, and the hourly account-state check adds 720 profile reads a month at $0.00018 (about $0.13). The same 21,600 calls on the official X API at $0.005/read are $108/month. See our cost-at-volume breakdown for the math at 10/100/1000 accounts.

A clean @cobratate dataset composes with other sources for several use cases:

Brand-safety scoring — Train a classifier on (Tate-tweet, brand-mention) pairs to flag tweets that mention a brand alongside controversial topics — useful as a real-time signal for brand PR teams who need to react fast.

Audience-overlap discovery — Pull the quoter user_ids across 100 viral Tate tweets, cluster them, and you have a high-resolution map of the political-commentary network. Same technique applied to a basket of accounts yields the full influencer-network graph.

Viral-velocity benchmarking — Use Tate tweets as a high-velocity comparison case for your own product's viral-detection thresholds. If your alerting system doesn't fire on Tate tweets, your sensitivity is too low.

Polarization-signal datasets — The bimodal reply-sentiment distribution makes Tate tweets useful as ground-truth examples in polarization-research training data. Most off-the-shelf datasets have weaker signal.