How to Track Ben Shapiro's Tweets Using the Twitter (X) API

Q: What is Ben Shapiro's official Twitter / X handle?

**@benshapiro** is the primary account. His podcast also has a secondary account at **@BenShapiroShow**. Don't confuse with the historical placeholder **@Ben__Shapiro** or the unrelated **@benjshap** — those are different users.

Q: Can I track @benshapiro using only the official X API?

Yes — the official X API exposes `users/by/username/{username}` and `users/{id}/tweets`. The catch is per-call pricing: at $0.005 per post read, even a single-account 60-second poll runs roughly $216/month. A third-party API like TwitterAPI.io provides the same data at $0.00015/read — about 33× cheaper — and skips the X developer account and saved payment card the official platform requires.

Q: What's a low-cost, legal way to back-fill @benshapiro's tweet history?

Use TwitterAPI.io's `advanced_search` endpoint with `from:benshapiro since:YYYY-MM-DD until:YYYY-MM-DD`. It returns 20 tweets per page with a cursor token; ~3 months of his output is roughly 3,500 tweets = 175 calls × $0.003 (a full 20-tweet page) = about **$0.53**. Official X API equivalent is around **$17.50**. The advanced-search filter syntax matches X's public search operators 1:1.

By Sarah Wong•9 min read

Ben Shapiro (@benshapiro) is one of X's highest-output political commentators — Daily Wire co-founder, podcast host, and #1 NYT bestselling author with a daily posting cadence that routinely exceeds 30 tweets across original posts, replies, and quoted reposts. For sentiment analysts, political-data shops, and news monitoring teams, his account is a continuous high-signal stream of conservative commentary on US politics, foreign policy, and current events.

Programmatic access to that stream — collecting every new tweet within seconds, normalizing the engagement metrics, and pushing it into your analysis pipeline — is the difference between reading commentary and measuring it. The official X API can do this, but the rate-limit + per-call cost economics are punishing at this volume; a third-party API path is usually cheaper and faster to integrate.

This guide covers both: what the official X API offers for @benshapiro tracking, where it breaks down at scale, and a runnable TwitterAPI.io-based pipeline (Python, ~40 lines) that pulls his timeline, computes engagement deltas, and outputs JSONL for downstream analysis.

01 — Section

Who is Ben Shapiro and why developers track @benshapiro

Ben Shapiro is a conservative political commentator, lawyer, and media entrepreneur. He co-founded The Daily Wire, one of the largest conservative news subscription platforms, and hosts The Ben Shapiro Show — a podcast that has consistently ranked in the top 5 US news podcasts on Apple Podcasts since 2018. His X handle is @benshapiro and his official secondary account for the show is @BenShapiroShow.

Why his account is a programmatic-tracking target:

Reason	Detail
High-volume output	Routinely 25-40 original tweets + replies per day — among the highest-cadence US political accounts
Predictable timing	Posts heaviest 6-10am ET (pre-show prep) and 9-11pm ET (post-show commentary)
High engagement rates	Original political tweets typically pull 5-50k likes; viral takes routinely exceed 200k
Topic concentration	Strong topic clusters: foreign policy (Israel, Iran), domestic politics, media criticism — clean for unsupervised topic-modeling
News-source signal	Frequently first-to-comment on major political events, useful for breaking-news alerting systems

These properties make the account a natural test bed for sentiment-pipeline development, predictive-engagement modeling, and real-time-alerting prototypes. The same techniques apply to any high-output political X account.

02 — Section

Twitter / X API methods for tracking @benshapiro

There are three architecturally distinct ways to programmatically follow a single account on the X platform; each has a different cost/freshness/operational profile.

1. Polling the user timeline (pull) — Periodically fetch the account's recent posts and diff against your last-known state. Simplest to operate; freshness limited by your poll interval. Most third-party APIs (including TwitterAPI.io) make this trivially cheap.

2. Real-time filter stream (push) — Subscribe to a filter rule (e.g. from:benshapiro) and receive matching tweets via a long-lived WebSocket. Sub-second freshness; requires you to operate a persistent worker. TwitterAPI.io exposes this via oapi/tweet_filter/add_rule + a wss:// connection.

3. Webhook callbacks — A push pattern but stateless on your side: the API service hits your endpoint with each matching tweet. Operationally easiest for serverless deploys (Vercel/Cloudflare Workers); slightly higher per-event cost than the WebSocket stream.

For most teams tracking a single high-output account, option 1 (timeline polling at 5-minute intervals) hits the right cost/complexity point — option 2 is overkill unless you're running an alerting product that must beat the news cycle by minutes.

03 — Section

TwitterAPI.io quickstart — pulling @benshapiro'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/tweet/advanced_search with query=from:benshapiro since_time:<unix seconds of the newest tweet you have seen> and queryType=Latest. It returns only the tweets newer than that timestamp (up to about 20 per call), including engagement metrics (likes, retweets, replies, quote counts) and the full tweet text, so for @benshapiro's volume 1 call every 5 minutes is enough to never miss a tweet (he posts roughly every 30-45 minutes during active hours). 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).

Authentication: put your API key in the X-API-Key header. That's the entire auth surface — no OAuth signing, no consumer/access token pair. Compare to the official X API's 4-key OAuth 1.0a signed request flow:

Path	Authentication	X developer account	Per-read cost
Official X API	4 keys + signed OAuth	Required (plus a saved payment card)	$0.005
TwitterAPI.io	1 X-API-Key header	Not required	$0.00015

The per-call gap is what makes high-cadence-account tracking economical: 30 tweets/day × 30 days = 900 reads/month per account. Official: $4.50/account/month. TwitterAPI.io: $0.135/account/month. On TwitterAPI.io a poll that finds nothing new is still billed the $0.00015 minimum, so the real bill is set mostly by your poll cadence (see the operational notes below).

04 — Section

Code example — a Python pipeline for @benshapiro

Below is a runnable Python script that asks search for @benshapiro's tweets newer than the last one it saw, dedupes against your local state, and appends new tweets to a JSONL file with normalized engagement metrics. Pair it with cron or systemd-timer for a 5-minute poll cadence.

The script makes one advanced_search call per poll and keeps two small local files: the next since_time and a seen_tweet_ids.json to dedupe. Polling cost at the recommended cadence: about $1.30/month for one account (8,640 polls × $0.00015), plus $0.00015 per new tweet returned (900 tweets a month is $0.135).

Key implementation notes embedded in the script:

The first run is seeded with the current time, so it reports only tweets posted after you start it; after that the next since_time is one second after the newest tweet seen.

We dedupe on tweet.id against a local file, so restarts don't replay tweets.

Engagement metrics are normalized to a flat dict so downstream consumers (pandas, BigQuery, etc.) don't have to parse the nested API shape.

We compute engagement_rate = (likes + retweets + replies + quotes) / follower_count per tweet — this is the unit that's actually comparable across high-vs-low-volume accounts, and it's the metric most sentiment-modeling work uses.

05 — Section

Patterns that show up in the data — engagement, timing, topics

Once the pipeline above has run for a week, you have a clean dataset to surface the @benshapiro-specific patterns that an off-the-shelf social-listening tool would miss. Three patterns commonly discussed in social-media analytics for high-output political accounts; verify against your own captured data:

1. Engagement is bimodal by tweet type. Original tweets pull 5-50k likes; replies + quoted reposts pull 1-3k. Group by is_reply and is_quote before computing any engagement-rate aggregates — mixing them produces a noisy median that doesn't predict anything.

2. Posting time predicts engagement. Pre-show prep tweets (6-10am ET) and post-show commentary tweets (9-11pm ET) pull substantially more engagement than mid-day tweets. The simplest model that beats a uniform-prior baseline: expected_engagement = mean_engagement_in_same_hour_of_week.

3. Topic clusters are stable over weeks. Run TF-IDF + k-means on a rolling 30-day window of tweet text; the clusters that emerge (foreign policy, domestic politics, media commentary, Daily Wire promotion) are stable enough that you can use last week's cluster centroids to classify this week's tweets without retraining.

06 — Section

Operational notes — rate limits, costs, and scaling to N accounts

At the 5-minute poll cadence, a single @benshapiro tracker uses 288 API calls per day = 8,640/month. A poll that finds nothing new is billed TwitterAPI.io's 15-credit minimum ($0.00015), so that's about $1.30/month in polls, plus $0.00015 per new tweet returned; the free credits for new accounts (up to $1 for accounts that pass the bot check, no card required, expires after 31 days; see /pricing) are enough to smoke-test the script before you fund it.

Scaling to N high-output accounts at the same cadence is linear: 100 accounts ≈ 864,000 polls/month ≈ $129.60/month on TwitterAPI.io (864,000 × $0.00015), plus $0.00015 per new tweet returned (90,000 tweets a month at 900 per account is $13.50). That is low enough to make multi-account political-data products viable on the third-party path.

A few operational gotchas worth knowing before you scale this:

The official X API enforces per-endpoint rate limits in 15-minute windows on endpoints like users/by/username (see docs.x.com for the current numbers). TwitterAPI.io has no 15-minute windows; it limits requests per second instead (0.2 before your first payment, 20 once paid, 50 on Scale, 500 on Business), returning HTTP 429 if you exceed it (no Retry-After header: back off about a second and retry). See the rate-limit explainer for the official cap math.

@benshapiro's tweets are deleted occasionally (typos, redrafts). If you need a deletion-aware archive, layer your timeline pull with periodic re-pulls of older tweet IDs and flag the ones that no longer come back — that's the only signal the public API gives.

For historical tweet collection (back-fill before your tracker started), use advanced_search with from:benshapiro since:YYYY-MM-DD until:YYYY-MM-DD — paginated with the cursor token. Returns 20 tweets per call; budget accordingly.

07 — Section

Use cases that this pipeline unlocks

Once @benshapiro's tweets are in your pipeline, the data composes well with other sources for several concrete applications:

Sentiment-event correlation — Join with daily market data (S&P, oil futures), polling aggregates, or news event timelines to test whether Shapiro's commentary leads or lags specific macro signals. Replace him with a basket of 50 commentators for stronger signal.

Audience-overlap discovery — Pull the user_ids of accounts that consistently like/retweet @benshapiro tweets (via tweet/retweets + tweet/likes endpoints), then cluster those users to find adjacent audiences for similar publications.

Breaking-news alerting — Run real-time topic detection on each new tweet; alert when a previously-unseen topic-bigram crosses a frequency threshold. Shapiro's high-output + topic concentration makes this surprisingly tractable.

Predictive-engagement modeling — Train a regression on (tweet_text, hour_of_day, tweet_type, day_of_week) → engagement_count; useful as a baseline for understanding what kinds of political content the platform amplifies independent of follower count.

python

# pip install requests
import json
import time
import pathlib
from datetime import datetime
import requests

API_KEY = "YOUR_TWITTERAPI_IO_KEY"
BASE = "https://api.twitterapi.io"
HANDLE = "benshapiro"
STATE_DIR = pathlib.Path(".state")
STATE_DIR.mkdir(exist_ok=True)
IDS_FILE = STATE_DIR / "seen_tweet_ids.json"
SINCE_FILE = STATE_DIR / "since_time.txt"
OUT_FILE = STATE_DIR / "benshapiro_tweets.jsonl"

headers = {"X-API-Key": API_KEY}


def seen_ids():
    if IDS_FILE.exists():
        return set(json.loads(IDS_FILE.read_text()))
    return set()


def save_seen(ids):
    IDS_FILE.write_text(json.dumps(sorted(ids)))


def to_unix(created_at):
    # createdAt looks like "Tue Oct 13 16:42:56 +0000 2026"
    return int(datetime.strptime(created_at, "%a %b %d %H:%M:%S %z %Y").timestamp())


def poll_once(follower_count):
    # Ask only for tweets newer than the last one we saw. A poll with nothing
    # new returns 0 tweets and is billed the 15-credit minimum ($0.00015).
    since = int(SINCE_FILE.read_text()) if SINCE_FILE.exists() else int(time.time())  # first run: start from now
    r = requests.get(f"{BASE}/twitter/tweet/advanced_search",
                     params={"query": f"from:{HANDLE} since_time:{since}",
                             "queryType": "Latest"},
                     headers=headers, timeout=10)
    r.raise_for_status()
    tweets = r.json().get("tweets") or []
    # next since_time = one second after the newest tweet seen
    SINCE_FILE.write_text(str(max([since] + [to_unix(t["createdAt"]) + 1 for t in tweets])))
    s = seen_ids()
    new = [t for t in tweets if t["id"] not in s]
    with OUT_FILE.open("a") as f:
        for t in new:
            engagement = (t.get("likeCount", 0) + t.get("retweetCount", 0) +
                          t.get("replyCount", 0) + t.get("quoteCount", 0))
            row = {
                "id": t["id"],
                "created_at": t.get("createdAt"),
                "text": t.get("text"),
                "is_reply": bool(t.get("inReplyToId")),
                "is_quote": bool(t.get("quoted_tweet")),
                "likes": t.get("likeCount", 0),
                "retweets": t.get("retweetCount", 0),
                "replies": t.get("replyCount", 0),
                "quotes": t.get("quoteCount", 0),
                "engagement": engagement,
                "engagement_rate": engagement / follower_count if follower_count else 0,
            }
            f.write(json.dumps(row) + "\n")
        s.update(t["id"] for t in new)
    save_seen(s)
    print(f"poll: {len(tweets)} fetched, {len(new)} new")


if __name__ == "__main__":
    # Follower count for engagement_rate. Refresh weekly via user/info.
    follower_count = 7_500_000  # approx; update from user/info periodically
    while True:
        try:
            poll_once(follower_count)
        except Exception as e:
            print("err:", e)
        time.sleep(300)  # 5-minute cadence: 8,640 polls/month x $0.00015 = ~$1.30

08 — Questions

Questions readers ask

What is Ben Shapiro's official Twitter / X handle?

@benshapiro is the primary account. His podcast also has a secondary account at @BenShapiroShow. Don't confuse with the historical placeholder @Ben__Shapiro or the unrelated @benjshap — those are different users.

How often does Ben Shapiro tweet?

Typically 25-40 original tweets and replies per day. He posts heaviest around 6-10am ET (pre-show prep) and 9-11pm ET (post-show commentary). For a real-time tracker, polling once per minute is more than enough to never miss a tweet; once every 5 minutes covers most analytics use cases.

Can I track @benshapiro using only the official X API?

Yes — the official X API exposes users/by/username/{username} and users/{id}/tweets. The catch is per-call pricing: at $0.005 per post read, even a single-account 60-second poll runs roughly $216/month. A third-party API like TwitterAPI.io provides the same data at $0.00015/read — about 33× cheaper — and skips the X developer account and saved payment card the official platform requires.

Are Ben Shapiro's deleted tweets accessible via the API?

No — deleted tweets disappear from the public-facing API surface on both the official X API and on third-party providers. To build a deletion-aware archive, store every tweet ID your poller sees and periodically re-fetch them; the IDs missing from the /twitter/tweets response (or returned with an empty id) are the deletions. Note: this is a measurement of what was visible at fetch time, not what was originally posted.

What's a low-cost, legal way to back-fill @benshapiro's tweet history?

Use TwitterAPI.io's advanced_search endpoint with from:benshapiro since:YYYY-MM-DD until:YYYY-MM-DD. It returns 20 tweets per page with a cursor token; ~3 months of his output is roughly 3,500 tweets = 175 calls × $0.003 (a full 20-tweet page) = about $0.53. Official X API equivalent is around $17.50. The advanced-search filter syntax matches X's public search operators 1:1.

Does tracking a public X account require the account-owner's consent?

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 — tweets posted publicly are available for public consumption and analysis under those terms. Note: this is access to public data only; protected/private accounts are excluded. Consult your jurisdiction's specific data-protection regulations for downstream use cases (especially journalistic / commercial analytics products).

09 — Further reading

Continue

Sources & further reading

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