How to Find Crypto Influencers on X with TwexAPI

Quick Answer

The TwexAPI Search User for crypto influencer research endpoint (/twitter/search-user//) returns candidate profiles matching crypto-related keywords. It does not verify investment claims, audience authenticity, identity, or regulatory suitability, so keep uncertain accounts in manual review.. Authenticate with a Bearer Token on api.twexapi.io. Typical reads use about 14 Credits (~$0.14 per 1,000 under the published conversion), and qualifying paid plans publish 20+ QPS. New accounts receive 20,000 starting Credits. Full request and response fields are in this guide and at https://docs.twexapi.io.

FAQ

What does the Search User for crypto influencer research endpoint return?

returns candidate profiles matching crypto-related keywords. It does not verify investment claims, audience authenticity, identity, or regulatory suitability, so keep uncertain accounts in manual review.

Why use TwexAPI instead of the official X API for this workflow?

For Search User for crypto influencer research, TwexAPI provides Bearer-authenticated workflows documented at https://docs.twexapi.io. A typical post or profile read uses 14 Credits (about $0.14 per 1,000 under the published conversion), and qualifying paid plans publish 20+ QPS. New accounts receive 20,000 starting Credits. As of 2026-08-12, official X API Post reads list at $5 per 1,000 resources and User reads at $10 per 1,000; official rate limits vary by endpoint and access level.

How much does this API workflow cost on TwexAPI?

A typical read uses about 14 Credits. Under the published conversion, 1,000 such reads use 14,000 Credits, or about $0.14; a 10,000-read job uses about 140,000 Credits. Actual costs vary by endpoint, so confirm the endpoint price and current plan at https://twexapi.io/pricing.

Crypto discovery on X is noisy. The same keyword can surface traders, founders, protocol accounts, NFT communities, meme accounts, scammy profiles, analysts, journalists, and bots. Manual search helps for a few handles, but it does not scale into a defensible shortlist.

TwexAPI's Search User endpoint gives you a structured starting point. Search keywords such as crypto analyst, DeFi founder, or Bitcoin trader, then compare the returned user profiles by bio fit, audience size, verification fields, and list membership before anyone reaches out.

What This Workflow Does

Answer: What This Workflow Does means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

This workflow uses GET /twitter/search-user/{keyword}/{target_count} to find X accounts whose profile metadata matches crypto-related search phrases.

Use it for:

• Building a KOL shortlist for a crypto, DeFi, NFT, or Web3 campaign.
• Finding analysts, traders, and newsletter writers to monitor.
• Creating a seed list for community research or launch tracking.
• Comparing crypto accounts before moving them into a CRM or watchlist.

The endpoint returns candidate profiles. It does not validate investment claims, confirm audience authenticity, or replace compliance review. Treat the output as a research queue.

Endpoint

Answer: Endpoint is implemented by calling the TwexAPI endpoint documented in this guide with a Bearer Token; batch or paginated requests reduce overhead to ~14 credits per call at 20+ QPS.

Send a GET request to:

https://api.twexapi.io/twitter/search-user/{keyword}/{target_count}

Authenticate with your TwexAPI bearer token.

curl --request GET \
  --url 'https://api.twexapi.io/twitter/search-user/crypto%20analyst/50' \
  --header 'Authorization: Bearer <token>'

A successful response contains code, msg, and data. The data array contains user profile objects.

Useful fields include id, name, screen_name, description, description_urls, urls, location, followers_count, following_count, listed_count, statuses_count, is_blue_verified, is_verified, verified_type, can_dm, profile_image_url, profile_banner_url, created_at, and created_at_datetime.

1{
2  "code": 200,
3  "msg": "success",
4  "data": [
5    {
6      "id": "1717001045992251392",
7      "name": "Example Crypto Analyst",
8      "screen_name": "example_crypto",
9      "description": "Crypto markets, DeFi research, BTC and ETH commentary",
10      "followers_count": 88000,
11      "listed_count": 740,
12      "is_verified": true,
13      "is_blue_verified": true,
14      "verified_type": "blue",
15      "can_dm": true
16    }
17  ]
18}

Pick Better Crypto Keywords

Answer: Pick Better Crypto Keywords means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

The keyword decides the quality of the candidate pool. Search intent matters more than volume.

Run multiple narrow searches and keep the source keyword in your output. A candidate found through DeFi founder and a candidate found through meme coin usually need different review criteria.

Python Client

Answer: Python Client means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Use URL encoding for phrases with spaces. Keep request code separate from ranking logic so you can test each part independently.

1import os
2from typing import Any
3from urllib.parse import quote
4
5import requests
6
7TOKEN = os.environ["TWEXAPI_BEARER_TOKEN"]
8BASE_URL = "https://api.twexapi.io/twitter/search-user"
9
10def search_users(keyword: str, *, target_count: int = 50) -> list[dict[str, Any]]:
11    encoded_keyword = quote(keyword)
12    url = f"{BASE_URL}/{encoded_keyword}/{target_count}"
13
14    response = requests.get(
15        url,
16        headers={"Authorization": f"Bearer {TOKEN}"},
17        timeout=30,
18    )
19    response.raise_for_status()
20    return response.json().get("data", [])
21
22if __name__ == "__main__":
23    users = search_users("crypto analyst", target_count=25)
24    for user in users[:10]:
25        print(
26            user.get("screen_name"),
27            user.get("followers_count"),
28            user.get("description"),
29        )

Normalize Profiles for a KOL Review Sheet

Answer: Normalize Profiles for a KOL Review Sheet means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

A crypto KOL workflow should make uncertainty visible. Save the keyword that found the account, the profile fields used for scoring, and the raw API response.

1from typing import Any
2
3CRYPTO_TERMS = [
4    "crypto",
5    "bitcoin",
6    "btc",
7    "ethereum",
8    "eth",
9    "defi",
10    "web3",
11    "nft",
12    "onchain",
13    "token",
14    "dao",
15    "solana",
16]
17
18RISK_TERMS = [
19    "signals",
20    "guaranteed",
21    "100x",
22    "pump",
23    "airdrop farming",
24]
25
26def contains_any(text: str, terms: list[str]) -> bool:
27    lowered = text.lower()
28    return any(term in lowered for term in terms)
29
30def to_kol_row(user: dict[str, Any], keyword: str) -> dict[str, Any]:
31    description = user.get("description") or ""
32    followers_count = user.get("followers_count") or 0
33    listed_count = user.get("listed_count") or 0
34
35    return {
36        "keyword": keyword,
37        "user_id": user.get("id"),
38        "screen_name": user.get("screen_name"),
39        "name": user.get("name"),
40        "description": description,
41        "location": user.get("location"),
42        "followers_count": followers_count,
43        "listed_count": listed_count,
44        "is_verified": user.get("is_verified") is True,
45        "is_blue_verified": user.get("is_blue_verified") is True,
46        "verified_type": user.get("verified_type"),
47        "can_dm": user.get("can_dm"),
48        "crypto_profile_match": contains_any(description, CRYPTO_TERMS),
49        "risk_term_match": contains_any(description, RISK_TERMS),
50        "profile_url": f"https://x.com/{user.get('screen_name')}",
51        "raw": user,
52    }

Score Candidates Conservatively

Answer: Score Candidates Conservatively means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Crypto campaigns need a stricter review queue than generic creator discovery. Penalize obvious risk terms and push uncertain profiles to manual review.

1import math
2
3def kol_score(row: dict[str, Any]) -> float:
4    score = 0.0
5
6    if row["crypto_profile_match"]:
7        score += 35
8    if row["is_verified"]:
9        score += 8
10    if row["is_blue_verified"]:
11        score += 4
12    if row["verified_type"] == "business":
13        score += 8
14    if row["risk_term_match"]:
15        score -= 20
16
17    followers = row["followers_count"] or 0
18    listed = row["listed_count"] or 0
19    score += min(math.log10(followers + 1) * 10, 45)
20    score += min(math.log10(listed + 1) * 7, 24)
21
22    return round(score, 2)
23
24keywords = [
25    "crypto analyst",
26    "DeFi founder",
27    "Bitcoin trader",
28    "onchain analyst",
29    "Web3 founder",
30]
31
32candidates = []
33seen_user_ids = set()
34
35for keyword in keywords:
36    for user in search_users(keyword, target_count=50):
37        user_id = user.get("id")
38        if not user_id or user_id in seen_user_ids:
39            continue
40        seen_user_ids.add(user_id)
41
42        row = to_kol_row(user, keyword)
43        row["score"] = kol_score(row)
44        row["review_status"] = "manual_review" if row["risk_term_match"] else "candidate"
45        candidates.append(row)
46
47candidates.sort(key=lambda row: row["score"], reverse=True)
48
49for row in candidates[:25]:
50    print(
51        row["score"],
52        row["review_status"],
53        row["screen_name"],
54        row["followers_count"],
55        row["profile_url"],
56    )

This is intentionally conservative. The score should decide review order, not whether a creator is approved for paid promotion, token communication, or investor-facing messaging.

Save the Shortlist

Answer: Save the Shortlist means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Export a CSV for the review team and JSONL for raw storage. Keep risk_term_match visible so reviewers know why an account needs extra attention.

1import csv
2import json
3from datetime import datetime, timezone
4from pathlib import Path
5
6timestamp = datetime.now(timezone.utc).isoformat()
7csv_path = Path("crypto-kol-candidates.csv")
8jsonl_path = Path("crypto-kol-candidates.raw.jsonl")
9
10fieldnames = [
11    "score",
12    "review_status",
13    "keyword",
14    "screen_name",
15    "name",
16    "description",
17    "location",
18    "followers_count",
19    "listed_count",
20    "is_verified",
21    "is_blue_verified",
22    "verified_type",
23    "can_dm",
24    "crypto_profile_match",
25    "risk_term_match",
26    "profile_url",
27]
28
29with csv_path.open("w", newline="", encoding="utf-8") as f:
30    writer = csv.DictWriter(f, fieldnames=fieldnames)
31    writer.writeheader()
32    for row in candidates:
33        writer.writerow({field: row.get(field) for field in fieldnames})
34
35with jsonl_path.open("w", encoding="utf-8") as f:
36    for row in candidates:
37        f.write(json.dumps({
38            "fetched_at": timestamp,
39            "candidate": row,
40        }, ensure_ascii=False) + "\n")
41
42print(f"Saved {len(candidates)} crypto KOL candidates")

Add Review Rules Before Outreach

Answer: Add Review Rules Before Outreach means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

A crypto influencer list should pass through policy and quality checks before anyone sends a message.

Use a review checklist like this:

1. Confirm the profile is relevant to the campaign topic.
2. Inspect recent posts for scams, undisclosed promotions, or audience mismatch.
3. Check whether the account discusses tokens, protocols, or markets in a way your compliance rules allow.
4. Confirm the contact path. can_dm is a signal, not a delivery guarantee.
5. Record why the account was approved, rejected, or held for later review.

For crypto, the workflow is stronger when "no decision yet" is an explicit state. Do not force every discovered account into approve or reject.

Combine Search User with Other TwexAPI Endpoints

Answer: Combine Search User with Other TwexAPI Endpoints is implemented by calling the TwexAPI endpoint documented in this guide with a Bearer Token; batch or paginated requests reduce overhead to ~14 credits per call at 20+ QPS.

Search User gives you candidates. Use other endpoints to enrich the decision:

Keep these steps separate. Discovery, validation, and monitoring answer different questions.

Common Pitfalls

Answer: Common Pitfalls means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

• Do not treat a crypto keyword match as endorsement or credibility.
• Do not use follower count as a proxy for audience quality.
• Do not automatically message every account with can_dm: true.
• Deduplicate by id, not just screen_name.
• Save the keyword that discovered each account so reviewers can understand context.
• Store raw profiles and fetch timestamps because account metadata changes.
• Add extra review for accounts whose bios include promotional or high-risk language.

Wrap-up

Answer: Wrap-up means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 14 Credits (about $0.14 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-12, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

TwexAPI's Search User endpoint helps turn crypto keywords into a structured KOL discovery workflow. Search specific phrases, normalize the returned profiles, score candidates conservatively, and keep risky or uncertain accounts in manual review.

That gives your team a cleaner way to build crypto influencer lists without treating noisy profile metadata as final truth.