How to Find AI Influencers on public platform via API

Quick Answer

The TwexAPI Search User for AI influencer research endpoint (/twitter/search-user//) returns candidate profiles matching specific AI-related keywords. Use the response to build a review queue, not as proof of expertise, trustworthiness, audience quality, or brand fit.. Authenticate with a Bearer Token on api.twexapi.io. Typical reads use about 5 Credits (~$0.05 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 AI influencer research endpoint return?

returns candidate profiles matching specific AI-related keywords. Use the response to build a review queue, not as proof of expertise, trustworthiness, audience quality, or brand fit.

Why use TwexAPI instead of the first-party developer API for this workflow?

For Search User for AI influencer research, TwexAPI provides Bearer-authenticated workflows documented at https://docs.twexapi.io. A typical post or profile read uses 5 Credits (about $0.05 per 1,000 under the published conversion), and qualifying paid plans publish 20+ QPS. New accounts receive 20,000 starting Credits. As of 2026-08-20, first-party developer 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 5 Credits. Under the published conversion, 1,000 such reads use 5,000 Credits, or about $0.05; a 10,000-read job uses about 50,000 Credits. Actual costs vary by endpoint, so confirm the endpoint price and current plan at https://twexapi.io/pricing.

AI conversations on X move through many account types: researchers, startup founders, independent builders, product operators, prompt engineers, newsletter writers, and investors. If you start from manual search, the workflow gets repetitive quickly. You search a phrase, open profile after profile, copy handles into a sheet, and still have no consistent way to compare them.

TwexAPI's Search User endpoint gives you a cleaner first step. Search for a keyword such as AI founder, LLM engineer, or AI product manager, request a target count, and turn the returned user profiles into a shortlist your team can review.

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 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer 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 discover X accounts whose profile information matches an AI-related keyword.

It is useful when you need to:

• Build an AI influencer research list before an internal launch or partnership review.
• Find founders and builders discussing LLM products, agents, copilots, or AI infrastructure.
• Create a seed list for monitoring, newsletter sourcing, or analyst review.
• Compare candidate accounts by profile fit, follower count, verification status, and list membership.

The endpoint returns profile objects. It does not tell you whether a person is trustworthy or a good brand fit. Use it to create a candidate list, then review the accounts before any relationship decision or spend.

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 ~5 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/AI%20founder/50' \
  --header 'Authorization: Bearer <token>'

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

Common fields include id, name, screen_name, description, location, followers_count, following_count, listed_count, statuses_count, is_blue_verified, is_verified, verified_type, 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 AI Founder",
8      "screen_name": "example_ai",
9      "description": "Building AI agents for support teams",
10      "followers_count": 42000,
11      "listed_count": 310,
12      "is_verified": true,
13      "is_blue_verified": true,
14      "verified_type": "blue"
15    }
16  ]
17}

Pick Better AI Keywords

Answer: Pick Better AI Keywords means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Broad queries like AI return mixed accounts. Start with phrases that describe the role, topic, or buyer segment you care about.

Run several narrow searches instead of one broad search. This gives you better control over why an account entered the shortlist.

Python Client

Answer: Python Client means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Keep the API client small. Return raw users first, then let separate functions normalize, score, and save the profiles.

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("AI founder", 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 Review

Answer: Normalize Profiles for Review means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

The first useful output is a stable review row. Keep the raw profile, but expose the fields a marketer, analyst, or partnerships lead can scan quickly.

1from typing import Any
2
3AI_TERMS = [
4    "ai",
5    "artificial intelligence",
6    "llm",
7    "agent",
8    "agents",
9    "machine learning",
10    "ml",
11    "copilot",
12]
13
14def text_contains_ai_signal(text: str) -> bool:
15    lowered = text.lower()
16    return any(term in lowered for term in AI_TERMS)
17
18def to_candidate_row(user: dict[str, Any], keyword: str) -> dict[str, Any]:
19    description = user.get("description") or ""
20    followers_count = user.get("followers_count") or 0
21    listed_count = user.get("listed_count") or 0
22    verified = user.get("is_verified") is True
23    blue_verified = user.get("is_blue_verified") is True
24    profile_match = text_contains_ai_signal(description)
25
26    return {
27        "keyword": keyword,
28        "user_id": user.get("id"),
29        "screen_name": user.get("screen_name"),
30        "name": user.get("name"),
31        "description": description,
32        "location": user.get("location"),
33        "followers_count": followers_count,
34        "listed_count": listed_count,
35        "is_verified": verified,
36        "is_blue_verified": blue_verified,
37        "verified_type": user.get("verified_type"),
38        "profile_match": profile_match,
39        "profile_url": f"https://x.com/{user.get('screen_name')}",
40        "raw": user,
41    }

Score the Shortlist

Answer: Score the Shortlist means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Follower count is visible, but it is a weak signal by itself. A better triage score combines profile relevance, list membership, verification signals, and audience size.

1import math
2
3def candidate_score(row: dict[str, Any]) -> float:
4    score = 0.0
5
6    if row["profile_match"]:
7        score += 40
8    if row["is_verified"]:
9        score += 10
10    if row["is_blue_verified"]:
11        score += 5
12    if row["verified_type"] in {"business", "government"}:
13        score += 10
14
15    followers = row["followers_count"] or 0
16    listed = row["listed_count"] or 0
17    score += min(math.log10(followers + 1) * 10, 45)
18    score += min(math.log10(listed + 1) * 6, 20)
19
20    return round(score, 2)
21
22keywords = [
23    "AI founder",
24    "LLM engineer",
25    "AI agents",
26    "AI product manager",
27]
28
29candidates = []
30seen_user_ids = set()
31
32for keyword in keywords:
33    for user in search_users(keyword, target_count=50):
34        user_id = user.get("id")
35        if not user_id or user_id in seen_user_ids:
36            continue
37        seen_user_ids.add(user_id)
38
39        row = to_candidate_row(user, keyword)
40        row["score"] = candidate_score(row)
41        candidates.append(row)
42
43candidates.sort(key=lambda row: row["score"], reverse=True)
44
45for row in candidates[:25]:
46    print(row["score"], row["screen_name"], row["followers_count"], row["profile_url"])

This score is only a review queue. It should help humans decide what to inspect first, not automatically approve a partnership, sponsorship, or contact.

Save a Review CSV

Answer: Save a Review CSV means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Most teams want the first pass in a spreadsheet. Export a compact CSV with the review fields, then keep the raw JSON separately for audits and future enrichment.

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

Where Search User Fits in the Influencer Stack

Answer: Where Search User Fits in the Influencer Stack means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

Search User is the discovery step. After you find candidate accounts, combine it with other workflows:

1. Use Search User to create the candidate list.
2. Use profile fields to remove obvious mismatches and duplicates.
3. Use timeline or search endpoints to inspect recent content.
4. Use follower or following data when audience overlap matters.
5. Move only reviewed accounts into a CRM, monitoring list, or partnership workflow.

This keeps the process explainable. You can always trace an account back to the keyword that found it and the fields that made it worth reviewing.

Common Pitfalls

Answer: Common Pitfalls means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

• Do not search only AI. Use role- and topic-specific keywords.
• Do not use profile metadata to initiate contact automatically.
• Do not rank by followers alone. Many useful AI accounts are smaller but highly relevant.
• Keep accounts with unclear bios in manual review instead of auto-removing them.
• Store the fetch time because profile metadata can change.
• Deduplicate by id, not just screen_name, because handles can change.

Wrap-up

Answer: Wrap-up means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 5 Credits (about $0.05 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, first-party developer API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.

TwexAPI's Search User endpoint is a practical way to turn AI-related keywords into a reviewable influencer shortlist. Search several narrow phrases, normalize the returned profile objects, score candidates conservatively, and send the top accounts to human review.

That gives your team a repeatable discovery workflow without pretending that profile metadata alone can decide who is influential.