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Influencer vetting

X influencer health check 템플릿

Campaign budget을 쓰기 전에 X creators의 fake followers, bot-like engagement, paid-post networks, collaboration risk를 점검하세요.

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X에는 follower 수, paid verification, generic replies, copied engagement만 보면 좋아 보이는 accounts가 많습니다. 하지만 실제 audience trust는 약할 수 있습니다.

Kollab은 influencer vetting을 evidence-based workflow로 바꿉니다. X profiles를 붙여 넣으면 public signals를 수집하고 health metrics를 계산하며 risk dimensions를 score해 brand team이 review할 수 있는 report를 남깁니다.

실행 단계

  1. 01

    Public X signals 수집

    Kollab이 profile과 recent timeline signals를 읽고 account age, follower data, bio links, verification, visible engagement를 정리합니다.

  2. 02

    Fake-account risk score

    Engagement rate, follow-back ratio, generic replies, repeated promo language, suspicious URLs, network overlap을 확인합니다.

  3. 03

    Creators side by side 비교

    각 creator에 evidence, risk score, contact decision이 붙어 raw follower count가 아니라 audience quality로 비교할 수 있습니다.

  4. 04

    Reviewable audit record 남기기

    Report, shortlist, rejected accounts, manual-review questions, payment guardrails를 campaign workspace에 남깁니다.

시작 프롬프트

Brand collaboration 전에 X influencers를 평가하고 싶습니다.

Brand context:
- Product or offer: [what we want to promote]
- Target audience: [who should care]
- Region and language: [markets we care about]
- Collaboration type: sponsored post / thread / long-term ambassador / affiliate
- Budget range: [optional]
- Must-avoid risks: fake followers, bot engagement, paid-post networks, irrelevant audience, reputation risk

X accounts to audit:
1. https://x.com/[username]
2. https://x.com/[username]
3. https://x.com/[username]

Please build an influencer health check report:
1. URL과 @handle을 clean account list로 normalize하세요.
2. 각 account의 public profile과 recent timeline signals를 수집하세요: account age, followers, following, bio, links, verification type, recent posts, replies, likes, visible engagement.
3. Metrics를 계산하세요: account age, following-to-follower ratio, average engagement rate, reply percentage, generic reply rate, daily likes given.
4. Suspicious patterns를 evidence와 함께 flag하세요: very low engagement, high follow-back ratio, repetitive replies, pure emoji replies, generic praise, copied promo wording, suspicious bio links, shared brand or URL clusters.
5. 각 account에 risk table, score, decision을 제공하세요: reject, manual review, or safe to contact.
6. Connected accounts가 의심되면 shared bios, links, handles, engagement patterns를 설명하는 network section을 추가하세요.
7. 마지막으로 brand-safe shortlist를 만드세요: contact first, manual review, avoid, and extra proof to request before payment.
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