虚假追随者和参与欺诈浪费了影响者的营销预算。检测 TikTok 上的假货需要分析多个信号：参与度与关注者比率、评论质量模式、关注者增长峰值和内容一致性。本教程使用 Scavio TikTok API 构建自动欺诈检测管道。它获取个人资料数据和最近的视频，运行异常统计测试，并输出欺诈风险评分。每个分析的创作者的总成本为 2-3 个积分（0.01-0.015 美元）。
前置条件
- 已安装 Python 3.9+
- 请求已安装库
- 来自 scavio.dev 的 Scavio API 密钥
- 待分析的 TikTok 用户名
操作指南
步骤 1: 获取个人资料和参与度数据
获取创作者个人资料统计数据和最近的视频表现数据。两者都需要检测表明虚假参与的不一致情况。
Python
import requests, os
API_KEY = os.environ['SCAVIO_API_KEY']
TIKTOK_URL = 'https://api.scavio.dev/api/v1/tiktok'
def fetch_creator_data(username: str) -> dict:
# Profile
profile_resp = requests.post(f'{TIKTOK_URL}/user/info',
headers={'Authorization': f'Bearer {API_KEY}',
'Content-Type': 'application/json'},
json={'username': username})
profile_data = profile_resp.json().get('data', {})
# Recent videos
videos_resp = requests.post(f'{TIKTOK_URL}/user/posts',
headers={'Authorization': f'Bearer {API_KEY}',
'Content-Type': 'application/json'},
json={'username': username, 'count': 30, 'cursor': 0})
videos = videos_resp.json().get('data', {}).get('videos', [])
stats = profile_data.get('stats', {})
return {
'username': username,
'followers': stats.get('followerCount', 0),
'following': stats.get('followingCount', 0),
'total_likes': stats.get('heartCount', 0),
'video_count': stats.get('videoCount', 0),
'videos': videos
}步骤 2: 检查参与率异常
合法帐户的参与度与关注者比率是可预测的。相对于粉丝数量而言，拥有假粉丝的账户的参与度异常低。
Python
def check_engagement_ratio(data: dict) -> dict:
followers = max(data['followers'], 1)
videos = data['videos']
if not videos:
return {'flag': 'NO_VIDEOS', 'risk': 50}
avg_likes = sum(v.get('stats', {}).get('diggCount', 0) for v in videos) / len(videos)
avg_plays = sum(v.get('stats', {}).get('playCount', 0) for v in videos) / len(videos)
likes_to_followers = (avg_likes / followers) * 100
plays_to_followers = (avg_plays / followers) * 100
# Normal ranges for TikTok:
# Likes/followers: 1-15% is typical, <0.5% suspicious, >20% suspicious (bought likes)
# Plays/followers: 10-200% is typical, <5% suspicious
engagement_risk = 0
flags = []
if likes_to_followers < 0.5:
engagement_risk += 30
flags.append(f'Very low like ratio: {likes_to_followers:.2f}%')
elif likes_to_followers > 25:
engagement_risk += 20
flags.append(f'Abnormally high like ratio: {likes_to_followers:.2f}%')
if plays_to_followers < 5:
engagement_risk += 25
flags.append(f'Very low play ratio: {plays_to_followers:.2f}%')
return {
'likes_to_followers': round(likes_to_followers, 2),
'plays_to_followers': round(plays_to_followers, 2),
'risk_score': engagement_risk,
'flags': flags
}步骤 3: 分析参与一致性
真实账户显示出参与度的自然变化。虚假的参与度通常看起来过于一致（机器人喜欢）或具有极端的峰值（购买的参与度）。
Python
import statistics
def check_engagement_consistency(data: dict) -> dict:
videos = data['videos']
if len(videos) < 5:
return {'risk_score': 10, 'flags': ['Too few videos to analyze']}
like_counts = [v.get('stats', {}).get('diggCount', 0) for v in videos]
play_counts = [v.get('stats', {}).get('playCount', 0) for v in videos]
flags = []
risk = 0
# Check if engagement is suspiciously uniform
if like_counts and statistics.mean(like_counts) > 0:
cv = statistics.stdev(like_counts) / statistics.mean(like_counts) # coefficient of variation
if cv < 0.1: # Less than 10% variation = suspiciously uniform
risk += 25
flags.append(f'Suspiciously uniform likes (CV={cv:.3f})')
# Check for sudden engagement spikes
if play_counts:
median_plays = statistics.median(play_counts)
spikes = sum(1 for p in play_counts if p > median_plays * 10)
spike_ratio = spikes / len(play_counts)
if spike_ratio > 0.3: # More than 30% of videos have 10x spikes
risk += 20
flags.append(f'{spikes}/{len(play_counts)} videos have 10x play spikes')
# Check likes-to-comments ratio (bots rarely comment)
for v in videos:
s = v.get('stats', {})
likes = s.get('diggCount', 0)
comments = s.get('commentCount', 0)
if likes > 1000 and comments < likes * 0.005: # Less than 0.5% comment rate
risk += 5
flags.append(f'Very low comment ratio on video with {likes} likes')
break # Only flag once
return {'risk_score': min(risk, 50), 'flags': flags}步骤 4: 检查关注/关注比例
相对于其关注者而言，关注大量账户的账户通常会参与关注计划或使用关注机器人。
Python
def check_follow_ratio(data: dict) -> dict:
followers = max(data['followers'], 1)
following = data['following']
ratio = following / followers
flags = []
risk = 0
if followers > 10000 and ratio > 1.0:
risk += 30
flags.append(f'Following > followers ({following:,} / {followers:,})')
elif followers > 10000 and ratio > 0.5:
risk += 15
flags.append(f'High follow ratio: {ratio:.2f}')
# Check if total likes seem inflated relative to video count
if data['video_count'] > 0:
likes_per_video = data['total_likes'] / data['video_count']
expected_likes = followers * 0.05 # 5% of followers per video is generous
if likes_per_video > expected_likes * 5:
risk += 15
flags.append(f'Inflated total likes: {likes_per_video:,.0f}/video vs {expected_likes:,.0f} expected')
return {'follow_ratio': round(ratio, 3), 'risk_score': risk, 'flags': flags}步骤 5: 生成完整的欺诈报告
将所有检测信号结合到全面的欺诈风险评估中，并提供总体风险评分和详细细分。
Python
def fraud_report(username: str) -> dict:
data = fetch_creator_data(username)
engagement = check_engagement_ratio(data)
consistency = check_engagement_consistency(data)
follow = check_follow_ratio(data)
total_risk = engagement['risk_score'] + consistency['risk_score'] + follow['risk_score']
all_flags = engagement.get('flags', []) + consistency.get('flags', []) + follow.get('flags', [])
verdict = 'LOW RISK' if total_risk < 20 else 'MEDIUM RISK' if total_risk < 50 else 'HIGH RISK'
report = {
'username': username,
'followers': data['followers'],
'total_risk_score': min(total_risk, 100),
'verdict': verdict,
'engagement_check': engagement,
'consistency_check': consistency,
'follow_check': follow,
'all_flags': all_flags,
'credits_used': 2
}
print(f'@{username}: {verdict} ({total_risk}/100)')
print(f' Followers: {data["followers"]:,}')
for flag in all_flags:
print(f' - {flag}')
return report
# report = fraud_report('suspicious_creator')Python 示例
Python
import os, requests, statistics
API_KEY = os.environ['SCAVIO_API_KEY']
TT = 'https://api.scavio.dev/api/v1/tiktok'
def tt(endpoint, body):
return requests.post(f'{TT}/{endpoint}',
headers={'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'},
json=body).json()
def detect_fake(username):
profile = tt('user/info', {'username': username}).get('data', {})
stats = profile.get('stats', {})
followers = stats.get('followerCount', 1)
posts = tt('user/posts', {'username': username, 'count': 20, 'cursor': 0})
videos = posts.get('data', {}).get('videos', [])
avg_likes = sum(v.get('stats', {}).get('diggCount', 0) for v in videos) / max(len(videos), 1)
ratio = (avg_likes / followers) * 100
risk = 'HIGH' if ratio < 0.5 else 'MEDIUM' if ratio < 1.0 else 'LOW'
print(f'@{username}: {risk} RISK (like ratio: {ratio:.2f}%, {followers:,} followers)')
detect_fake('example_creator')JavaScript 示例
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
const TT = 'https://api.scavio.dev/api/v1/tiktok';
async function tt(endpoint, body) {
const r = await fetch(`${TT}/${endpoint}`, {
method: 'POST',
headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify(body)
});
return r.json();
}
async function detectFake(username) {
const profile = await tt('user/info', { username });
const followers = profile.data?.stats?.followerCount || 1;
const posts = await tt('user/posts', { username, count: 20, cursor: 0 });
const videos = posts.data?.videos || [];
const avgLikes = videos.reduce((s, v) => s + (v.stats?.diggCount || 0), 0) / Math.max(videos.length, 1);
const ratio = (avgLikes / followers) * 100;
console.log(`@${username}: ${ratio < 0.5 ? 'HIGH' : ratio < 1 ? 'MED' : 'LOW'} RISK (${ratio.toFixed(2)}%)`);
}
detectFake('example_creator').catch(console.error);预期输出
JSON
@suspicious_creator: HIGH RISK (72/100)
Followers: 500,000
- Very low like ratio: 0.31%
- Suspiciously uniform likes (CV=0.082)
- Following > followers (520,000 / 500,000)
@legitimate_creator: LOW RISK (8/100)
Followers: 85,000
(no flags)
Credits used: 2 per creator ($0.01)