[中文]

用途:

通过高级算法分析TikTok视频流量数据，精确检测可能存在的虚假观看量和不自然互动

基于TikTok赛马机制(Traffic Pool)流量池理论，评估内容真实性和流量质量

提供全面的欺诈风险分析，包含8种维度、20+指标的深度评估

为创作者、MCN机构和内容管理者提供专业的流量质量报告和优化建议

参数:

item_id: 视频作品ID，必填参数，可从视频URL中提取(例如: https://www.tiktok.com/@tiktok/video/7460937381265411370 中的7460937381265411370)

content_category: 内容分类，可选参数，影响互动率基准值，选项包括:

default: 默认类别，通用内容

entertainment: 娱乐内容，预期有较高互动率

education: 教育内容，预期有适中互动和较高收藏率

product: 产品内容，预期有较低互动但较高转化

verified_large: 大型认证账号，预期互动率适当降低

返回内容详解:

video_metrics: 视频核心指标

total_views: 总观看量，视频被观看的总次数

total_likes: 总点赞数，用户点赞互动次数

total_comments: 总评论数，用户评论互动次数

total_favorites: 总收藏数，用户收藏次数

total_shares: 总分享数，用户分享次数

engagement_rates: 互动率指标，值越高越好

like_ratio: 点赞率，正常值 1-10%，大账号可能较低

comment_ratio: 评论率，正常值 0.1-0.5%，高于1%极佳

favorite_ratio: 收藏率，正常值 0.05-0.8%

share_ratio: 分享率，正常值 0.05-0.5%，高于1%极佳

creator_metrics: 创作者账号健康指标

account_age_days: 账号存在天数，越长越可信

follower_count: 粉丝数量，影响预期观看量

verified: 是否验证账号，认证账号可信度更高

trust_score: 账号信任度评分(0-100)，越高越可信

content_metrics: 内容质量指标

content_type: 内容类型(video, image等)

created_by_ai: 是否AI生成，AI生成内容可能有特定流量模式

high_quality_upload: 是否高质量上传，高质量上传更可信

fake_view_analysis: 虚假流量综合分析

fake_score: 虚假流量评分(0-100)，评分越低越好:

0-20: 极低风险，自然流量模式

20-40: 低风险，可能有少量异常但不构成问题

40-60: 中等风险，存在值得关注的异常

60-80: 高风险，明显的虚假流量特征

80-100: 极高风险，几乎确定存在虚假流量

confidence_level: 风险等级，分为"Minimal", "Low", "Medium", "High"

estimated_fake_views: 估计虚假观看量，基于虚假流量模型推算

fake_view_percentage: 虚假观看百分比，虚假占总量的比例

is_suspicious: 是否可疑，综合判断是否需要关注

main_detection_reason: 主要检测原因，最显著的异常特征

component_scores: 各维度异常评分，各项都是0-100，越低越好:

engagement_score: 互动异常评分

distribution_score: 分布异常评分

consistency_score: 一致性异常评分

creator_credibility_score: 创作者可信度异常评分

content_authenticity_score: 内容真实性异常评分

follower_correlation_score: 粉丝相关性异常评分

racing_mechanism_score: 赛马机制异常评分

fan_growth_score: 粉丝增长异常评分

traffic_pool: 流量池分析(TikTok赛马机制)

current_tier: 当前流量池级别(1-8)，越高代表流量越大

current_tier_name: 当前流量池名称

expected_tier: 预期流量池级别，基于有机流量预测

expected_tier_name: 预期流量池名称

current_views_range: 当前流量池预期观看范围

expected_views_range: 预期流量池观看范围

estimated_organic_views: 估计有机观看量，扣除虚假后的真实观看

suspicious_features: 可疑特征列表，检测到的具体异常现象

recommendations: 建议操作

action: 建议操作类型，可能值包括:

no_action: 无需操作，健康内容

monitor: 持续监控，存在轻微异常

scheduled_review: 安排审核，存在值得关注的异常

immediate_review: 立即审核，存在严重异常

risk_level: 风险等级("low", "medium", "high", "critical")

potential_revenue_impact: 潜在收益影响

suggested_steps: 建议步骤，具体操作建议

mcn_report: (可选)MCN商业影响分析报告，适用于商业账号

summary: 摘要信息

business_impact: 商业影响评估

revenue_impact: 收益影响评估

brand_safety_impact: 品牌安全影响

platform_relationship: 平台关系影响

contract_impact: 合约影响评估

recommended_actions: 建议操作清单

historical_context: 历史背景数据

特性与优势:

基于TikTok原生流量池(Traffic Pool)理论构建的精确评估系统

8个维度、20+指标的全面分析，覆盖流量、互动、创作者、内容等全方位评估

自适应算法，根据账号规模、认证状态、内容类型自动调整阈值

基于大数据统计模型的异常检测，准确识别不自然流量模式

为不同规模账号(微型、小型、中型、大型、超大型)提供定制化评估标准

提供详细的商业影响分析和具体可行的建议步骤

示例响应:

{
  "code": 200,
  "router": "/api/v1/tiktok/analytics/detect_fake_views",
  "params": {
    "item_id": "7460937381265411370",
    "content_category": "verified_large"
  },
  "data": {
    "video_metrics": {
      "total_views": 159414915,
      "total_likes": 15817234,
      "total_comments": 392493,
      "total_favorites": 1051470,
      "total_shares": 1312741,
      "engagement_rates": {
        "like_ratio": 0.09922,
        "comment_ratio": 0.00246,
        "favorite_ratio": 0.0066,
        "share_ratio": 0.00823
      }
    },
    "creator_metrics": {
      "account_age_days": 3733.94,
      "follower_count": 89827771,
      "verified": true,
      "trust_score": 100
    },
    "content_metrics": {
      "content_type": "video",
      "created_by_ai": false,
      "high_quality_upload": true
    },
    "fake_view_analysis": {
      "fake_score": 7.16,
      "confidence_level": "Minimal",
      "estimated_fake_views": 7970745,
      "fake_view_percentage": 5.0,
      "is_suspicious": false,
      "main_detection_reason": "Statistical View Anomalies",
      "component_scores": {
        "engagement_score": 0.0,
        "distribution_score": 10.0,
        "consistency_score": 0,
        "creator_credibility_score": 0,
        "content_authenticity_score": 34.0,
        "follower_correlation_score": 35.0,
        "racing_mechanism_score": 0,
        "fan_growth_score": 45
      }
    },
    "traffic_pool": {
      "current_tier": 8,
      "current_tier_name": "8th-Level Traffic Pool",
      "expected_tier": 8,
      "expected_tier_name": "8th-Level Traffic Pool",
      "current_views_range": "30M+",
      "expected_views_range": "30M+",
      "estimated_organic_views": 148000807
    },
    "suspicious_features": [
      "Suspicious: Reached 100000 followers from 10000 in only 31 days",
      "Suspicious: Account gaining 24063 followers per day on average"
    ],
    "recommendations": {
      "action": "no_action",
      "risk_level": "low",
      "potential_revenue_impact": "minimal",
      "suggested_steps": [
        "No immediate action required",
        "Include in routine monitoring"
      ]
    },
    "mcn_report": {
      "summary": {
        "estimated_revenue_impact": 7970.745,
        "recommended_actions": "No immediate action required"
      },
      "business_impact": {
        "revenue_impact": {
          "level": "low",
          "estimated_amount": 7970.745
        },
        "brand_safety_impact": {
          "level": "minimal"
        },
        "platform_relationship": {
          "status": "good"
        }
      }
    }
  }
}

[English]

Purpose:

Analyze TikTok video traffic data using advanced algorithms to precisely detect potential fake views and unnatural engagement

Evaluate content authenticity and traffic quality based on TikTok's Traffic Pool theory

Provide comprehensive fraud risk analysis with in-depth assessment across 8 dimensions and 20+ metrics

Deliver professional traffic quality reports and optimization recommendations for creators, MCN agencies, and content managers

Parameters:

item_id: Video ID, required parameter, can be extracted from video URL (e.g., 7460937381265411370 from https://www.tiktok.com/@tiktok/video/7460937381265411370)

content_category: Content category, optional parameter, affects engagement rate benchmarks, options include:

default: Default category for general content

entertainment: Entertainment content, expected to have higher engagement

education: Educational content, expected to have moderate engagement and higher save rates

product: Product content, expected to have lower engagement but higher conversion

verified_large: Large verified accounts, expected to have appropriately lower engagement rates

Return Description:

video_metrics: Core video metrics

total_views: Total number of views

total_likes: Total number of likes

total_comments: Total number of comments

total_favorites: Total number of saves

total_shares: Total number of shares

engagement_rates: Engagement rate metrics, higher is better

like_ratio: Like rate, normal range 1-10%, may be lower for large accounts

comment_ratio: Comment rate, normal range 0.1-0.5%, excellent if above 1%

favorite_ratio: Save rate, normal range 0.05-0.8%

share_ratio: Share rate, normal range 0.05-0.5%, excellent if above 1%

creator_metrics: Creator account health indicators

account_age_days: Account age in days, longer is more credible

follower_count: Number of followers, affects expected view count

verified: Whether account is verified, verified accounts have higher credibility

trust_score: Account trust score (0-100), higher is more trustworthy

content_metrics: Content quality indicators

content_type: Content type (video, image, etc.)

created_by_ai: Whether AI-generated, AI-generated content may have specific traffic patterns

high_quality_upload: Whether high-quality upload, high-quality uploads are more credible

fake_view_analysis: Comprehensive fake traffic analysis

fake_score: Fake view score (0-100), lower is better:

0-20: Very low risk, natural traffic patterns

20-40: Low risk, may have minor anomalies but not problematic

40-60: Medium risk, anomalies worth attention

60-80: High risk, obvious fake traffic characteristics

80-100: Very high risk, almost certainly fake traffic

confidence_level: Risk level, categorized as "Minimal", "Low", "Medium", "High"

estimated_fake_views: Estimated fake views, calculated based on fake traffic model

fake_view_percentage: Fake view percentage, proportion of fake views to total views

is_suspicious: Whether suspicious, comprehensive judgment if attention is needed

main_detection_reason: Main detection reason, most significant anomaly feature

component_scores: Dimensional anomaly scores, each 0-100, lower is better:

engagement_score: Engagement anomaly score

distribution_score: Distribution anomaly score

consistency_score: Consistency anomaly score

creator_credibility_score: Creator credibility anomaly score

content_authenticity_score: Content authenticity anomaly score

follower_correlation_score: Follower correlation anomaly score

racing_mechanism_score: Racing mechanism anomaly score

fan_growth_score: Fan growth anomaly score

traffic_pool: Traffic pool analysis (TikTok racing mechanism)

current_tier: Current traffic pool level (1-8), higher means more traffic

current_tier_name: Current traffic pool name

expected_tier: Expected traffic pool level, based on organic traffic prediction

expected_tier_name: Expected traffic pool name

current_views_range: Current traffic pool expected view range

expected_views_range: Expected traffic pool view range

estimated_organic_views: Estimated organic views, real views after deducting fake ones

suspicious_features: List of suspicious features, specific detected anomalies

recommendations: Recommended actions

action: Recommended action type, possible values include:

no_action: No action needed, healthy content

monitor: Continuous monitoring, minor anomalies present

scheduled_review: Schedule review, anomalies worth attention

immediate_review: Immediate review, serious anomalies present

risk_level: Risk level ("low", "medium", "high", "critical")

potential_revenue_impact: Potential revenue impact

suggested_steps: Suggested steps, specific action recommendations

mcn_report: (Optional) MCN business impact analysis report, applicable for business accounts

summary: Summary information

business_impact: Business impact assessment

revenue_impact: Revenue impact assessment

brand_safety_impact: Brand safety impact

platform_relationship: Platform relationship impact

contract_impact: Contract impact assessment

recommended_actions: Recommended action list

historical_context: Historical background data

Features and Advantages:

Precise evaluation system built on TikTok's native Traffic Pool theory

Comprehensive analysis across 8 dimensions and 20+ metrics, covering traffic, engagement, creator, content, etc.

Adaptive algorithm automatically adjusts thresholds based on account size, verification status, content type

Anomaly detection based on big data statistical models, accurately identifies unnatural traffic patterns

Provides customized evaluation standards for different account sizes (micro, small, medium, large, extra-large)

Delivers detailed business impact analysis and specific, actionable recommendations

Example Response:

{
  "code": 200,
  "router": "/api/v1/tiktok/analytics/detect_fake_views",
  "params": {
    "item_id": "7460937381265411370",
    "content_category": "verified_large"
  },
  "data": {
    "video_metrics": {
      "total_views": 159414915,
      "total_likes": 15817234,
      "total_comments": 392493,
      "total_favorites": 1051470,
      "total_shares": 1312741,
      "engagement_rates": {
        "like_ratio": 0.09922,
        "comment_ratio": 0.00246,
        "favorite_ratio": 0.0066,
        "share_ratio": 0.00823
      }
    },
    "creator_metrics": {
      "account_age_days": 3733.94,
      "follower_count": 89827771,
      "verified": true,
      "trust_score": 100
    },
    "content_metrics": {
      "content_type": "video",
      "created_by_ai": false,
      "high_quality_upload": true
    },
    "fake_view_analysis": {
      "fake_score": 7.16,
      "confidence_level": "Minimal",
      "estimated_fake_views": 7970745,
      "fake_view_percentage": 5.0,
      "is_suspicious": false,
      "main_detection_reason": "Statistical View Anomalies",
      "component_scores": {
        "engagement_score": 0.0,
        "distribution_score": 10.0,
        "consistency_score": 0,
        "creator_credibility_score": 0,
        "content_authenticity_score": 34.0,
        "follower_correlation_score": 35.0,
        "racing_mechanism_score": 0,
        "fan_growth_score": 45
      }
    },
    "traffic_pool": {
      "current_tier": 8,
      "current_tier_name": "8th-Level Traffic Pool",
      "expected_tier": 8,
      "expected_tier_name": "8th-Level Traffic Pool",
      "current_views_range": "30M+",
      "expected_views_range": "30M+",
      "estimated_organic_views": 148000807
    },
    "suspicious_features": [
      "Suspicious: Reached 100000 followers from 10000 in only 31 days",
      "Suspicious: Account gaining 24063 followers per day on average"
    ],
    "recommendations": {
      "action": "no_action",
      "risk_level": "low",
      "potential_revenue_impact": "minimal",
      "suggested_steps": [
        "No immediate action required",
        "Include in routine monitoring"
      ]
    },
    "mcn_report": {
      "summary": {
        "estimated_revenue_impact": 7970.745,
        "recommended_actions": "No immediate action required"
      },
      "business_impact": {
        "revenue_impact": {
          "level": "low",
          "estimated_amount": 7970.745
        },
        "brand_safety_impact": {
          "level": "minimal"
        },
        "platform_relationship": {
          "status": "good"
        }
      }
    }
  }
}

Bearer Token

Provide your bearer token in the

Authorization

header when making requests to protected resources.

Example:

Authorization: Bearer ********************

{ "code": 200, "request_id": "string", "message": "Request successful. This request will incur a charge.", "message_zh": "请求成功，本次请求将被计费。", "support": "Discord: https://discord.gg/aMEAS8Xsvz", "time": "string", "time_stamp": 0, "time_zone": "America/Los_Angeles", "docs": "string", "cache_message": "string", "cache_message_zh": "string", "cache_url": "string", "router": "", "params": {}, "data": null }

检测视频虚假流量分析/Detect fake views in video

[中文]

用途:

参数:

返回内容详解:

特性与优势:

示例响应:

[English]

Purpose:

Parameters:

Return Description:

Features and Advantages:

Example Response:

Request

Responses

检测视频虚假流量分析/Detect fake views in video

[中文]#

用途:#

参数:#

返回内容详解:#

特性与优势:#

示例响应:#

[English]#

Purpose:#

Parameters:#

Return Description:#

Features and Advantages:#

Example Response:#

Request

Responses

[中文]

用途:

参数:

返回内容详解:

特性与优势:

示例响应:

[English]

Purpose:

Parameters:

Return Description:

Features and Advantages:

Example Response: