# 检测视频虚假流量分析/Detect fake views in video ## OpenAPI Specification ```yaml openapi: 3.0.1 info: title: '' description: '' version: 1.0.0 paths: /api/v1/tiktok/analytics/detect_fake_views: get: summary: 检测视频虚假流量分析/Detect fake views in video deprecated: false description: >- # [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: ```json { "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: ```json { "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" } } } } } ``` operationId: detect_fake_views_api_v1_tiktok_analytics_detect_fake_views_get tags: - TikTok-Analytics-API - TikTok-Analytics-API parameters: - name: item_id in: query description: 作品id/Video id required: true example: '7460937381265411370' schema: type: string description: 作品id/Video id title: Item Id - name: content_category in: query description: >- 内容分类/Content category, options: default, entertainment, education, product, verified_large required: false schema: type: string description: >- 内容分类/Content category, options: default, entertainment, education, product, verified_large default: default title: Content Category responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/ResponseModel' headers: {} x-apifox-name: OK '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' headers: {} x-apifox-name: Unprocessable Entity security: - HTTPBearer: [] x-apifox: schemeGroups: - id: F-kDrTJZ1ebpNRK4-NqPH schemeIds: - HTTPBearer required: true use: id: F-kDrTJZ1ebpNRK4-NqPH scopes: F-kDrTJZ1ebpNRK4-NqPH: HTTPBearer: [] x-apifox-folder: TikTok-Analytics-API x-apifox-status: released x-run-in-apifox: https://app.apifox.com/web/project/4705614/apis/api-298317627-run components: schemas: ResponseModel: properties: code: type: integer title: Code description: HTTP status code | HTTP状态码 default: 200 request_id: anyOf: - type: string - type: 'null' title: Request Id description: Unique request identifier | 唯一请求标识符 message: type: string title: Message description: Response message (EN-US) | 响应消息 (English) default: Request successful. This request will incur a charge. message_zh: type: string title: Message Zh description: Response message (ZH-CN) | 响应消息 (中文) default: 请求成功，本次请求将被计费。 support: type: string title: Support description: Support message | 支持消息 default: 'Discord: https://discord.gg/aMEAS8Xsvz' time: type: string title: Time description: The time the response was generated | 生成响应的时间 time_stamp: type: integer title: Time Stamp description: The timestamp the response was generated | 生成响应的时间戳 time_zone: type: string title: Time Zone description: The timezone of the response time | 响应时间的时区 default: America/Los_Angeles docs: anyOf: - type: string - type: 'null' title: Docs description: >- Link to the API Swagger documentation for this endpoint | 此端点的 API Swagger 文档链接 cache_message: anyOf: - type: string - type: 'null' title: Cache Message description: Cache message (EN-US) | 缓存消息 (English) default: >- This response is cached and accessible via the URL below for 24 hours at no extra cost. The cache is for request tracing only — it doesn't affect the API's data freshness and won't be returned through the API again. cache_message_zh: anyOf: - type: string - type: 'null' title: Cache Message Zh description: Cache message (ZH-CN) | 缓存消息 (中文) default: >- 本次响应已缓存，可通过下方 URL 直接查看，有效期 24 小时，访问缓存链接无额外费用。缓存仅用于请求溯源，不影响接口数据的时效性，也不会再次通过接口返回。 cache_url: anyOf: - type: string - type: 'null' title: Cache Url description: The URL to access the cached result | 访问缓存结果的 URL router: type: string title: Router description: The endpoint that generated this response | 生成此响应的端点 default: '' params: type: string data: anyOf: - type: string - type: 'null' title: Data description: The response data | 响应数据 type: object title: ResponseModel x-apifox-orders: - code - request_id - message - message_zh - support - time - time_stamp - time_zone - docs - cache_message - cache_message_zh - cache_url - router - params - data x-apifox-ignore-properties: [] x-apifox-folder: '' HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError x-apifox-orders: - detail x-apifox-ignore-properties: [] x-apifox-folder: '' ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError x-apifox-orders: - loc - msg - type x-apifox-ignore-properties: [] x-apifox-folder: '' securitySchemes: HTTPBearer: type: bearer description: > ---- #### API Token Introduction: ##### Method 1: Use API Token in the Request Header (Recommended) - **Header**: `Authorization` - **Format**: `Bearer {token}` - **Example**: `{"Authorization": "Bearer your_token"}` - **Swagger UI**: Click on the `Authorize` button in the upper right corner of the page to enter the API token directly without the `Bearer` keyword. ##### Method 2: Use API Token in the Cookie (Not Recommended, Use Only When Method 1 is Unavailable) - **Cookie**: `Authorization` - **Format**: `Bearer {token}` - **Example**: `Authorization=Bearer your_token` #### Get API Token: 1. Register and log in to your account on the TikHub website. 2. Go to the user center, click on the API token menu, and create an API token. 3. Copy and use the API token in the request header. 4. Keep your API token confidential and use it only in the request header. ---- #### API令牌简介: ##### 方法一：在请求头中使用API令牌（推荐） - **请求头**: `Authorization` - **格式**: `Bearer {token}` - **示例**: `{"Authorization": "Bearer your_token"}` - **Swagger UI**: 点击页面右上角的`Authorize`按钮，直接输入API令牌，不需要`Bearer`关键字。 ##### 方法二：在Cookie中使用API令牌（不推荐，仅在无法使用方法一时使用） - **Cookie**: `Authorization` - **格式**: `Bearer {token}` - **示例**: `Authorization=Bearer your_token` #### 获取API令牌: 1. 在TikHub网站注册并登录账户。 2. 进入用户中心，点击API令牌菜单，创建API令牌。 3. 复制并在请求头中使用API令牌。 4. 保密您的API令牌，仅在请求头中使用。 scheme: bearer servers: - url: https://api.tikhub.io description: Production Environment security: [] ```