--- title: "检测视频虚假流量分析/Detect fake views in video" method: GET path: "/api/v1/tiktok/analytics/detect_fake_views" tags: ["TikTok-Analytics-API"] --- # 检测视频虚假流量分析/Detect fake views in video `GET /api/v1/tiktok/analytics/detect_fake_views` ## Request - Base URL: `https://api.tikhub.io` (the document declares no server; this is the origin it was published from) - URL: `https://api.tikhub.io/api/v1/tiktok/analytics/detect_fake_views` - Auth: HTTP bearer # [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" } } } } } ``` ## Query parameters - `item_id` string, required: 作品id/Video id - `content_category` string: 内容分类/Content category, options: default, entertainment, education, product, verified_large ## Response `200` Successful Response - ResponseModel - `code` integer: HTTP status code | HTTP状态码 - `request_id` string, nullable: Unique request identifier | 唯一请求标识符 - `message` string: Response message (EN-US) | 响应消息 (English) - `message_zh` string: Response message (ZH-CN) | 响应消息 (中文) - `support` string: Support message | 支持消息 - `time` string: The time the response was generated | 生成响应的时间 - `time_stamp` integer: The timestamp the response was generated | 生成响应的时间戳 - `time_zone` string: The timezone of the response time | 响应时间的时区 - `docs` string, nullable: Link to the API Swagger documentation for this endpoint | 此端点的 API Swagger 文档链接 - `cache_message` string, nullable: Cache message (EN-US) | 缓存消息 (English) - `cache_message_zh` string, nullable: Cache message (ZH-CN) | 缓存消息 (中文) - `cache_url` string, nullable: The URL to access the cached result | 访问缓存结果的 URL - `router` string: The endpoint that generated this response | 生成此响应的端点 - `params` unknown - `data` unknown ## Other responses - `422`: Validation Error ## Changes - **2026-10-08** `96a122a5f220`: 2 info - added the optional property `detail/items/ctx` to the response with the `422` status - added the optional property `detail/items/input` to the response with the `422` status - **2026-09-01** `50f9ec6e1cd3`: 2 info - the `cache_message` response's property default value changed from `This request will be cached. You can access the cached result directly using the URL below, valid for 24 hours. Accessing the cache will not incur additional charges.` to `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.` for the status `200` - the `cache_message_zh` response's property default value changed from `本次请求将被缓存，你可以使用下面的 URL 直接访问缓存结果，有效期为 24 小时，访问缓存不会产生额外费用。` to `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。` for the status `200` - **2026-04-14** `ee5b7c9c88f5`: 1 info - endpoint added [Operation history](https://skmtc.dev/tikhub/apis/tikhub-douyin-tiktok-xiaohongshu-lemon8-bilibili-kuaishou-pi/changes/api/v1/tiktok/analytics/detect_fake_views/get.md) --- [API](https://skmtc.dev/tikhub/apis/tikhub-douyin-tiktok-xiaohongshu-lemon8-bilibili-kuaishou-pi.md) - [All operations](https://skmtc.dev/tikhub/apis/tikhub-douyin-tiktok-xiaohongshu-lemon8-bilibili-kuaishou-pi/llms.txt) - [OpenAPI document](https://skmtc.dev/tikhub/apis/tikhub-douyin-tiktok-xiaohongshu-lemon8-bilibili-kuaishou-pi/revisions/86a3483c705a?raw)