我们提供TikTok红人数据洞察工具和服务助力找寻高性价比红人合作，We Provide TikTok Influencer Insight Tools.

我们提供洞察TikTok红人数据的工具和服务。假粉丝、假播放量、假点赞、机器人评论，往往是影响是否与一个TK红人合作的天坑。我们|飞机号: t.me/bulk006|开发出的TK红人粉丝及互动数据洞察工具，可以让一个海外抖音红人的真实粉丝数、账号获得的真实点赞、转发、评论等互动数近乎裸体般的呈现在你面前。使用我们的数据洞察工具，可以提供关于 TikTok 红人的以下数据洞察见解：

1. 账户性能：

• 粉丝增长率

• 参与率（点赞、评论、分享等）

• 平均观看时长

• 视频观看次数和展示次数

2. 粉丝受众画像统计：

• 年龄范围

• 性别分布

• 地点（国家、城市等）

• 兴趣爱好

• 语言

3. 内容表现：

• 最佳表现的内容类型（例如，视频、直播、故事）

• 平均参与率每帖

• 帖子范围和展示次数

• 参与率按内容类型

4. 参与度指标：

• 点赞、评论、分享等

• 平均参与率每帖

• 参与率按内容类型

• 一天/一周中参与度最高的时间

5. 粉丝质量：

• 假粉丝检测

• 粉丝增长率

• 粉丝画像统计

• 参与率按粉丝分段

6. 红人触达和展示：

• 总触达和展示次数

• 平均触达和展示次数每帖

• 触达和展示次数按内容类型

7. 哈希标签性能：

• 最佳表现的hashtag标签

• 标签参与率

• 标签触达和展示次数

8. 品牌提及和情感：

• 品牌提及和情感分析

• 净情感评分

• 品牌提及频率

9. 合作和伙伴关系见解：

• 与其他达人和品牌的合作历史

• 合作性能指标（例如，参与率、范围等）

• 创作者的合作风格和偏好

10. 达人个人资料和简介洞察：

• 达人者个人资料完整性

• 简介关键词和主题

• 个人资料参与率

• 个人资料粉丝和关注数

请注意，除此之外，我们|飞机号: t.me/bulk006|开发出的TK红人粉丝及互动数据洞察工具功能不仅限于从，还能提供更具深度的其他数据点，限于篇幅没有一一在这里列举。

These tools can provide insights into the following data points for TikTok influencers:

1. Account Performance:

• Follower growth rate

• Engagement rate (likes, comments, shares, etc.)

• Average watch time

• Views and impressions

2. Audience Demographics:

• Age range

• Gender distribution

• Location (country, city, etc.)

• Interests and hobbies

• Language spoken

3. Content Performance:

• Top-performing content types (e.g., videos, lives, stories)

• Average engagement per post

• Post reach and impressions

• Engagement rate by content type

4. Engagement Metrics:

• Likes, comments, shares, etc.

• Average engagement rate per post

• Engagement rate by content type

• Time of day/week with highest engagement

5. Follower Quality:

• Fake follower detection

• Follower growth rate

• Follower demographics

• Engagement rate by follower segment

6. Influencer Reach and Impressions:

• Total reach and impressions

• Average reach and impressions per post

• Reach and impressions by content type

7. Hashtag Performance:

• Top-performing hashtags

• Hashtag engagement rate

• Hashtag reach and impressions

8. Brand Mentions and Sentiment:

• Brand mentions and sentiment analysis

• Net sentiment score

• Brand mention frequency

9. Collaboration and Partnership Insights:

• Collaboration history with other influencers and brands

• Partnership performance metrics (e.g., engagement, reach, etc.)

• Influencer's collaboration style and preferences

10. Influencer Profile and Bio Insights:

• Influencer profile completeness

• Bio keywords and topics

• Profile engagement rate

• Profile followers and following count

Please note that not all tools provide all of these insights, and some may offer additional data points not listed here.

下面，我们|飞机号: t.me/bulk006|已检测假粉丝的功能，介绍一下实现这个功能的技术路径和示例代码。

技术路径1：基于API的方法

1. 使用TikTok API检索红人的粉丝列表和账户信息。

2. 使用机器学习模型或启发式方法分析粉丝列表，识别可疑的账户。

3. 使用API调用检索可疑账户的更多信息，例如帖子历史和参与度指标。

示例代码（Python）

技术路径2：基于网页爬虫的方法

1. 使用网页爬虫库从TikTok网站上检索达人的粉丝列表。

2. 使用机器学习模型或启发式方法分析粉丝列表，识别可疑的账户。

3. 使用网页爬虫检索可疑账户的更多信息。

示例代码（Python）

请注意，这些只是示例代码，您可能需要根据您的具体使用情况进行修改。此外，网页爬虫一定会违反TikTok的服务条款，请使用我们|飞机号: t.me/bulk006|开发出的TK红人粉丝及互动数据洞察工具，绕过海外抖音平台的攻防技术和政策，避免大规模死号或者被限制。

Here are some technical paths and example code for detecting fake followers on TikTok:

Technical Path 1: API-based Approach

1. Use the TikTok API to retrieve the influencer's follower list and account information.

2. Analyze the follower list using machine learning models or heuristics to identify suspicious accounts.

3. Use API calls to retrieve additional information about suspicious accounts, such as their posting history and engagement metrics.

Example Code (Python)

import requestsimport pandas as pd# Set API endpoint and credentialsendpoint = "https://api.tiktok.com/api/v2/followers"username = "influencer_username"password = "influencer_password"client_id = "client_id"client_secret = "client_secret"# Authenticate and retrieve access tokenresponse = requests.post(f"{endpoint}/auth", json={"username": username, "password": password})access_token = response.json()["access_token"]# Retrieve follower listresponse = requests.get(f"{endpoint}/list", headers={"Authorization": f"Bearer {access_token}"})follower_list = response.json()["followers"]# Analyze follower list using machine learning modelfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.feature_extraction.text import TfidfVectorizervectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(follower_list)y = [1 if follower["is_fake"] else 0 for follower in follower_list]model = RandomForestClassifier()
model.fit(X, y)# Predict fake followerspredictions = model.predict(X)# Retrieve additional information about suspicious accountssuspicious_accounts = [follower for follower, prediction in zip(follower_list, predictions) if prediction == 1]for account in suspicious_accounts:    response = requests.get(f"{endpoint}/{account['id']}", headers={"Authorization": f"Bearer {access_token}"})    account_info = response.json()    print(account_info)

Technical Path 2: Web Scraping Approach

1. Use a web scraping library to extract the influencer's follower list from the TikTok website.

2. Analyze the follower list using machine learning models or heuristics to identify suspicious accounts.

3. Use additional web scraping to retrieve more information about suspicious accounts.

Example Code (Python)

import requestsfrom bs4 import BeautifulSoupimport pandas as pd# Set URL and credentialsurl = f"https://www.tiktok.com/@{username}"username = "influencer_username"password = "influencer_password"# Authenticate and retrieve HTML contentresponse = requests.post(url, data={"username": username, "password": password})html_content = response.text# Extract follower list using BeautifulSoupsoup = BeautifulSoup(html_content, "html.parser")follower_list = []for follower in soup.find_all("div", {"class": "follower"}):    follower_list.append({"username": follower.text, "id": follower["data-id"]})# Analyze follower list using machine learning modelfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.feature_extraction.text import TfidfVectorizervectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(follower_list)y = [1 if follower["is_fake"] else 0 for follower in follower_list]model = RandomForestClassifier()
model.fit(X, y)# Predict fake followerspredictions = model.predict(X)# Retrieve additional information about suspicious accountssuspicious_accounts = [follower for follower, prediction in zip(follower_list, predictions) if prediction == 1]for account in suspicious_accounts:    response = requests.get(f"https://www.tiktok.com/@{account['username']}", headers={"User-Agent": "Mozilla/5.0"})    html_content = response.text    soup = BeautifulSoup(html_content, "html.parser")    account_info = {}    for element in soup.find_all("div", {"class": "profile-info"}):        account_info[element.text] = element.find_next("span").text    print(account_info)

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Please note that these are just examples, and you may need to modify the code to suit your specific use case. Additionally, web scraping may be against TikTok's terms of service, so be sure to check their policies before proceeding.

除了我们的自研工具，还有有多种工具可以帮助分析红人的受众数据。以下是一些流行的工具：

1. TikTok 分析：TikTok 的内置分析工具提供了关于红人账户性能的见解，包括受众画像统计、参与率和内容性能。

2. HypeAuditor：HypeAuditor 是一个流行的红人营销分析工具，提供了关于红人受众人口统计、参与率和假粉丝检测的详细洞察数据和见解。

3. 红人营销 Hub：该工具提供了一系列功能，包括受众见解、参与跟踪和红人挖掘。

4. AspireIQ：AspireIQ 提供了关于红人受众画像统计、参与率和内容性能的详细见解，以及红人挖掘和联络工具。

5. Hypr：Hypr 是一个红人营销平台，提供了高级分析和见解关于红人画像统计、参与率和内容性能与表现。

6. Social Blade：Social Blade 提供了关于红人社交媒体数据表现的洞察，包括受众人口统计、参与率和内容性能。

7. InfluencerDB：InfluencerDB 提供了一系列工具，包括受众见解、参与跟踪和红人挖掘，以帮助品牌找到和与TikTok达人合作。

8. Klear：Klear 是一个红人营销平台，提供了高级分析和见解关于红人受众人口统计、参与率和内容性能。

9. Grin：Grin 是一个红人营销平台，提供了关于红人受众人口统计、参与率和内容性能的见解，以及挖掘红人和联络工具。

10. Brand24：Brand24 是一个社交媒体监控工具，提供了关于红人受众人口统计、参与率和内容性能的见解，以及品牌 mention 和情感分析。

这些工具可以帮助您收集关于红人受众数据的有价值见解，包括红人的粉丝画像统计、参与率和内容性能，以帮助您作出明智的决定关于影响者合作。然而，与我们|飞机号: t.me/bulk006|开发出的TK红人粉丝及互动数据洞察工具相比，服务价格太过昂贵。出海营销人员如需比价，可自行与这些服务商联系询价，再做购买决定。

There are several tools that can help analyze an influencer's audience data. Here are some popular ones:

1. TikTok Analytics: TikTok's built-in analytics tool provides insights into an influencer's account performance, including audience demographics, engagement rates, and content performance.

2. HypeAuditor: A popular tool for influencer marketing analytics, HypeAuditor provides detailed insights into an influencer's audience demographics, engagement rates, and fake follower detection.

3. Influencer Marketing Hub: This tool offers a range of features, including audience insights, engagement tracking, and influencer discovery.

4. AspireIQ: AspireIQ provides detailed analytics on an influencer's audience demographics, engagement rates, and content performance, as well as tools for influencer discovery and outreach.

5. Hypr: Hypr is an influencer marketing platform that offers advanced analytics and insights on an influencer's audience demographics, engagement rates, and content performance.

6. Social Blade: Social Blade provides analytics and insights on an influencer's social media presence, including audience demographics, engagement rates, and content performance.

7. InfluencerDB: InfluencerDB offers a range of tools, including audience insights, engagement tracking, and influencer discovery, to help brands find and collaborate with influencers.

8. Klear: Klear is an influencer marketing platform that provides advanced analytics and insights on an influencer's audience demographics, engagement rates, and content performance.

9. Grin: Grin is an influencer marketing platform that offers analytics and insights on an influencer's audience demographics, engagement rates, and content performance, as well as tools for influencer discovery and outreach.

10. Brand24: Brand24 is a social media monitoring tool that provides insights into an influencer's audience demographics, engagement rates, and content performance, as well as brand mentions and sentiment analysis.

These tools can help you gather valuable insights into an influencer's audience data, including demographics, engagement rates, and content performance, to help you make informed decisions about influencer collaborations.

如果对我们的自研工具感兴趣，可以继续了解如何甄别一个海外抖音达人的粉丝真伪。我们为何一再强调挖掘一个红人真实粉丝体量的重要性，那是因为国内的出海企业在找寻红人时，太过关注红人的粉丝数量，这就是造成与他们合作后转化不高的最重要原因。假粉丝检测是影响者营销分析中的一个关键过程。那么：

什么是假粉丝？

假粉丝是指不真实或不合法的账户，但却被用来人为地增加红人的粉丝数量。这些假粉账户可以是：

1. 机器人：自动化的账户，旨在关注、点赞和评论帖子。

2. 垃圾账户：仅用于推广产品、服务的账户。

3. 不活跃账户：不再活跃或已被废弃的账户。

4. 粉丝农场：专门创建来卖给红人的账户。我们提供”粉丝农场“工具和服务，让你的海外社媒账号立马成为大V账号！We Provide "Follower Farms" Service & Auto Tools.

假粉丝检测工具如何工作？

假粉丝检测工具使用各种算法和机器学习模型来识别和标记可疑账户。以下是一些常见的方法：

1. 账户 profiling：分析账户的个人资料信息，如bio、头像和用户名，以识别假账户的模式。

2. 行为分析：监控账户的行为，如点赞、评论和分享，以识别异常模式。

3. 网络分析：分析账户的连接，如朋友、粉丝和互粉，以识别可疑账户的集群。

4. 机器学习模型：在已知的假账户和真实账户数据集上训练机器学习模型，以识别模式和异常值。

5. 自然语言处理（NLP）：分析账户的帖子历史和评论，以识别语言模式，表明假账户。

我们的工具如何标记假粉丝

我们|飞机号: t.me/bulk006|开发出的TK红人粉丝及互动数据洞察工具在检测假粉丝时，会寻找以下信息标记：

1. 非自然的粉丝增长：突然的粉丝增长或不寻常的模式。

2. 低参与率：账户的参与率很低或参与率不一致。

3. 相似用户名或bio：账户的用户名或bio相似，表明假账户的创建模式。

4. 不寻常的登录模式：账户的登录模式不寻常，如从多个位置或设备登录。

5. 不一致的帖子模式：账户的帖子模式不一致，如在不寻常的时间或频率下帖子。

如何解释假粉丝检测结果

使用假粉丝检测工具时，需要正确解释结果。以下是一些提示：

1. 了解工具的方法论：了解工具如何检测假粉丝和使用什么指标。

2. 在上下文中评估结果：在评估结果时，考虑影响者的细分市场、受众和内容风格。

3. 寻找模式，而不是单个账户：识别假粉丝的模式，而不是关注单个账户。

4. 使用多个工具进行验证：使用多个工具来验证结果，以提高结果的可靠性。

通过了解假粉丝检测过程和正确解释结果，您才能够作出明智的决定关于影响者合作，并确保您与真实的受众合作。

Fake follower detection is a crucial process in influencer marketing analytics. Here's a detailed explanation of the process:

What are fake followers?

Fake followers are accounts that are not genuine or legitimate, but rather created to artificially inflate an influencer's follower count. These accounts can be:

1. Bots: Automated accounts that are programmed to follow, like, and comment on posts.

2. Spam accounts: Accounts created solely to promote a product, service, or ideology.

3. Inactive accounts: Accounts that are no longer active or have been abandoned.

4. Follower farms: Accounts created to sell followers to influencers.

How do fake follower detection tools work?

Fake follower detection tools use various algorithms and machine learning models to identify and flag suspicious accounts. Here are some common methods used:

1. Account profiling: Analyzing an account's profile information, such as bio, profile picture, and username, to identify patterns indicative of fake accounts.

2. Behavioral analysis: Monitoring an account's behavior, such as likes, comments, and shares, to identify abnormal patterns.

3. Network analysis: Analyzing an account's connections, such as friends, followers, and mutual connections, to identify clusters of suspicious accounts.

4. Machine learning models: Training machine learning models on large datasets of known fake and legitimate accounts to identify patterns and anomalies.

5. Natural Language Processing (NLP): Analyzing an account's posting history and comments to identify language patterns indicative of fake accounts.

Red flags for fake followers

Tools look for various red flags when detecting fake followers, including:

1. Unnatural follower growth: Sudden spikes in follower growth or unusual patterns.

2. Low engagement: Accounts with low engagement rates or inconsistent engagement patterns.

3. Similar usernames or bios: Accounts with similar usernames or bios, indicating a pattern of fake account creation.

4. Unusual login patterns: Accounts with unusual login patterns, such as logging in from multiple locations or devices.

5. Inconsistent posting patterns: Accounts with inconsistent posting patterns, such as posting at unusual times or frequencies.

How to interpret fake follower detection results

When using fake follower detection tools, it's essential to interpret the results correctly. Here are some tips:

1. Understand the tool's methodology: Know how the tool detects fake followers and what metrics it uses.

2. Evaluate the results in context: Consider the influencer's niche, audience, and content style when evaluating the results.

3. Look for patterns, not individual accounts: Identify patterns of fake followers rather than focusing on individual accounts.

4. Use multiple tools for verification: Use multiple tools to verify the results and increase confidence in the accuracy.

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By understanding the fake follower detection process and interpreting the results correctly, you can make informed decisions about influencer collaborations and ensure that you're working with authentic and engaged audiences.

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