Explore the fascinating world of TikTok with our comprehensive TikTok User Profiles Dataset. Whether you're a marketer, researcher, or enthusiast, this dataset provides a wealth of information on public TikTok profiles, allowing you to extract valuable business and non-business insights. You have the flexibility to purchase the complete dataset or tailor it to your specific needs by utilizing a range of filtering options.

Key Data Points:

• Timestamp
• Account name
• Nickname
• Bio
• Average engagement score
• Creation date
• Verification status
• Likes count
• Followers count
• External link in bio
• and many more

Popular Use Cases: Unleash the potential of this dataset for a variety of applications, including:

Sentiment Analysis: Gain deep insights into user sentiment by analyzing profiles' content, engagement, and interactions. Brand Monitoring: Track mentions of your brand, products, or services across TikTok, understanding how users perceive and engage with your offerings. Influencer Marketing: Identify potential influencers by assessing their follower count, engagement, and overall impact, helping you make informed collaboration decisions. Audience Insights: Understand your target audience by examining user bios, locations, and other profile details, aiding in tailoring your content and strategies.

Source: BrightData

Our TikTok Influencer Dataset provides comprehensive insights into influencer profiles, audience engagement, and market impact. This dataset is ideal for brands, marketers, and researchers looking to identify top-performing influencers, analyze engagement metrics, and optimize influencer marketing strategies on TikTok.

Key Features:

  Influencer Profiles: Access detailed influencer data, including profile name, bio, profile picture, and direct profile URL.
  Follower & Engagement Metrics: Track key performance indicators such as follower count, engagement rate, and interaction levels.
  Monetization Insights: Analyze influencer earnings with Gross Merchandise Value (GMV) and currency details.
  Category & Niche Segmentation: Identify influencers based on their associated product categories to match brand campaigns with relevant audiences.
  Contact Information: Retrieve available influencer email addresses for direct outreach and collaboration.


Use Cases:

  Influencer Discovery & Marketing: Find high-performing TikTok influencers for brand partnerships and sponsored campaigns.
  Competitive Analysis: Compare influencer engagement rates and audience reach to optimize marketing strategies.
  Market Research & Trend Analysis: Identify emerging influencers and track content trends within different product categories.
  Performance Benchmarking: Evaluate influencer success based on GMV, engagement rate, and follower growth.
  Lead Generation & Outreach: Use available contact details to connect with influencers for collaborations and brand promotions.



  Our TikTok Influencer Dataset is available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Gain valuable insights into the TikTok influencer landscape and enhance your marketing strategies with high-quality, structured data.

This dataset tracks influencer marketing campaigns across major social media platforms, providing a robust foundation for analyzing campaign effectiveness, engagement, reach, and sales outcomes. Each record represents a unique campaign and includes details such as the campaign’s platform (Instagram, YouTube, TikTok, Twitter), influencer category (e.g., Fashion, Tech, Fitness), campaign type (Product Launch, Brand Awareness, Giveaway, etc.), start and end dates, total user engagements, estimated reach, product sales, and campaign duration. The dataset structure supports diverse analyses, including ROI calculation, campaign benchmarking, and influencer performance comparison.

Columns: - campaign_id: Unique identifier for each campaign
- platform: Social media platform where the campaign ran
- influencer_category: Niche or industry focus of the influencer
- campaign_type: Objective or style of the campaign
- start_date, end_date: Campaign time frame
- engagements: Total user interactions (likes, comments, shares, etc.)
- estimated_reach: Estimated number of unique users exposed to the campaign
- product_sales: Number of products sold as a result of the campaign
- campaign_duration_days: Duration of the campaign in days

Getting Started with the Data

1. Load and Inspect the Dataset

import pandas as pd

df = pd.read_csv('influencer_marketing_roi_dataset.csv', parse_dates=['start_date', 'end_date'])
print(df.head())
print(df.info())

2. Basic Exploration

# Overview of campaign types and platforms
print(df['campaign_type'].value_counts())
print(df['platform'].value_counts())

# Summary statistics
print(df[['engagements', 'estimated_reach', 'product_sales']].describe())

3. Engagement and Sales Analysis

# Average engagements and sales by platform
platform_stats = df.groupby('platform')[['engagements', 'product_sales']].mean()
print(platform_stats)

# Top influencer categories by product sales
top_categories = df.groupby('influencer_category')['product_sales'].sum().sort_values(ascending=False)
print(top_categories)

4. ROI Calculation Example

# Assume a fixed campaign cost for demonstration
df['campaign_cost'] = 500 + df['estimated_reach'] * 0.01 # Example formula

# Calculate ROI: (Revenue - Cost) / Cost
# Assume each product sold yields $40 revenue
df['revenue'] = df['product_sales'] * 40
df['roi'] = (df['revenue'] - df['campaign_cost']) / df['campaign_cost']

# View campaigns with highest ROI
top_roi = df.sort_values('roi', ascending=False).head(10)
print(top_roi[['campaign_id', 'platform', 'roi']])

5. Visualizing Campaign Performance

import matplotlib.pyplot as plt
import seaborn as sns

# Engagements vs. Product Sales scatter plot
plt.figure(figsize=(8,6))
sns.scatterplot(data=df, x='engagements', y='product_sales', hue='platform', alpha=0.6)
plt.title('Engagements vs. Product Sales by Platform')
plt.xlabel('Engagements')
plt.ylabel('Product Sales')
plt.legend()
plt.show()

# Average ROI by Influencer Category
category_roi = df.groupby('influencer_category')['roi'].mean().sort_values()
category_roi.plot(kind='barh', color='teal')
plt.title('Average ROI by Influencer Category')
plt.xlabel('Average ROI')
plt.show()

6. Time-Based Analysis

# Campaigns over time
df['month'] = df['start_date'].dt.to_period('M')
monthly_sales = df.groupby('month')['product_sales'].sum()
monthly_sales.plot(figsize=(10,4), marker='o', title='Monthly Product Sales from Influencer Campaigns')
plt.ylabel('Product Sales')
plt.show()

Use Cases

• ROI Analysis: Quantify the return on investment for influencer campaigns across platforms and categories.
• Campaign Benchmarking: Compare campaign performance by type, influencer niche, or platform.
• Trend Analysis: Track engagement, reach, and sales trends over time.
• Influencer Selection: Identify high-performing influencer categories and campaign types for future partnerships.

This dataset contains metadata from TikTok videos in the beauty and personal care niche. The data is structured to analyze video performance, user interaction, and content features, with specific metrics such as play count, share count, comments, and video details. It also includes user-level attributes like follower count, region, and engagement metrics, enabling analysis of influencer activity and content trends in this domain.

Source: Public TikTok profiles collected via Apify (a web scraping tools).

Inspiration: Explore how users engage with TikTok content and profiles. Use this data to create predictive models or track trends in social media engagement.

Use our TikTok profiles dataset to extract business and non-business information from complete public profiles and filter by account name, followers, create date, or engagement score. You may purchase the entire dataset or a customized subset depending on your needs. Popular use cases include sentiment analysis, brand monitoring, influencer marketing, and more. The TikTok dataset includes all major data points: timestamp, account name, nickname, bio,average engagement score, creation date, is_verified,l ikes, followers, external link in bio, and more. Get your TikTok dataset today!

This dataset documents the empirical material used in a study on the activity of lay Catholic influencers on TikTok in Poland. The temporal scope covers one full liturgical year in the Catholic Church: from December 1, 2024 to November 23, 2025.
The dataset includes 10 profiles and 1,790 video materials (TikToks) published within the analyzed period. The data were collected using desk research and are based exclusively on publicly available content. The sampling procedure was two-stage: (1) identification of content using a set of hashtags related to Catholic religion, (2) selection of profiles based on the number of followers, quality and creativity of publications, and recognition within religiously engaged communities.
The dataset does not include video files. This is due to the large volume of the material and restrictions related to further redistribution of content published on TikTok. Instead, it provides data that allow for clear identification of the sources (profiles with links), enabling access to the analyzed materials in their original publication environment.
The dataset has a documentary and methodological character. It enables verification of the empirical basis of the study and reconstruction of the sampling procedure.

Please upvote if you like this dataset

TikTok, known in China as Douyin (Chinese: 抖音; pinyin: Dǒuyīn), is a short-form video hosting service owned by Chinese company ByteDance. It hosts a variety of short-form user videos, from genres like pranks, stunts, tricks, jokes, dance, and entertainment with durations from 15 seconds to ten minutes. TikTok is an international version of Douyin, which was originally released in the Chinese market in September 2016. TikTok was launched in 2017 for iOS and Android in most markets outside of mainland China; however, it became available worldwide only after merging with another Chinese social media service, Musical.ly, on 2 August 2018.

TikTok and Douyin have almost the same user interface but no access to each other's content. Their servers are each based in the market where the respective app is available. The two products are similar, but features are not identical. Douyin includes an in-video search feature that can search by people's faces for more videos of them and other features such as buying, booking hotels and making geo-tagged reviews. Since its launch in 2016, TikTok and Douyin rapidly gained popularity in virtually all parts of the world. TikTok surpassed 2 billion mobile downloads worldwide in October 2020.

In this dataset you will find the details about top 1000 tiktokers all over the world.

Gain a competitive edge with our comprehensive Advertising Dataset, designed for marketers, analysts, and businesses to track ad performance, analyze competitor strategies, and optimize campaign effectiveness.

Dataset Features

Sponsored Posts & Ads: Access structured data on paid advertisements, including post content, engagement metrics, and platform details. Competitor Advertising Insights: Extract data on competitor campaigns, influencer partnerships, and promotional strategies. Audience Engagement Metrics: Analyze likes, shares, comments, and impressions to measure ad effectiveness. Multi-Platform Coverage: Track ads across LinkedIn, Instagram, Facebook, TikTok, Twitter (X), Pinterest, and more. Historical & Real-Time Data: Retrieve historical ad performance data or access continuously updated records for real-time insights.

Customizable Subsets for Specific Needs Our Advertising Dataset is fully customizable, allowing you to filter data based on platform, ad type, engagement levels, or specific brands. Whether you need broad coverage for market research or focused data for ad optimization, we tailor the dataset to your needs.

Popular Use Cases

Targeted Advertising & Audience Segmentation: Refine ad targeting by analyzing competitor content, audience demographics, and engagement trends. Campaign Performance Analysis: Measure ad effectiveness by tracking engagement metrics, reach, and conversion rates. Competitive Intelligence: Monitor competitor ad strategies, influencer collaborations, and promotional trends. Market Research & Trend Forecasting: Identify emerging advertising trends, high-performing content types, and consumer preferences. AI & Predictive Analytics: Use structured ad data to train AI models for automated ad optimization, sentiment analysis, and performance forecasting.

Whether you're optimizing ad campaigns, analyzing competitor strategies, or refining audience targeting, our Advertising Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

📱 About Dataset Overview This Social Media Engagement Dataset contains comprehensive engagement metrics from 5,000 social media posts across six major platforms: Instagram, Twitter, Facebook, LinkedIn, TikTok, and YouTube. The dataset spans over 2 years (2024-2025) and provides valuable insights into content performance, audience engagement patterns, and influencer analytics.

Dataset Contents The dataset includes 20 detailed features covering various aspects of social media engagement:

Post Information Post_ID: Unique identifier for each post Timestamp: Date and time when the post was published Platform: Social media platform (Instagram, Twitter, Facebook, LinkedIn, TikTok, YouTube) Content_Type: Type of content (Photo, Video, Reel, Tweet, Story, etc.) Category: Content category (Technology, Fashion, Food, Travel, Fitness, Education, Entertainment, Business, Lifestyle, Gaming, Health, Sports) Engagement Metrics Likes: Number of likes/reactions received Comments: Number of comments on the post Shares: Number of shares/retweets/reposts Views: Total number of views Saves: Number of bookmarks/saves Engagement_Rate: Calculated engagement rate percentage Account Information Follower_Count: Number of followers of the account Influencer_Tier: Classification (Nano, Micro, Mid-tier, Macro) Is_Verified: Whether the account is verified (True/False) Content Characteristics Hashtag_Count: Number of hashtags used Content_Length: Length in characters (text) or seconds (video) Sentiment: Sentiment analysis (Positive, Neutral, Negative) Has_Media: Whether post contains media (True/False) Temporal Features Hour_of_Day: Hour when the post was published (0-23) Day_of_Week: Day of the week (Monday-Sunday) Use Cases This dataset is perfect for:

📊 Predictive Analytics: Build ML models to predict engagement rates 📈 Data Visualization: Create insightful dashboards and charts 🤖 Machine Learning: Classification, regression, and clustering tasks ⏰ Time Series Analysis: Analyze posting patterns and optimal timing 🎯 Content Strategy: Optimize content strategy based on data insights 🔍 Sentiment Analysis: Study correlation between sentiment and engagement 📱 Platform Comparison: Compare performance across different platforms 💼 Influencer Marketing: Analyze influencer tier performance Technical Details Format: CSV Size: ~651 KB Rows: 5,000 Columns: 20 Time Period: January 2024 - December 2025 Missing Values: None Potential Research Questions What time of day generates the most engagement? Which platform has the highest engagement rates? How does content type affect performance? Does verified status impact engagement? What's the optimal hashtag count? How does sentiment correlate with engagement? Notes Engagement metrics are platform-realistic and proportional All data is synthetically generated for educational and research purposes Suitable for beginners and advanced data scientists

Important : its a 3 month gap data Starting from March 2022 to Dec 2022

Influencers are categorized by the number of followers they have on social media. They include celebrities with large followings to niche content creators with a loyal following on social-media platforms such as YouTube, Instagram, Facebook, and Twitter.Their followers range in number from hundreds of millions to 1,000. Influencers may be categorized in tiers (mega-, macro-, micro-, and nano-influencers), based on their number of followers.

Businesses pursue people who aim to lessen their consumption of advertisements, and are willing to pay their influencers more. Targeting influencers is seen as increasing marketing's reach, counteracting a growing tendency by prospective customers to ignore marketing.

Marketing researchers Kapitan and Silvera find that influencer selection extends into product personality. This product and benefit matching is key. For a shampoo, it should use an influencer with good hair. Likewise, a flashy product may use bold colors to convey its brand. If an influencer is not flashy, they will clash with the brand. Matching an influencer with the product's purpose and mood is important.

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This dataset provides information about the top 100 TikTok accounts worldwide in 2025, ranked based on their popularity. The data has been manually curated and includes essential metrics that reflect the performance and engagement of TikTok creators. It can be used for various purposes such as trend analysis, content strategy development, or understanding the growth of social media influencers.

Features Included: Rank: Ranking based on follower count. Uploads: The total number of videos uploaded by the account. Views: Total views generated by the account's videos. Followers: Number of followers for the account. Following: Number of accounts the user is following. Username: The username of the TikTok account.

This dataset is suitable for data analysis, machine learning model development, and studying trends in social media content.

This dataset provides comprehensive social media profile links discovered through real-time web search. It includes profiles from major social networks like Facebook, TikTok, Instagram, Twitter, LinkedIn, Youtube, Pinterest, Github and more. The data is gathered through intelligent search algorithms and pattern matching. Users can leverage this dataset for social media research, influencer discovery, social presence analysis, and social media marketing. The API enables efficient discovery of social profiles across multiple platforms. The dataset is delivered in a JSON format via REST API.