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

This dataset captures the pulse of viral social media trends across TikTok, Instagram, Twitter, and YouTube. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

• Trend analysis 🔍
• Sentiment modeling 💭
• Understanding influencer marketing 📈

Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

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!

📱 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

A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. Videos and related metadata were downloaded using a third-party TikTok Scraper using the search term #coronavirus. Videos were reviewed for content and data were entered on a spreadsheet.

Public-safe TikTok beauty brand monitoring sample produced by Octoparse Managed Data Service. The dataset contains video-level engagement records and comment-level signals for brand intelligence, creator monitoring, social listening, and campaign analysis. It includes schema documentation, a data dictionary, workflow statistics, and separated video/comment tables. Methodology: public or properly authorized social content is collected, timestamps and engagement metrics are normalized, comments are linked to videos, and personally sensitive identifiers are removed or hashed. Limitations: this is a static sample, not a complete TikTok firehose; platform metrics can change after collection and sample records are curated for public-safe demonstration. Enterprise Data Pipelines & Production-Grade Delivery: Octoparse can deliver monitored social feeds to API, files, Snowflake, AWS S3, or BigQuery with custom schemas and QA-backed operations. Request a Custom Data Pipeline Workshop on Octoparse Data Service: https://www.octoparse.com/data-service

Need TikTok data that matches your research or business case? DataOx collects a custom TikTok dataset around the videos, creators, hashtags, comments, and engagement signals that matter to your use case.

This dataset supports research on how engagement with social media (Instagram and TikTok) was related to problematic social media use (PSMU) and mental well-being. There are three different files. The SPSS and Excel spreadsheet files include the same dataset but in a different format. The SPSS output presents the data analysis in regard to the difference between Instagram and TikTok users.

This dataset contains comprehensive information about TikTok posts, originally fetched from RapidAPI. It provides valuable insights into various aspects of TikTok content, including details about the videos, their creators, and audience engagement metrics.

Here's a breakdown of the columns included in this dataset:

video_id: A unique identifier for each TikTok video. author: The username or handle of the TikTok account that posted the video. description: The textual description or caption provided by the creator for the video. (Note: This column contains some missing values.) likes: The number of likes the video has received. comments: The number of comments on the video. shares: The number of times the video has been shared. plays: The total number of plays or views the video has accumulated. (Note: This column contains some missing values.) hashtags: A list of hashtags used in the video's description, which helps categorize content and improve discoverability. (Note: This column contains some missing values.) music: Information about the background music or sound used in the video. create_time: The timestamp indicating when the video was created or published. (Note: This column contains some missing values.) video_url: The direct URL to the TikTok video. fetch_time: The timestamp when the data for the video was fetched from the API. (Note: This column has a high number of missing values.) views: Another metric for the number of views. (Note: This column has a high number of missing values and appears to overlap with plays.) posted_time: The time the video was posted. (Note: This column has a high number of missing values and appears to overlap with create_time.) Potential Uses of This Dataset:

Content Analysis: Analyze popular TikTok content by examining descriptions, hashtags, and engagement metrics. Trend Identification: Identify trending topics, music, and creators on TikTok. Audience Engagement Studies: Understand how different types of content generate likes, comments, shares, and plays. Creator Analysis: Study the posting habits and performance of various TikTok creators. Social Media Research: Conduct research on the dynamics of content dissemination and user interaction on short-form video platforms. Notes on Data Quality:

The description, plays, hashtags, and create_time columns have some missing values, which may require handling (e.g., imputation or removal) depending on your analysis. The fetch_time, views, and posted_time columns are largely empty, suggesting they may not be reliable for comprehensive analysis. It is recommended to primarily rely on create_time for timestamps and plays for engagement metrics. This dataset can be a valuable resource for anyone looking to explore the vast and dynamic world of TikTok content and user engagement.

Use our TikTok Shop dataset to extract detailed e-commerce insights, including product names, prices, discounts, seller details, product descriptions, categories, customer ratings, and reviews. You may purchase the entire dataset or a customized subset tailored to your needs. Popular use cases include trend analysis, pricing optimization, customer behavior studies, and marketing strategy refinement. The TikTok Shop dataset includes key data points: product performance metrics, user engagement, customer reviews, and more. Unlock the potential of TikTok's shopping platform today with our comprehensive dataset!

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 contains the data used for the article titled “Algorithm Awareness and User Motivation as Predictors of TikTok Engagement Among Generation Z in South Jakarta.” The study examines how various user motivations, based on the Uses and Gratifications framework and levels of algorithm awareness influence engagement behavior on TikTok among Gen Z users.

The dataset includes: • Motivation variables (information seeking, entertainment, social interaction, self-expression) • Engagement behavior indicators (consumption, contribution, creation) • Algorithm awareness scores (measured using the AMCA scale) • Final processed dataset used for statistical analysis (SMART-PLS)

All data were collected through an online questionnaire and are shared for transparency, replication, and further academic research on social media engagement and algorithmic literacy.

TikTok Creator and Video Engagement (200K)

This release contains 209,543 TikTok videos from 1,872 creators with daily engagement and follower statistics, covering videos posted from 2024-06-24 to 2024-11-09. The release contains derived video-content fields. Audio transcripts, screenshots, textual data, and music metadata were used to generate short video summaries; topic labels and emotion scores were then derived from those summaries using machine learning models. These… See the full description on the dataset page: https://huggingface.co/datasets/lingbow/tiktok-video-engagement-200k.

This dataset contains the data used for the article titled “Algorithm Awareness and User Motivation as Predictors of TikTok Engagement Among Generation Z in South Jakarta.” The study examines how various user motivations, based on the Uses and Gratifications framework and levels of algorithm awareness influence engagement behavior on TikTok among Gen Z users. The dataset includes: • Motivation variables (information seeking, entertainment, social interaction, self-expression) • Engagement behavior indicators (consumption, contribution, creation) • Algorithm awareness scores (measured using the AMCA scale) • Final processed dataset used for statistical analysis (SMART-PLS) All data were collected through an online questionnaire and are shared for transparency, replication, and further academic research on social media engagement and algorithmic literacy.

🚀TikTok Shop Viral Video Sales, Attribution & CAC Analytics Dataset

📊 100,000 Transactions • 180+ Features • Creator Economics • Multi-Touch Attribution • CAC Optimization Traditional e-commerce analytics models are failing. In modern social commerce ecosystems like TikTok Shop, virality, creator authority, and micro-retention curves dictate revenue—not standard search traffic.

This dataset provides a production-grade analytical environment containing 100,000 granular observations and 180+ features. It bridges the gap between unstructured social media video engagement (hooks, rewatches, drop-offs) and enterprise financial performance (ROAS, CPA, Net Profit Margins, Multi-Touch Attribution).

💡 Why This Dataset Beats Standard Datasets End-to-End Customer Path: Tracks the complete journey from video impression and 3-second hook to product conversion and return risk.

Modern Social Commerce Vectors: Includes influencer tiering, affiliate tracking, and viral video hook dynamics that traditional retail datasets ignore.

Advanced Financial Metrics: Measures true net margins after creator commissions, platform fees, shipping costs, and customer acquisition expenses.

Multi-Touch Attribution Engine: Features First-Touch, Last-Touch, Linear, Position-Based, and Data-Driven revenue attribution models.

🧬 Dataset Schema & Feature Vectors (180+ Features) The data is structured into 6 interconnected analytical vectors:

1. 🎬 Video Performance & Content Hook Dynamics Video runtime, unique impressions, likes, shares, saves, retention curves at 3s and 10s, exact drop-off second, rewatch ratio, viral score, and hook clarity ratings.

2. 👤 Creator & Affiliate Intelligence Creator tier (Micro, Mid, Mega), follower count, engagement velocity, historical conversion authority, affiliate commission tier, and influencer ROI impact.

3. 💰 Financials & Unit Economics Gross revenue, net profit margin, Cost Per Acquisition (CPA), Customer Acquisition Cost (CAC), Return on Ad Spend (ROAS), platform fees, and marketing ROI.

4. 📈 Multi-Touch Attribution Models First-click revenue, last-click revenue, linear attribution, time-decay attribution, position-based model, and machine learning attribution confidence scores.

5. 🛒 Funnel & Traffic Analytics Product page clicks, add-to-cart rate, checkout abandonment rate, profile visits, traffic origin, and scroll depth.

6. 📦 Operations, Logistics & Profitability Shipping duration, delivery failure rate, return reason codes, processing cost, refund impact, and net contribution margin.

🎯 Machine Learning & Predictive Modeling Targets Use this dataset to build models targeting real-world business challenges:

Is_Viral (Binary Classification): Predict whether a video will achieve viral reach based on early 3-second hook metrics.

Net_Profit_Margin (Regression): Predict true profitability after subtracting creator commissions, return rates, and CAC.

Attribution_Model_Winner (Multi-Class Classification): Classify which attribution model best reflects true conversion impact based on customer path complexity.

Customer_Return_Probability (Predictive Modeling): Predict item return risk using product category, video claims, and shipping delays.

🚀 Quick-Start Analysis Snippet (Python)

import pandas as pd import numpy as np

Load dataset

df = pd.read_csv("tiktok_shop_viral_analytics_100k.csv")

1. Calculate Virality vs. Net Profitability Ratio

df['CAC_Efficiency'] = df['Net_Revenue'] / (df['CAC'] + 1e-5)

2. Compare ROAS across Creator Tiers

creator_perf = df.groupby('Creator_Tier')[['ROAS', 'Net_Profit_Margin', 'CAC']].mean() print("=== Creator Economics Overview ===") print(creator_perf)

3. Attribution Discrepancy Index

df['Attribution_Delta'] = df['Data_Driven_Revenue'] - df['Last_Click_Revenue'] print(" === Revenue Misattributed by Last-Click Model ===") print(df['Attribution_Delta'].describe())

🛠️ Recommended Portfolio Projects Project 1: Executive Creator Economics Dashboard (Power BI / Tableau) Visualize ROAS vs. Creator Commission rates across 100K transactions.

Project 2: Multi-Touch Attribution Engine (Python / SQL) Compare First-Touch vs. Data-Driven Attribution to reallocate marketing spend.

Project 3: Viral Hook & Drop-off Predictor (XGBoost / LightGBM) Predict video conversion failure using early 3-second viewer retention signals.

📌 Community Engagement & License License: CC0: Public Domain (Free for personal, academic, and commercial portfolio use).

Support: If you find this dataset valuable for your research, portfolio, or machine learning experiments, please ⭐ UPVOTE to support future dataset releases!

Showcase:Feel free to share your EDA notebooks and dashboard links in the Code and Discussion tabs!

Tiktok network graph with 5,638 nodes and 318,986 unique links, representing up to 790,599 weighted links between labels, using Gephi network analysis software. Source of: Peña-Fernández, Simón, Larrondo-Ureta, Ainara, & Morales-i-Gras, Jordi. (2022). Current affairs on TikTok. Virality and entertainment for digital natives. Profesional De La Información, 31(1), 1–12. https://doi.org/10.5281/zenodo.5962655 Abstract: Since its appearance in 2018, TikTok has become one of the most popular social media platforms among digital natives because of its algorithm-based engagement strategies, a policy of public accounts, and a simple, colorful, and intuitive content interface. As happened in the past with other platforms such as Facebook, Twitter, and Instagram, various media are currently seeking ways to adapt to TikTok and its particular characteristics to attract a younger audience less accustomed to the consumption of journalistic material. Against this background, the aim of this study is to identify the presence of the media and journalists on TikTok, measure the virality and engagement of the content they generate, describe the communities created around them, and identify the presence of journalistic use of these accounts. For this, 23,174 videos from 143 accounts belonging to media from 25 countries were analyzed. The results indicate that, in general, the presence and impact of the media in this social network are low and that most of their content is oriented towards the creation of user communities based on viral content and entertainment. However, albeit with a lesser presence, one can also identify accounts and messages that adapt their content to the specific characteristics of TikTok. Their virality and engagement figures illustrate that there is indeed a niche for current affairs on this social network.

Tiktok network graph with 5,638 nodes and 318,986 unique links, representing up to 790,599 weighted links between labels, using Gephi network analysis software. Source of: Peña-Fernández, Simón, Larrondo-Ureta, Ainara, & Morales-i-Gras, Jordi. (2022). Current affairs on TikTok. Virality and entertainment for digital natives. Profesional De La Información, 31(1), 1–12. https://doi.org/10.5281/zenodo.5962655 Abstract: Since its appearance in 2018, TikTok has become one of the most popular social media platforms among digital natives because of its algorithm-based engagement strategies, a policy of public accounts, and a simple, colorful, and intuitive content interface. As happened in the past with other platforms such as Facebook, Twitter, and Instagram, various media are currently seeking ways to adapt to TikTok and its particular characteristics to attract a younger audience less accustomed to the consumption of journalistic material. Against this background, the aim of this study is to identify the presence of the media and journalists on TikTok, measure the virality and engagement of the content they generate, describe the communities created around them, and identify the presence of journalistic use of these accounts. For this, 23,174 videos from 143 accounts belonging to media from 25 countries were analyzed. The results indicate that, in general, the presence and impact of the media in this social network are low and that most of their content is oriented towards the creation of user communities based on viral content and entertainment. However, albeit with a lesser presence, one can also identify accounts and messages that adapt their content to the specific characteristics of TikTok. Their virality and engagement figures illustrate that there is indeed a niche for current affairs on this social network.

The dataset was collected through an anonymous online survey conducted in Bulgaria from 30 November 2025 to 7 May 2026. It contains 295 complete responses, of which 268 met the predefined eligibility criteria of TikTok use and exposure to fashion-brand content. Participants evaluated TikTok fashion-brand content using 16 Likert-scale indicators measuring content involvement, brand awareness, brand engagement, and purchase-related intentions, supplemented by demographic, platform-use, and open-ended questions. The data were analyzed using descriptive statistics, PLS path modeling, clustering, cross-validated machine learning, demographic analysis, manually verified open-response coding, and multi-criteria synthesis. The results indicate that content involvement is strongly associated with brand awareness and engagement, while brand engagement has the strongest direct association with purchase-related intentions.

Dataset collected for the study "Between Influence and Disinformation: Personal Branding and Responsible Communication of Influencers on TikTok." It contains coded data from 241 TikTok videos published by ten leading Spanish influencers between September and December 2025. Variables include engagement metrics, thematic classification, narrative tone, disinformation indicators, and commercial transparency markers.

TikTok Creator and Video Engagement (1M)

This release contains 1,035,817 TikTok videos from 4,926 creators with daily engagement and follower statistics, covering videos posted from 2024-06-09 to 2025-03-20. Github: https://github.com/lingbowzd/tiktok-creator-video-trend-data Cite this dataset: When does Trend-following Pay off? Evidence from Trending Content and Hashtag use

  Uses

This dataset supports research on TikTok creator behavior, content strategy, trend… See the full description on the dataset page: https://huggingface.co/datasets/lingbow/tiktok-video-engagement-1m.