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.

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!

Context

Find the top TikTok accounts.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

Data source: https://hypeauditor.com/top-tiktok/

Structured dataset of 30M+ creator profiles across YouTube, Instagram and TikTok, with up to 245 data points per creator including real engagement rate (computed against active followers), audience demographics (country, age, gender), sponsorship history with cost-per-video estimates, content-niche classification, verified contact emails and up to 4+ years of follower and engagement time-series. Refreshed almost daily.

The TikTok Creator Profiles Dataset provides access to millions of publicly available TikTok creator profiles across industries, audience sizes, and regions worldwide.

Designed for marketers, agencies, researchers, and analytics teams, the dataset supports influencer discovery, market research, competitive analysis, audience intelligence, and creator economy insights.

Each profile may include publicly available information such as username, display name, bio, profile URL, follower count, following count, total likes, video count, verified status, category, country, language, external links, and contact details where available.

The dataset covers creators across major categories including lifestyle, beauty, fashion, gaming, fitness, technology, entertainment, travel, food, and more.

Data is available in CSV, JSON, and API formats, with regular updates to ensure fresh and reliable coverage of the global TikTok creator ecosystem.

This dataset contains metadata from online news coverage of the arrest and prosecution of female TikTok influencers in Egypt, a case labelled by the media as "the TikTok Girls" case. The data were collected from five major Egyptian and Arabic-language news websites between 2020 and 2024. It includes article-level metadata (such as titles, publication dates, authors, and URLs). The dataset was compiled to examine how these prosecutions and the women involved have been represented and debated in digital news media during this period.

Dataset Description: Top 100 Influencers

The dataset provides structured information about the top 100 influencers from various countries globally. Each entry represents an influencer and includes the following attributes:

• Rank: The ranking of the influencer in the top 100 list.
• Name: The name or pseudonym of the influencer.
• Follower Count: The total number of followers or subscribers the influencer has on their primary - platform(s).
• Engagement Rate: The level of interaction that the influencer's content receives from users on social media platforms, expressed as a percentage.
• Country: The geographical location or country where the influencer is based or primarily operates.
• Topic Of Influence: The niche or category in which the influencer specializes or creates content, such as fashion, beauty, technology, fitness, etc.
• Reach: The primary social media platform(s) where the influencer is active, such as Instagram, YouTube, TikTok, Twitter, etc.

The dataset consists of primary data collected through an online questionnaire distributed to followers of a selected TikTok influencer in Indonesia. Respondents were selected using purposive sampling based on predefined criteria. The data were measured using a Likert scale and include variables of influencer credibility, purchase intention, and purchase decision. The dataset was analyzed using SEM-PLS to examine the relationships between variables.

📱 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

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.

📊 Trending TikTok Analysis Search Dataset

This dataset is designed to capture and analyze real-world trending behavior on TikTok, focusing on how content spreads, what keywords are searched, and how trends evolve over time.

It includes structured data that reflects search trends, engagement signals, and viral content patterns, making it ideal for machine learning, data science, and social media analytics research.

🔍 Key Features

• Search Queries: Captures what users are actively searching on TikTok
• Trend Score: Measures popularity level of each query or topic
• Engagement Metrics: Includes likes, shares, views, and comments
• Time-Series Data: Tracks how trends rise and fall over time
• Geospatial Signals (if available): Shows where trends are strongest
• Content Categories: Music, dance, memes, education, lifestyle, etc.

📈 Use Cases

• Predicting viral content before it peaks
• Building recommendation systems for short-video platforms
• Social media trend forecasting
• NLP analysis of viral keywords
• Influencer growth strategy modeling

⚙️ Why This Dataset Matters

TikTok trends move fast—sometimes in hours, not days. This dataset helps researchers and developers understand the algorithmic heartbeat of virality, giving insight into what makes content explode globally.

🚀 Ideal For

• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Social Media Analysts
• Kaggle Competitions

📌 What’s Inside?

This dataset captures realistic + synthetic TikTok ecosystem signals, including:

• 🔎 Search Queries (TikTok-style keywords)
• 📈 Trend Velocity Score (how fast a topic goes viral)
• ❤️ Engagement Metrics (likes, shares, comments, views)
• ⏱️ Time-based Trend Evolution (hour/day progression)
• 🧠 Content Category Labels (music, meme, education, lifestyle, etc.)
• 🌍 Optional Geo Trend Signals (country/city-level behavior patterns)

💡 Why This Dataset is Powerful

TikTok isn’t just social media — it’s a real-time global attention engine.

This dataset helps you:

• ⚡ Predict viral content BEFORE it peaks
• 🧠 Train AI models for trend forecasting & recommendation systems
• 📊 Build dashboards for social media intelligence (SMI)
• 🤖 Develop next-gen AI influencer growth tools
• 🔥 Analyze how memes & sounds spread globally in HOURS

🧪 Perfect For Kaggle Competitions

This dataset is built for high-impact ML tasks like:

• 📉 Time Series Forecasting (trend prediction)
• 🧠 NLP Keyword Clustering (viral phrase detection)
• 🎯 Classification (viral vs non-viral content)
• 🔥 Anomaly Detection (sudden spike trends)
• 🤖 Reinforcement Learning (content recommendation simulation)

🌐 Dataset Vibe

Think of it as:

“Google Trends meets TikTok algorithm + AI prediction layer”