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.

https://sceptermarketing.com/wp-content/uploads/2019/02/social-media-influencers-2l4ues9.png">

The idea is the figure out the success ratio of youtube content creators and answer some of the basic questions like, how videos is takes for a channel to become successful, what language to choose, what type of content works and establish proof of success with Data and help them make a decision.

Hence the entire team of Business Analyst Interns at KultureHire took the responsibility of collecting and cleaning the data and brought it to an decent shape.

The dataset has 22 fields/columns and over 900 rows or 900 different videos from various youtube channels to it.

Preferred file format is Xlsx or CSV.

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.

Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

In October 2006, 18 months after posting its first video and 10 months after its official launch, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone, to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube and approved creators participate in Google's AdSense program, which seeks to generate more revenue for both parties. YouTube reported revenue of $19.8 billion in 2020. In 2021, YouTube's annual advertising revenue increased to $28.8 billion.

This dataset consists details on top 1000 influencers all over the world.

This dataset contains anonymized YouTube comment data associated with the 2019 online controversy known as Dramageddon, involving beauty influencers James Charles, Tati Westbrook, and Jeffree Star. The dataset was created for research on online hostility, cancel culture, and competitive communication dynamics among influencers.

The dataset includes public user comments collected from 14 YouTube videos posted during May–June 2019, including primary source videos from the influencers involved and reaction videos from commentary channels. A total of ~15,000 comments were collected using the YouTube Data API v3. All comments are anonymized and contain no personally identifiable information.

Each comment record is enriched with metadata and derived variables, including: - Sentiment score (range −1 to +1) - Toxicity score (probability 0–1) - Cancel behavior classification (cold, cool, hot) - Moral language category - Engagement metrics (likes, reply depth) - Time of posting - Video-level metadata (creator, phase of controversy)

This dataset supports research in computational social science, communication studies, digital sociology, and platform governance. It has been used in studies on cancel culture, moral contagion, algorithmic amplification, and influencer reputation dynamics. This dataset contains only publicly available YouTube comments retrieved in accordance with the YouTube Terms of Service. All usernames, channel IDs, and profile references were hashed or removed during preprocessing to ensure anonymization. No attempts were made to identify or contact any YouTube users. The dataset is provided strictly for research purposes. Users must agree to comply with ethical guidelines for internet research (AoIR 2019) and cite the dataset appropriately.

Use our YouTube Videos dataset to extract detailed information from public videos and filter by video title, views, upload date, or likes. Data points include video URL, title, description, thumbnail, upload date, view count, like count, comment count, tags, and more. You can purchase the entire dataset or a customized subset, tailored to your needs. Popular use cases for this dataset include trend analysis, content performance tracking, brand monitoring, and influencer campaign optimization.

YouTube is a global online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day.

File containing two dataset about 100 top YouTube channels in world and India, based upon subscription. Both the dataset contains 6 columns. Column is named as ranking, channel_name, category, subscribers and average view.

1. T-series channel ranked first in the world and it gained 234 M subscriptions it is music based channel.
2. Cocomelon - Nursery Rhymes ranked second it is education based channel made for little babies and having 152 M subscriptions.
3. SET- India entertainment based channel has 150 M subscription. Among top 3 YouTube channels 2 channels are Indians channels. In all 100 YouTube channel 36% Entertainment channel 35% Music and only 29% from other category.

url="https://www.noxinfluencer.com/youtube-channel-rank/top-100-all-all-youtuber-sorted-by-subs-weekly"

To help the influencer Marketing campaigns for Brands and agencies to analyze the trust worthiness of Influncers across India, we at YourExcelguy took this initiative to collect and analyze the data of the influencers (micro & macro influencers).

File Format is Xlsx

Babbl Labs' YouTube Public Company & Brand Mentions dataset enables enterprise-level intelligence from unstructured YouTube video content, transformed into actionable insights for brands, PR consultancies, investment firms, and more.

With over 30,000 curated channels and more than 1 million videos per month, this dataset provides unprecedented visibility into how products, executives and messaging resonate with consumers across the world's largest video platform.

Our proprietary platform combines advanced AI/ML technologies to deliver real-time brand monitoring and influencer tracking. The core innovation is our proprietary voice-print technology that identifies and tracks 50,000+ executives, experts, analysts, and influencers with unprecedented accuracy across channels and appearances.

Advanced NLP maps brand mentions, product references, and competitor comparisons across millions of hours of content. Multi-dimensional sentiment analysis algorithms detect brand perception, purchase intent, and viral conversation trends, delivering structured insights through enterprise-grade dashboards and S3/API access.

Babbl Labs' YouTube Public Company & Brand Mentions dataset enables enterprise-level intelligence from unstructured YouTube video content, transformed into actionable insights for brands, PR consultancies, investment firms, and more.

With over 30,000 curated channels and more than 1 million videos per month, this dataset provides unprecedented visibility into how products, executives and messaging resonate with consumers across the world's largest video platform.

Our proprietary platform combines advanced AI/ML technologies to deliver real-time brand monitoring and influencer tracking. The core innovation is our proprietary voice-print technology that identifies and tracks 50,000+ executives, experts, analysts, and influencers with unprecedented accuracy across channels and appearances.

Advanced NLP maps brand mentions, product references, and competitor comparisons across millions of hours of content. Multi-dimensional sentiment analysis algorithms detect brand perception, purchase intent, and viral conversation trends, delivering structured insights through enterprise-grade dashboards and S3/API access.

Babbl Labs' YouTube Public Company & Brand Mentions dataset enables enterprise-level intelligence from unstructured YouTube video content, transformed into actionable insights for brands, PR consultancies, investment firms, and more.

With over 30,000 curated channels and more than 1 million videos per month, this dataset provides unprecedented visibility into how products, executives and messaging resonate with consumers across the world's largest video platform.

Our proprietary platform combines advanced AI/ML technologies to deliver real-time brand monitoring and influencer tracking. The core innovation is our proprietary voice-print technology that identifies and tracks 50,000+ executives, experts, analysts, and influencers with unprecedented accuracy across channels and appearances.

Advanced NLP maps brand mentions, product references, and competitor comparisons across millions of hours of content. Multi-dimensional sentiment analysis algorithms detect brand perception, purchase intent, and viral conversation trends, delivering structured insights through enterprise-grade dashboards and S3/API access.

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! 🌍

My own work, created with the YouTube API. Over 50,000 entries crawled circa 10/2020. Primarily contains product review influencers and other influencers.
Not at all exhaustive!

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

This dataset provides a comprehensive look at Dhruv Rathee's YouTube channel for 2024, including key metrics like video views, likes, comments, and engagement rates. With Dhruv Rathee's focus on political, social, and educational content, this dataset is ideal for analyzing content trends, audience engagement, and the impact of influencer-driven education. Whether for data science projects, trend analysis, or social media insights, this dataset offers valuable information on one of India's prominent YouTube creators.

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 provides comprehensive analytics on Irfan Malik's YouTube channel for 2024, capturing video metrics such as views, likes, comments, and engagement rates. Irfan Malik is known for his engaging travel and lifestyle content, sharing experiences and stories from Pakistan and beyond. This dataset is ideal for analyzing trends in travel content, social media engagement, and audience preferences. It is suitable for projects in data science, social analytics, and trend analysis, offering insights into the impact and reach of a prominent YouTube influencer.

About Ivana Alawi

https://www.youtube.com/watch?v=pLF0bhcY5l8" alt="">

From Youtube channel - Half Moroccan / Half Filipina - Actress in the Philippines

Channel details - www.youtube.com/@IvanaAlawi - 17.4M subscribers - 198 videos - 1,439,294,300 views - Joined Jun 1, 2018 - Philippines

From Wikipedia

Regarded as one of the biggest social media influencers of her time, Alawi is the most subscribed Filipino celebrity on YouTube, having been honored by Google as the "Top YouTube Content Creator" in the Philippines for two consecutive years. In 2019, she won "Best New Female TV Personality" at the PMPC Star Awards for Television. In 2021, Alawi was ranked fourth on the "100 Most Beautiful Faces in the World" list by TC Candler.

Cover Image

From Official Youtube Channel https://www.youtube.com/@IvanaAlawi

Important Note

There may be some missing videos esp if the channel has more than 600+ videos, this is because the API itself doesn't return all the videos as explained in this Stackoverlow post.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is currently owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

Youtube is very much used to influence people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

Data Dictionary