Description

The top Instagram influencers and celebrities in the globe are included in this dataset. It contains important parameters like nation, average likes, total posts, number of followers, engagement rate, and worldwide ranking. The dataset aids in the analysis of Instagram audience engagement, influencer performance, and online popularity.

Data science initiatives, digital marketing tactics, influencer marketing research, and social media analysis may all benefit from it. This dataset may be used by researchers, students, and marketers to examine trends in online celebrity, contrast influencers and celebrities, and comprehend the relationship between follower numbers and engagement.

Discover high-performing influencers with our comprehensive Instagram Influencers dataset. Access critical metrics including follower counts, engagement rates, verified status, business categories, and bio information. Analyze top posts, profile details, related accounts, and contact information to identify the perfect influencers for your brand partnerships and marketing campaigns. Millions of influencer records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Account Fbid Id Followers Posts Count Is Business Account Is Professional Account Is Verified Avg Engagement External Url Biography Business Category Name Category Name Following Posts (Top Posts Data) Profile Image Link Profile URL Profile Name Highlights Count Full Name Is Private Bio Hashtags URL Is Joined Recently Has Channel Partner ID Business Address Related Accounts Email Address And much more

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.

As a data analyst, I conducted an in-depth analysis of top influencers on Instagram. Through rigorous data cleaning processes and the use of advanced analysis matrices, I was able to study their strategies and present my findings in a comprehensive dashboard. This project showcases my expertise in data analysis and my ability to derive valuable insights from complex data sets

Instagram is an American photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by American company Facebook Inc., now known as Meta Platforms. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram network 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

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 supports research on how Instagram Influencers impact female consumer behaviour to purchase products and the role of factors such as envy, scepticism towards advertising, satisfaction with life, social comparison and maternalism on consumer behaviour. There are two different files. The SPSS and CVS spreadsheet files include the same dataset but in a different format.

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">

This dataset contains 33,935 Instagram influencers who are classified into the following nine categories including beauty, family, fashion, fitness, food, interior, pet, travel, and other. We collect 300 posts per influencer so that there are 33,935x330 = 10,180,500 Instagram posts in the dataset.

The dataset includes two types of files, post metadata and image files.

1) Post metadata files are in JSON format and contain the following information: caption, usertags, hashtags, timestamp, sponsorship, likes, comments, etc. Its size is at about 37GB.

2) Image files are in JPEG format and the dataset contains 12,933,406 image files since a post can have more than one image file. The total size of these image files is 189GB.

If a post has only one image file then the JSON file and the corresponding image files have the same name. However, if a post has more than one image then the JSON file and corresponding image files have different names. Therefore, we also provide a JSON-Image_mapping file that shows a list of image files that corresponds to post metadata.

If you want to use this dataset, please cite it accordingly. The data can be accessed on the respective website link below.

"Multimodal Post Attentive Profiling for Influencer Marketing," Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han and Wei Wang. In Proceedings of The Web Conference (WWW '20), ACM, 2020.

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

This dataset provides a comprehensive look into Instagram influencer performance and detailed audience demographics. It is specifically designed for advertising agencies, brand managers, and market researchers to identify high-signal influencers and mitigate the risk of "bot" followers in marketing campaigns.

Instagram is an American photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by Facebook Inc.. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram network 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.

Quantitative judge-based data: 398 Instagram influencer profiles evaluated by two independent judges each over a three-month observation period.Secondary analytics data: objective metrics from HypeAuditor, including number of followers, Audience Quality Score (AQS), engagement indicators, and average compensation.The final quantitative sample included nano-influencers (1k–10k followers) and micro-influencers (10k–100k followers) active on Instagram.Variables and short definitionsMain antecedent variablesAuthenticity – the extent to which the influencer is perceived as genuine, real, and true to themselves.Inspiration – the extent to which the influencer motivates, uplifts, or encourages followers.Content showing expertise – the degree to which the influencer’s content demonstrates knowledge, competence, or experience in a topic area.Main mediating variablesOpinion leadership – the extent to which the influencer is perceived as a trusted source of guidance, knowledge, and influence.Number of followers – the size of the influencer’s Instagram audience.Main outcome variablesIntention to consume content – willingness to view, read, or follow the influencer’s content.Intention to recommend the account – willingness to suggest the influencer’s account to others.Intention to follow the advice – willingness to act on the influencer’s recommendations or suggestions.Average compensation – estimated average earnings the influencer receives from social media collaborations.Additional objective/platform variableAudience Quality Score (AQS) – a quality indicator of the influencer’s audience, provided by HypeAuditor, reflecting how authentic/valuable the audience is.Control variablesLuxury lifestyle – the extent to which the content presents an affluent or high-status lifestyle.Mirth – the degree to which the influencer’s content is humorous or amusing.Content usefulness – the extent to which the content is perceived as practical, informative, or helpful.Content showing other people – the extent to which posts feature other individuals.Content linking to different media – the extent to which the influencer connects followers to other media channels or platforms.Content with celebrities – the extent to which the influencer features celebrities in content.Follower engagement (comments and likes) – observable audience interaction with the influencer’s posts.Number of posts / total posts – posting activity or volume of published content.Physical attractiveness – perceived attractiveness of the influencer as a person.Visual attractiveness – perceived visual appeal and aesthetics of the influencer’s content.Uniqueness – the degree to which the influencer’s content is seen as distinctive or original.

Use our Instagram Hashtags dataset (public data) to extract insights by filtering hashtags, follower counts, account type, or engagement metrics. Depending on your needs, you can purchase the full dataset or a customized subset. Popular use cases include trend analysis, brand monitoring, hashtag optimization, and influencer marketing. The dataset includes key data points such as hashtags, engagement scores, associated posts, locations, account types (business/non-business), and much more.

Instagram Influencer and Brand Dataset

  Deskripsi

Dataset ini berisi data influencer Instagram, brand, caption, komentar, dan label terkait untuk keperluan analisis data, klasifikasi, dan riset data science di bidang pemasaran digital dan media sosial.

  Struktur Dataset





  Penjelasan Struktur File

instagram_influencers.csv: Berisi data profil influencer Instagram. Kolom utama: username, followers_tier, demografi (misal: usia, gender, lokasi), psikografi… See the full description on the dataset page: https://huggingface.co/datasets/AzrilFahmiardi/instagram_influencer_and_brand.

Instagram[a] is a photo and video sharing social networking service founded in 2010 by Kevin Systrom and Mike Krieger, and later acquired by American company Facebook Inc. The app allows users to upload media that can be edited with filters and organized by hashtags and geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tag and location, view trending content, like photos, and follow other users to add their content to a personal feed.

Instagram was originally distinguished by allowing content to be framed only in a square (1:1) aspect ratio of 640 pixels to match the display width of the iPhone at the time. In 2015, this restrictions was eased with an increase to 1080 pixels. It also added messaging features, the ability to include multiple images or videos in a single post, and a Stories feature—similar to its main competitor Snapchat—which allowed users to post their content to a sequential feed, with each post accessible to others for 24 hours. As of January 2019, Stories is used by 500 million people daily.

This dataset comprises of the details of top 1000 influencers in instagram

Overview: This dataset contains primary survey responses from 720 Generation Z Instagram users, collected to investigate the non-linear effects of Behavioral Realism (BR) and Perceived Agency (PA) on User Engagement (UE) with AI-generated virtual influencers (VIs). This repository provides a complete replication package, including the raw data, the unstandardized results from the Multivariate Adaptive Regression Splines (MARS) analysis, and the R script used for non-linear estimation.

File Specifications: The repository consists of three files necessary for full computational reproducibility: a) Data.xlsx: The anonymized master dataset containing all survey items and constructs. b) MARS Unstd.xlsx: The unstandardized output file of the latent variable scores extracted from SmartPLS c) Non-Linearity using MARS Algorithm.R: The custom R script used for the secondary analysis.

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.

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! 🌍

Adolescent obesity remains a public health concern, exacerbated by unhealthy food marketing, particularly on digital platforms. Social media influencers are increasingly utilized in digital marketing, yet their impact remains understudied. This research explores the frequency of posts containing food products/brands, the most promoted food categories, the healthfulness of featured products, and the types of marketing techniques used by social media influencers popular with male and female adolescents. By analyzing these factors, the study aims to provide a deeper understanding of how social media influencer marketing might contribute to dietary choices and health outcomes among adolescents, from a gender perspective, shedding light on an important yet underexplored aspect of food marketing. A content analysis was conducted on posts made between June 1, 2021, and May 31, 2022, that were posted by the top three social media influencers popular with males and female adolescents (13–17) on Instagram, TikTok, and YouTube (N = 1373). Descriptive statistics were used to calculate frequencies for posts containing food products/brands, promoted food categories, product healthfulness, and marketing techniques. Health Canada’s Nutrient Profile Model was used to classify products as either healthy or less healthy based on their content in sugar, sodium, and saturated fats. Influencers popular with males featured 1 food product/brand for every 2.5 posts, compared to 1 for every 6.1 posts for influencers popular with females. Water (27% of posts) was the primary food category for influencers popular with females, while restaurants (24% of posts) dominated for males. Influencers popular with males more commonly posted less healthy food products (89% vs 54%). Marketing techniques varied: influencers popular with females used songs or music (53% vs 26%), other influencers (26% vs 11%), appeals to fun or coolness (26% vs 13%), viral marketing (29% vs 19%), and appeals to beauty (11% vs 0%) more commonly. Influencers popular with males more commonly used calls-to-action (27% vs 6%) and price promotions (8% vs 1%). Social media influencers play a role in shaping adolescents’ dietary preferences and behaviors. Understanding gender-specific dynamics is essential for developing targeted interventions, policies, and educational initiatives aimed at promoting healthier food choices among adolescents. Policy efforts should focus on regulating unhealthy food marketing, addressing gender-specific targeting, and fostering a healthy social media environment for adolescents to support healthier dietary patterns.