This dataset contains 29,999 Instagram posts with key performance metrics commonly used for content analytics and growth modeling. It includes engagement counts (likes, comments, shares, saves), exposure metrics (reach, impressions), content metadata (media type, category, caption length, hashtags), account features (account type, follower count), traffic source, posting time features, and a performance label.

The dataset is ideal for:

Engagement prediction Performance classification (low/medium/high/viral) Best posting time analysis Traffic source impact Content strategy & optimization EDA / dashboards

What’s included Post identifiers and timestamp features Engagement metrics: likes, comments, shares, saves Reach & impressions Engagement rate (continuous) Content category & media type Traffic source CTA indicator Performance bucket label

Dataset size Rows: 29,999 Columns: 23 Time span: Nov 2024 – Nov 2025

Notes Some engagement fields contain missing values (NaNs). This reflects realistic analytics exports where certain post types or tracking conditions may omit metrics. Users can either impute missing values or remove incomplete rows depending on their modeling goals.

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.

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 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.

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.

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.

Instagram 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 200 top influencers profile data of instagram

The Top Instagram Accounts Dataset is a collection of 200 rows of data that provides valuable insights into the most popular Instagram accounts across different categories. The dataset contains several columns that provide comprehensive information on each account's performance, engagement rate, and audience size.

1. The "rank": column lists the accounts in order of their popularity on Instagram, starting from the most followed account.

2. The "name": column displays the Instagram handle of the account, which can be used to locate and follow the account on Instagram.

3. The "channel_info": column provides a brief description of the account, such as the type of content it features or the products and services it offers.

4. The "Category": column categorizes the account based on its primary theme or subject matter, such as fashion, sports, entertainment, or food.

5. The "posts": column displays the total number of posts on the account. This column helps to understand the account's level of activity and the amount of content it has produced over time.

6. The "followers": column indicates the number of people who follow the account on Instagram.

7. The "avg likes": column displays the average number of likes that the account's posts receive per post.

8. The "eng rate": column calculates the account's engagement rate by dividing the total number of likes and comments received by the total number of followers, expressed as a percentage.

How you can use this Dataset?

The Top Instagram Accounts Dataset can be used in a variety of ways to gain insights into the performance and engagement levels of popular Instagram accounts. Here are a few examples of what you can do with this dataset:

1. Conduct category analysis: The dataset provides information on the category of each Instagram account. You can use this information to conduct a category analysis and identify the most popular categories on Instagram.

2. Identify top influencers: The dataset ranks Instagram accounts based on their follower count. You can use this information to identify the top influencers in different categories and use them for influencer marketing campaigns.

3. Analyze engagement levels: The dataset includes columns such as "avg likes" and "eng rate" that provide insights into the engagement levels of Instagram accounts. You can use this information to understand what type of content resonates with Instagram users and create more engaging content for your own account.

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

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.

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.

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.

About Dataset

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.

About this file

In this file, basically there are 10 attributes. It has been ordered on basis of the rank which has been decided on basis of "followers".

rank: Rank of the Influencer on basis of number of followers they have channel_info: Username of the Instagrammer influence score: Influence score of the users. It is calculated on basis of mentions, importance and popularity posts: Number of posts they have made so far followers: Number of followers of the user avg_likes: Average likes on instagrammer posts (total likes/ total posts) 60_day_eng_rate: Last 60 days engagement rate of instagrammer as faction of engagements they have done so far new_post_avg_like: Average likes they have on new posts total Likes: Total likes the user has got on their posts. (in Billion) country: Country or region of origin of the user.

The captions of 20 Instagram Influencers were collected manually. Four branches of Influencers were chosen, namely Beauty, Fitness, Food and Technology. From each category five Influencers, male and female, of the Forbes List of Top Influencers 2017 (Forbes 2017) were chosen according to the following two criteria. First, they must be "Macro-Influencers", which means they must have around 1,000,000 followers and are no celebrities outside social media. Second, their primary language must be English. No restriction to British English was made in order to cover a diverse geographic distribution. In this way, it was ensured that the data was not biased due to regional variations of language.

The sample of captions was restricted to posts that were either marked as a collaboration or paid partnership with a brand or captions which either included the hashtag #ad or directly mentioned a product or brand via name tagging (@ sign). Captions were excluded where the name tag led to a related person of the Influencer, such as the partner, a family member or a pet. Captions were collected during the month of December 2019. This month was chosen due to two facts. First, recent research has shown that on weekends, when people do not go to work and sleep more, and during holidays, such as Christmas or New Year's Eve, the emotive content, especially happiness, on social media increases. The sample contained 293 captions with a total of 18,721 words. The average Instagram caption of Influencers working with brands was therefore about 64 words.

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.

This dataset contains publicly available Instagram profile metadata and engagement-related information collected from Instagram public profiles inspired by the social media ecosystem surrounding Virat Kohli.

The dataset is designed for:

Social media analytics Machine learning projects Influencer analysis Fake follower detection Engagement prediction NLP and sentiment analysis Data visualization Recommendation systems

The dataset includes profile-level public metadata such as usernames, verification status, follower counts, biographies, engagement statistics, and profile information.

This dataset can be useful for:

Beginner data science projects AI/ML model training Instagram analytics dashboards Research and educational purposes

All information included in the dataset is collected only from publicly accessible Instagram data sources.

This is a Data analysis Project. I used Excel to clean up the messy raw data about the "Top 100 Instagram Influencers" and turn it into a neat, easy-to-read table. Then, I dove into the data on the Top 100 Instagram influencers, using Excel to uncover interesting insights about which countries were leading in terms of engagement and reach. I made sense of it all by setting up Pivot tables to connect the dots and bring together the most important information. To make sure everyone could understand the findings, I got creative and visualized the data, creating a dynamic dashboard that made it easy to see what was going on at a glance.

Context

This dataset is for local (Saudi Arabia) social media influencers, and the dataset is built using web scraping to get influencers information from https://influence.co/category/riyadh . The dataset focused on Instagram influencers in Saudi Arabia and contains 5 attributes and 243 rows. In particular, the dataset has the Instagram id for the influencers,number of followers, the category name that they belong to and level of impact of influencers on Instagramwhich is the avg engagement rate.

Content

# Data Set Information:

• IG_id, The influencer Instagram id, object.
• No_followers, The number of followers the influencer have, int64.
• Category_name, the category which the influencer belongs to (# Here I assumed that when there were other social media platforms, I would replace them with the name of persons in these programs, for example, 'snapchat - lifestyle', youtube - vlogger','Facebook, Blogger'), object.
• Locations, the influencer location (based city), object.
• engagment_rate_avg , the engagement rate the influencer have in % ,float64.

Acknowledgements

Data source : https://influence.co/category/riyadh

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

As a data analyst, I have created a comprehensive and visually stunning Power BI presentation that delves into the top 200 influencers on Instagram. This project utilizes advanced data analysis techniques to provide valuable insights into the social media landscape and the impact of these influencers.

Through this project, we can explore the reach and engagement of these top influencers, as well as their content strategies. The presentation also includes detailed metrics and visualizations that allow us to better understand the trends and patterns within this influential group.

Overall, this project represents a powerful tool for anyone looking to gain a deeper understanding of the role of influencers on Instagram and the broader social media landscape. Whether you are a marketer, researcher, or simply curious about the world of social media, this presentation is sure to provide valuable insights and information