This dataset contains 30,000 Instagram posts with detailed analytics generated to mimic real Instagram Insights behavior from the past 12 months. It includes media performance indicators such as likes, comments, shares, saves, reach, impressions, and engagement rate. The data reflects realistic patterns of Instagram’s algorithm and content performance trends, including Reels, Photos, Videos, and Carousels.

This dataset is ideal for: Social media analytics Trend prediction Engagement modeling Machine learning Influencer performance analysis Dashboard creation Growth forecasting Reels vs. Posts comparisons

All upload dates are within the previous 365 days, making the dataset time-relevant and aligned with current Instagram usage behavior.

COLUMN DESCRIPTIONS

Post_ID A unique ID for each Instagram post. Useful as a primary key.

Upload_Date The date the post was uploaded, within the last 1 year.

Media_Type Type of content: Photo, Video, Reel, or Carousel. Important for performance comparisons.

Likes Number of likes received by the post.

Comments Total comments the post received.

Shares How many times users shared the post.

Saves Number of users who saved the content. A major Instagram ranking factor.

Reach Unique accounts who saw the post.

Impressions Total views from all sources (may exceed reach).

Caption_Length Number of characters in the post caption.

Hashtags_Count Total hashtags used.

Followers_Gained How many followers were gained directly from this post.

Traffic_Source Where viewers discovered the post: Explore, Home Feed, Hashtags, Profile, Reels Feed, or External.

Engagement_Rate Percentage of engagement relative to impressions. Useful for performance scoring.

WHY THIS DATASET IS USEFUL

Instagram’s algorithm rewards: High saves Strong engagement rates Reels performance Discoverability through Explore Good content retention This dataset allows analysis of: High-performing media types Which traffic sources bring the most reach Ideal hashtag usage Relationship between caption length and engagement Factors influencing followers gained Predicting engagement rate trends

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

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

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.

1) Data Introduction • The Instagram Data provides social media activity data, including various indicators such as categories, participation, and reach of posts generated on Instagram.

2) Data Utilization (1) Instagram Data has characteristics that: • This dataset contains various characteristic information related to the performance of Instagram content, including the type of post, number of likes, number of comments, and reach. (2) Instagram Data can be used to: • Popular Content Analysis: By analyzing participation and reach by category of posts, you can use them to establish effective content strategies. • influencer Marketing Strategy: Use influencer Post Performance Data for Brand Collaboration and Marketing Campaign Planning.

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 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! 🌍

Impact of Paid Partnership Posts’ Characteristics on User Engagement; With Reference to Global Sports Instagram Influencers

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.

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

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.

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.

data Source - https://statso.io/instagram-reach-analysis-case-study/

Certainly! Let's conduct a case study on Instagram reach analysis. To make the case study more specific, let's imagine a scenario where a fashion brand called "Fashionista" wants to analyze the reach of their Instagram account over the past six months.

Objective: Analyze the reach of Fashionista's Instagram account and identify trends, patterns, and insights that can help improve their reach and engagement.

Steps for the Instagram Reach Analysis:

1. Data Collection:

   • Gather data from Fashionista's Instagram account for the past six months.
   • Collect metrics such as follower count, post reach, impressions, likes, comments, and engagement rate.
   • Use Instagram's built-in analytics or third-party tools like Iconosquare or Sprout Social to retrieve the necessary data.

2. Define Key Metrics:

   • Identify the key metrics that will help assess the reach of Fashionista's Instagram account.
   • Key metrics may include follower growth rate, average reach per post, total impressions, engagement rate, and engagement per post.

3. Analyze Follower Growth:

   • Plot the follower count over the past six months to observe any trends.
   • Calculate the follower growth rate to understand the rate at which the account is gaining or losing followers.
   • Look for any significant changes in follower count and investigate potential reasons behind those changes.

4. Evaluate Post Reach and Impressions:

   • Analyze the average reach per post and total impressions to understand the reach of Fashionista's content.
   • Identify posts with the highest and lowest reach and compare their characteristics.
   • Look for patterns or themes that resonate well with the audience and those that underperform.

5. Assess Engagement:

   • Calculate the average engagement rate and compare it across different types of content (e.g., images, videos, stories, reels).
   • Identify posts with the highest engagement rate and analyze their content, captions, and hashtags.
   • Look for patterns or elements that encourage higher engagement from the audience.

6. Identify Optimal Posting Times:

   • Analyze the data to identify the days and times when Fashionista's posts receive the highest reach and engagement.
   • Experiment with posting at different times and measure the impact on reach and engagement.

7. Monitor Competitors:

   • Analyze the reach and engagement of Fashionista's competitors' Instagram accounts.
   • Identify strategies or content types that work well for competitors and consider adopting similar approaches if relevant.

8. Generate Insights and Recommendations:

   • Summarize the findings from the analysis and identify key insights and trends.
   • Recommend strategies to improve Fashionista's Instagram reach based on the insights obtained.
   • Provide actionable recommendations such as optimizing content, using relevant hashtags, collaborating with influencers, or running Instagram ads.

By conducting a thorough analysis of Fashionista's Instagram reach, you'll gain valuable insights into their audience's behavior, content performance, and engagement patterns. These insights can help guide future content strategies and optimize reach and engagement on Instagram.

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

Context

Discover 1000 Top Ranked Influencers by Type and Category of Influence in United States.

Acknowledgements

Data source: https://starngage.com/app/global/influencer/ranking/united-states

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.

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 ...

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