Access detailed insights with our Instagram datasets, featuring follower counts, verified status, account types, and engagement scores. Explore post information including URLs, descriptions, hashtags, comments, likes, media, posting dates, locations, and reel URLs. Perfect for understanding user engagement and content trends to drive informed decisions and optimize your social media strategies. Over 750M 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 Post Hashtags Following Posts Profile Image Link Profile URL Profile Name Highlights Count Highlights Full Name Is Private Bio Hashtags URL Is Joined Recently And much more

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

Instagram data-download example dataset

In this repository you can find a data-set consisting of 11 personal Instagram archives, or Data-Download Packages (DDPs).

How the data was generated

These Instagram accounts were all new and generated by a group of researchers who were interested to figure out in detail
the structure and variety in structure of these Instagram DDPs. The participants user the Instagram account extensively for approximately a week. The participants also intensively communicated with each other so that the data can be used as an example of a network.

The data was primarily generated to evaluate the performance of de-identification software. Therefore, the text in the DDPs particularly contain many randomly chosen (Dutch) first names, phone numbers, e-mail addresses and URLS. In addition, the images in the DDPs contain many faces and text as well. The DDPs contain faces and text (usernames) of third parties. However, only content of so-called `professional accounts' are shared, such as accounts of famous individuals or institutions who self-consciously and actively seek publicity, and these sources are easily publicly available. Furthermore, the DDPs do not contain sensitive personal data of these individuals.

Obtaining your Instagram DDP

After using the Instagram accounts intensively for approximately a week, the participants requested their personal Instagram DDPs by using the following steps. You can follow these steps yourself if you are interested in your personal Instagram DDP.

1. Go to www.instagram.com and log in
2. Click on your profile picture, go to *Settings* and *Privacy and Security*
3. Scroll to *Data download* and click *Request download*
4. Enter your email adress and click *Next*
5. Enter your password and click *Request download*

Instagram then delivered the data in a compressed zip folder with the format **username_YYYYMMDD.zip** (i.e., Instagram handle and date of download) to the participant, and the participants shared these DDPs with us.

Data cleaning

To comply with the Instagram user agreement, participants shared their full name, phone number and e-mail address. In addition, Instagram logged the i.p. addresses the participant used during their active period on Instagram. After colleting the DDPs, we manually replaced such information with random replacements such that the DDps shared here do not contain any personal data of the participants.

How this data-set can be used

This data-set was generated with the intention to evaluate the performance of the de-identification software. We invite other researchers to use this data-set for example to investigate what type of data can be found in Instagram DDPs or to investigate the structure of Instagram DDPs. The packages can also be used for example data-analyses, although no substantive research questions can be answered using this data as the data does not reflect how research subjects behave `in the wild'.

Authors

The data collection is executed by Laura Boeschoten, Ruben van den Goorbergh and Daniel Oberski of Utrecht University. For questions, please contact l.boeschoten@uu.nl.

Acknowledgments

The researchers would like to thank everyone who participated in this data-generation project.

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

In 2021, there were 1.21 billion monthly active users of Meta's Instagram, making up over 28 percent of the world's internet users. By 2025, it has been forecast that there will be 1.44 billion monthly active users of the social media platform, which would account for 31.2 percent of global internet users.

How popular is Instagram?

Instagram, as of January 2022, was the fourth most popular social media platform in the world in terms of user numbers. YouTube and WhatsApp ranked in second and third place, respectively, whilst Facebook remained the most popular, with almost three billion monthly active users worldwide.

India had the largest number of Instagram users as of January 2022, with a total of over 230 million users in the country. The second-largest Instagram audience could be found in the United States, with almost 160 million people subscribing to the photo and video sharing app.

Gen Z and Instagram

As of September 2021, Gen Z users in the United States spent an average of five hours per week on Instagram. Although Instagram ranked third in terms of hours per week spent on the platform, Gen Z users spent considerably more time on TikTok, amounting to a weekly average of over 10 hours being spent on the mobile-first video app.

Most followed accounts on Instagram

As of May 2022, Instagram’s own account had 504.37 million followers. In terms of celebrities, Portuguese footballer Cristiano Ronaldo (@chistiano) had over 440.41 million followers on the social network. Moreover, the average media value of an Instagram post by Ronaldo was over 985,000 U.S. dollars.

The most liked post on Instagram as of May 2022 was Photo of an Egg, which was posted in 2019 by the account @world_record_egg. Photo of an Egg has not only exceeded 55 million likes on the platform, but it also has nearly 3.5 million comments, and the account itself has over 4.5 million Instagram followers. After mysterious posts published by the account, World Record Egg revealed itself as part of a mental health campaign aimed at the difficulties and demands of using social media.

The current dataset comprises a total of 1681 instances (rows) of Instagram posts that originate from 120 academic library profiles across the globe. Each post instance comes with a range of features, including the publication date, text caption, profile name, post interaction rate, post link, post type (image, carousel, reel), and the thematic category to which it belongs out of 14 proposed categories. This dataset provides a valuable resource for researchers and practitioners interested in investigating the social media practices and content structure strategies of academic libraries worldwide.

Note: This dataset was developed by the authors and has been utilized in the publication of the paper titled "Unveiling the Feed: Academic Libraries' Instagram Unpacked" in The Journal of Academic Librarianship. Find more for this publication at: https://doi.org/10.1016/j.acalib.2024.102924.

Kindly refer to my paper for more information. Please cite my work if you use my dataset in any work : K. R. Purba, D. Asirvatham and R. K. Murugesan, "Classification of instagram fake users using supervised machine learning algorithms," International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 3, pp. 2763-2772, 2020.

The dataset was collected using web scraping from third-party Instagram websites, to capture their metadata and up to 12 latest media posts from each user. The collection process was executed from September 1st, 2019, until September 20th, 2019. The dataset contains authentic users and fake users, which were filtered using human annotators. The authentic users were taken from followers of 24 private university pages (8 Indonesian, 8 Malaysian, 8 Australian) on Instagram. To reduce the number of users, they are picked using proportional random sampling based on their source university. All private users were removed, which is a total of 31,335 out of 63,795 users (49.11%). The final number of public users used in this research was 32,460 users.

Var name | Feature name | Description pos | Num posts | Number of total posts that the user has ever posted. flg | Num following | Number of following flr | Num followers | Number of followers bl | Biography length | Length (number of characters) of the user's biography pic | Picture availability | Value 0 if the user has no profile picture, or 1 if has lin | Link availability | Value 0 if the user has no external URL, or 1 if has cl | Average caption length | The average number of character of captions in media cz | Caption zero | Percentage (0.0 to 1.0) of captions that has almost zero (<=3) length ni | Non image percentage | Percentage (0.0 to 1.0) of non-image media. There are three types of media on an Instagram post, i.e. image, video, carousel erl | Engagement rate (Like) | Engagement rate (ER) is commonly defined as (num likes) divide by (num media) divide by (num followers) erc | Engagement rate (Comm.) | Similar to ER like, but it is for comments lt | Location tag percentage | Percentage (0.0 to 1.0) of posts tagged with location hc | Average hashtag count | Average number of hashtags used in a post pr | Promotional keywords | Average use of promotional keywords in hashtag, i.e. {regrann, contest, repost, giveaway, mention, share, give away, quiz} fo | Followers keywords | Average use of followers hunter keywords in hashtag, i.e. {follow, like, folback, follback, f4f} cs | Cosine similarity | Average cosine similarity of between all pair of two posts a user has pi | Post interval | Average interval between posts (in hours)

Output : 2-class User classes : r (real/authentic user), f (fake user / bought followers) 4-class User classes : r (authentic/real user), a (active fake user), i (inactive fake user), s (spammer fake user) Note that the 3 fake user classes (a, i, s) were judged by human annotators.

Analysis of ‘Instagram fake spammer genuine accounts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/free4ever1/instagram-fake-spammer-genuine-accounts on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

[comment]: <> (There's a story behind every dataset and here's your opportunity to share yours.) Fakes and spammers are a major problem on all social media platforms, including Instagram. This is the subject of my final-year project in which I set out to find ways of detecting them using machine learning. In this dataset fake and spammer are interchangeable terms.

Content

[comment]: <> (What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.) I have personally identified the spammer/fake accounts included in this dataset after carefully examining each instance and as such the dataset has high level of accuracy though there might be a couple of misidentified accounts in the spammers list as well. The dataset has been collected using a crawler from 15-19, March 2019.

[comment]: <> (### Acknowledgements)

[comment]: <> (We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.)

Inspiration

[comment]: <> (Your data will be in front of the world's largest data science community. What questions do you want to see answered?) This dataset could be further improved in quantity and quality measures, but how much accuracy can it achieve? Possible ways of using the models to tackle the problem?

--- Original source retains full ownership of the source dataset ---

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.

These four datasets are gathered from Instagram users who were chosen randomly.

The MainDataset encompasses data for 818 users. The TestDataset encompasses data for 78 users.

Data gathered for each user includes :

1- number of posts

2- number of followers

3- number of followings

4- number of likes for the tenth previous post

5- number of likes for the eleventh previous post

6- number of likes for the twelfth previous post

7- number of self-presenting posts from nine previous posts

8- gender

The MainDataset_after_150_days and TestDataset_after_150_days encompass data of the users of the Main data set and the Test data set, respectively, for after 150 days. For example, User_1 in the MainDataset has 486 posts and in the MainDataset_after_150_days has 562 posts, which means over the course of 150 days he had published 76 posts.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

Companies of all sizes seek out influencer collaborations that can provide a lasting ROI. Check out some of the brands that use our platform to manage the full life cycle of their influencer marketing campaigns.

We know that contact records are at the heart of every influencer database. That's why we introduced custom properties to reflect the unique needs of your influencer data.

• 10M+ Influencers • Get the Look Your Brand Is After • Increase Audience Size and Demographics • Gain Insights for a Stronger Campaign • Effectively track and measure the impact of your campaigns

Context

Who are leading Pakistan on Instagram?

Content

The dataset contains Top 25 (2 additional for tie) Instagram accounts from Pakistan with category and followers count. All accounts have more than 2 million followers.

Inspiration

Can you find out what kind of contents Pakistanis are interested in on Instagram?

Instagram Fake Profile Detection Dataset

  Dataset Summary

This dataset contains 5,000 Instagram profiles labeled as either fake or real, designed for binary classification tasks in social media fraud detection. The dataset provides comprehensive profile features that can be used to train machine learning models to automatically identify fake Instagram accounts.

  Dataset Details

Total Samples: 5,000 profiles Classes: Binary (0 = Real, 1 = Fake)
Class… See the full description on the dataset page: https://huggingface.co/datasets/nahiar/instagram_bot_detection.

A curated dataset of Instagram posts tagged with fashion-related hashtags. Includes post captions, engagement metrics, creator info, and timestamps. Ideal for trend analysis, influencer tracking, and campaign benchmarking in the global fashion space.

NOTE: We can provide data on any hashtag or word

This dataset supports research on how engagement with social media (Instagram and TikTok) was related to problematic social media use (PSMU) and mental well-being. There are three different files. The SPSS and Excel spreadsheet files include the same dataset but in a different format. The SPSS output presents the data analysis in regard to the difference between Instagram and TikTok users.

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.

This dataset investigates the Instagram engagement metrics (likes and comments) of the U.S. and British Armies to understand their strengths and weaknesses in their marketing. For the quantitative data collection, a random number generator was used to compile a 20% data sample (73 posts) from a total of 365 posts from each account. For instance, a number 1 in the random generator corresponded to the most recent post from the start date of data collection (May 23rd, 2024). By picking from 365 posts, the data collection was meant to represent roughly a year of Instagram content, assuming their Instagram accounts posted every day. This method ensured an unbiased representation of which content was included in the 20% data sample.However, the U.S Army posted almost once a day while the British Army posted only a few days a week. In the end, data was collected across 365 U.S. Army posts from May 23rd, 2024, to October 28th, 2023. For the British Army’s Instagram, the data collection span from May 23rd, 2024, to November 25th, 2021. By engaging with recent posts, the purpose was to understand how effectively these Armies responded to their recruitment crisis (which started in 2022).For the data collection, variables for each post included the following:Date of postNumber of likesPercentage of likes by follower populationNumber of commentsPercentage of comments by follower populationTo understand which Instagram posts were successful, the content with the highest number of likes and comments were defined as the most engaged. But, to accurately compare the British Army’s Instagram engagement to the U.S., the number of likes/comments was divided by the number of their followers. As of May 23, 2024, the U.S. Army had 2.9 million followers on Instagram whereas the British Army had 594,000 followers. While social media users outside of the Armies’ followers engaged with the posts, these ratios provided a basis to fairly compare their engagement metrics.

Through social media like Instagram, users are constantly exposed to “perfect” lives and bodies. Research in this field has predominantly focused on the mere time youth spend on Instagram and the effects on their body image, oftentimes uncovering negative effects. Little research has been done on the root of the influence: the consumed content itself. Hence, this study aims to qualitatively uncover the types of content that trigger youths’ body image. Using a diary study, 28 youth (Mage = 21.86; 79% female) reported 140 influential body image Instagram posts over five days, uncovering trigger points and providing their motivations, emotions, and impacts on body image. Based on these posts, four content categories were distinguished: Thin Ideal, Body Positivity, Fitness, and Lifestyle. These different content types triggered different emotions regarding body image, and clear gender distinctions in content could be noticed. The study increased youths’ awareness of Instagram's influence on their mood and body perception. The findings imply that the discussion about the effects of social media on body image should be nuanced, taking into account different types of content and users. Using this information, future interventions could focus on conscious use of social media rather than merely limiting its use.

Dataset of vertical temperature and salinity profiles obtained at various locations across the Hornsund fjord. Several CTD instruments have been used for data collection: Valeport miniCTD, two separate SAIV A/S 208 STD/CTDs and two separate RBR concerto CTDs. The data are stored in folders organized by the year (YYYY) of measurements. Each vertical profile is stored as an individual, tab-separated ASCII file. The filenames are formed from the date (and time) of measurement followed by the instrument and station names: YYYYMMDD_instrument_station.txt or YYYYMMDDhhmmss_instrument_station.txt. Each file includes eight headerlines with information on station name, geographical location (decimal degrees), bottom depth at the location (m), date (and time) of measurement (YYYY-MM-DDThh:mm:ss), instrument and its serial number, source of financial support and data column names. There are seven data columns: pressure (dbar), depth (m), temperature (°C), potential temperature (°C), practical salinity (PSU), SigmaT density (kg/m**3) and sound velocity (m/s). The data are averaged to 1-dbar vertical bins. Before averaging, data are visually inspected and suspicious data are removed. Based on inter-calibration between the instruments, a linear correction has been calculated for temperature and conductivity and added to the measurements by SAIV A/S 208 CTD. In general, both down- and up-profiles are used for averaging. Finally, the data is interpolated and smoothed.