49 datasets found
  1. Instagram Dataset

    • brightdata.com
    .json, .csv, .xlsx
    Updated Apr 26, 2022
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    Bright Data (2022). Instagram Dataset [Dataset]. https://brightdata.com/products/datasets/instagram
    Explore at:
    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Apr 26, 2022
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    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

  2. Instagram: number of global users 2020-2025

    • statista.com
    Updated May 22, 2024
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    Statista (2024). Instagram: number of global users 2020-2025 [Dataset]. https://www.statista.com/statistics/183585/instagram-number-of-global-users/
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    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  3. Instagram accounts with the most followers worldwide 2024

    • statista.com
    • es.statista.com
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    Stacy Jo Dixon, Instagram accounts with the most followers worldwide 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    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.
    
  4. Fake/Authentic User Instagram

    • kaggle.com
    zip
    Updated Feb 11, 2021
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    Kristo Radion Purba (2021). Fake/Authentic User Instagram [Dataset]. https://www.kaggle.com/krpurba/fakeauthentic-user-instagram
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    zip(3451107 bytes)Available download formats
    Dataset updated
    Feb 11, 2021
    Authors
    Kristo Radion Purba
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  5. Instagram: most used hashtags 2024

    • statista.com
    • es.statista.com
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    Statista Research Department, Instagram: most used hashtags 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

  6. Data from: Instagram posts dataset

    • kaggle.com
    Updated May 10, 2023
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    Hardik Kapadia (2023). Instagram posts dataset [Dataset]. https://www.kaggle.com/datasets/thecoderenroute/instagram-posts-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 10, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Hardik Kapadia
    License

    http://www.gnu.org/licenses/lgpl-3.0.htmlhttp://www.gnu.org/licenses/lgpl-3.0.html

    Description

    This is a dataset of 1968 instagram photo posts totalling to 5,426 images.

    There are 1968 folder each containing one or more image corresponding to the image, the post's metadata in a comprossed json file and the post's caption in a txt file.

  7. c

    Social Media Usage Dataset(Applications)

    • cubig.ai
    Updated May 28, 2025
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    CUBIG (2025). Social Media Usage Dataset(Applications) [Dataset]. https://cubig.ai/store/products/321/social-media-usage-datasetapplications
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    Dataset updated
    May 28, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Synthetic data generation using AI techniques for model training, Privacy-preserving data transformation via differential privacy
    Description

    1) Data Introduction • The Social Media Usage Dataset(Applications) features patterns and activity indicators that 1,000 users use seven major social media platforms, including Facebook, Instagram, and Twitter.

    2) Data Utilization (1) Social Media Usage Dataset(Applications) has characteristics that: • This dataset provides different social media activity data for each user, including daily usage time, number of posts, number of likes received, and number of new followers. (2) Social Media Usage Dataset(Applications) can be used to: • Analysis of User Participation by Platform: You can analyze participation and popular trends by platform by comparing usage time and activity for each social media. • Establish marketing strategy: Based on user activity data, it can be used for targeted marketing, content production, and user retention strategies.

  8. Social Media Datasets

    • brightdata.com
    .json, .csv, .xlsx
    Updated Sep 18, 2024
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    Bright Data (2024). Social Media Datasets [Dataset]. https://brightdata.com/products/datasets/social-media
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    .json, .csv, .xlsxAvailable download formats
    Dataset updated
    Sep 18, 2024
    Dataset authored and provided by
    Bright Datahttps://brightdata.com/
    License

    https://brightdata.com/licensehttps://brightdata.com/license

    Area covered
    Worldwide
    Description

    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.

  9. Z

    Dataset for the Instagram and TikTok problematic use

    • data.niaid.nih.gov
    Updated Jul 19, 2023
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    Limniou, Maria (2023). Dataset for the Instagram and TikTok problematic use [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8159159
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    Dataset updated
    Jul 19, 2023
    Dataset provided by
    Hendrikse, Calanthe
    Limniou, Maria
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  10. Instagram: distribution of global audiences 2024, by age group

    • statista.com
    • es.statista.com
    + more versions
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    Stacy Jo Dixon, Instagram: distribution of global audiences 2024, by age group [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    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.
    
  11. Data from: Five Years of COVID-19 Discourse on Instagram: A Labeled...

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated Oct 21, 2024
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    Nirmalya Thakur, Ph.D.; Nirmalya Thakur, Ph.D. (2024). Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis [Dataset]. http://doi.org/10.5281/zenodo.13896353
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    binAvailable download formats
    Dataset updated
    Oct 21, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Nirmalya Thakur, Ph.D.; Nirmalya Thakur, Ph.D.
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Oct 6, 2024
    Description

    Please cite the following paper when using this dataset:

    N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

    Abstract

    The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

    For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

    The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

    There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

    The following is a description of the attributes present in this dataset

    • Post ID: Unique ID of each Instagram post
    • Post Description: Complete description of each post in the language in which it was originally published
    • Date: Date of publication in MM/DD/YYYY format
    • Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API
    • Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API
    • Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

    Open Research Questions

    This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

    1. How does sentiment toward COVID-19 vary across different languages?
    2. How has public sentiment toward COVID-19 evolved from 2020 to the present?
    3. How do cultural differences affect social media discourse about COVID-19 across various languages?
    4. How has COVID-19 impacted mental health, as reflected in social media posts across different languages?
    5. How effective were public health campaigns in shifting public sentiment in different languages?
    6. What patterns of vaccine hesitancy or support are present in different languages?
    7. How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?
    8. What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?
    9. How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?
    10. What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

    All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

  12. Z

    Time and Dynamics of Instagram Users

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jan 21, 2020
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    Amirhosein Bodaghi (2020). Time and Dynamics of Instagram Users [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_1439177
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    Dataset updated
    Jan 21, 2020
    Dataset provided by
    Sama Goliaei
    Amirhosein Bodaghi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  13. Social Media Usage Dataset(Applications)

    • kaggle.com
    Updated Oct 23, 2024
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    Bhadra Mohit (2024). Social Media Usage Dataset(Applications) [Dataset]. https://www.kaggle.com/datasets/bhadramohit/social-media-usage-datasetapplications/suggestions
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 23, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Bhadra Mohit
    License

    https://cdla.io/sharing-1-0/https://cdla.io/sharing-1-0/

    Description

    Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

    Dataset Features:

    User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

    Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

    Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

  14. Instagram: distribution of global audiences 2024, by age and gender

    • statista.com
    • es.statista.com
    + more versions
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    Stacy Jo Dixon, Instagram: distribution of global audiences 2024, by age and gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

                  Teens and social media
    
                  As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
                  Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.
    
  15. d

    9M+ Instagram Posts with #Fashion | Global | Social Media Data Posts by...

    • datarade.ai
    .csv, .xls, .txt
    Updated May 16, 2024
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    Data Unify (2024). 9M+ Instagram Posts with #Fashion | Global | Social Media Data Posts by Keyword [Dataset]. https://datarade.ai/data-products/social-media-data-9m-instagram-posts-with-fashion-posts-data-unify
    Explore at:
    .csv, .xls, .txtAvailable download formats
    Dataset updated
    May 16, 2024
    Dataset authored and provided by
    Data Unify
    Area covered
    New Zealand, Bermuda, Sao Tome and Principe, Guatemala, Solomon Islands, Central African Republic, Togo, Lebanon, Guadeloupe, Netherlands
    Description

    🔍 ️⃣ NOTE: We can provide data on any hashtag or word 🔍 ️⃣

    Dive into fashion culture on Instagram with this curated dataset of posts tagged with fashion-related hashtags. It includes millions of real-time and historical posts from creators across the style spectrum—featuring content from influencers, brands, and users worldwide.

    Key Features:

    📱 Post-Level Detail: Captures caption text, hashtags, image URLs, timestamps, like counts, comment counts, and engagement metrics.

    👗 Fashion-Centric Filtering: Every entry includes at least one fashion-related hashtag (e.g., fashion, ootd, style).

    👤 Creator Metadata: Includes username, follower count, bio, and account type where available.

    ⚡ Insight-Ready: Ideal for trend spotting, campaign benchmarking, sentiment analysis, and brand tracking within the fashion space.

    🚀 Scalable Format: Delivered in structured CSV, ready for analysis or model training.

    This dataset is perfect for brands, agencies, researchers, and AI teams looking to analyze how fashion is represented, consumed, and engaged with on Instagram at scale. Post data: By default the dataset provides the latest 10 posts per profile. This can be expanded at request.

  16. P

    Sentiment Analysis for Social Media Monitoring Dataset

    • paperswithcode.com
    Updated Mar 6, 2025
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    (2025). Sentiment Analysis for Social Media Monitoring Dataset [Dataset]. https://paperswithcode.com/dataset/sentiment-analysis-for-social-media
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    Dataset updated
    Mar 6, 2025
    Description

    Problem Statement

    👉 Download the case studies here

    A global consumer goods company struggled to understand customer sentiment across various social media platforms. With millions of posts, reviews, and comments generated daily, manually tracking and analyzing public opinion was inefficient. The company needed an automated solution to monitor brand perception, address negative feedback promptly, and leverage insights for marketing strategies.

    Challenge

    Analyzing social media sentiment posed the following challenges:

    Processing vast amounts of unstructured text data from multiple platforms like Twitter, Facebook, and Instagram.

    Accurately interpreting slang, emojis, and nuanced language used by social media users.

    Identifying trends and actionable insights in real-time to respond to potential crises or opportunities effectively.

    Solution Provided

    An advanced sentiment analysis system was developed using Natural Language Processing (NLP) and sentiment analysis algorithms. The solution was designed to:

    Classify social media posts into positive, negative, and neutral sentiments.

    Extract key topics and trends related to the brand and its products.

    Provide real-time dashboards for monitoring customer sentiment and identifying areas of improvement.

    Development Steps

    Data Collection

    Aggregated data from major social media platforms using APIs, focusing on brand mentions, hashtags, and product keywords.

    Preprocessing

    Cleaned and normalized text data, including handling slang, emojis, and misspellings, to prepare it for analysis.

    Model Training

    Trained NLP models for sentiment classification using supervised learning. Implemented topic modeling algorithms to identify recurring themes and discussions.

    Validation

    Tested the sentiment analysis models on labeled datasets to ensure high accuracy and relevance in classifying social media posts.

    Deployment

    Integrated the sentiment analysis system with a real-time analytics dashboard, enabling the marketing and customer support teams to track trends and respond proactively.

    Monitoring & Improvement

    Established a continuous feedback mechanism to refine models based on evolving language patterns and new social media trends.

    Results

    Gained Actionable Insights

    The system provided detailed insights into customer opinions, helping the company identify strengths and areas for improvement.

    Improved Brand Reputation Management

    Real-time monitoring enabled swift responses to negative feedback, mitigating potential reputation risks.

    Informed Marketing Strategies

    Insights from sentiment analysis guided targeted marketing campaigns, resulting in higher engagement and ROI.

    Enhanced Customer Relationships

    Proactive engagement with customers based on sentiment analysis improved customer satisfaction and loyalty.

    Scalable Monitoring Solution

    The system scaled efficiently to analyze data across multiple languages and platforms, broadening the company’s reach and understanding.

  17. Instagram Post Reach

    • kaggle.com
    Updated Jan 23, 2021
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    Saurabh Prakash Giri (2021). Instagram Post Reach [Dataset]. https://www.kaggle.com/saurabhprakashgiri/instagram-post-reach/code
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 23, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Saurabh Prakash Giri
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    By now you must have understood that the target variable or the value to be predicted is the likes column which has been referred to as "reach" so far. When do people get more likes on a social media platform like Instagram?

    Content

    The dataset has 101 rows and 6 main features namely, "USERNAME, Caption, Followers, Hashtags, Time since posted, Likes".

    When a user has more followers, they are more likely to get more likes and their post reaches out to more people. Similarly, the number of people your post reaches increases with an increase in the time since the post has been put up. Now, this allows us to select our 2 input variables, which are "Followers and Time since posted" because these 2 variables together in a linear combination will show a linear (or somewhat linear relationship) with the target/output variable.

    However, before building the model, the data needs to be cleaned. The variables "Followers" and "Likes" are both numbers only but the variable "Time since posted" has values like "1 hour", "4 hours", etc. This makes it of string type.

    While building ML models, engineers generally want to make sure that all the values that are sent in as input are in the numerical format. So you will need to remove the string "hour" or "hours" from the "Time since posted" column.

    This can be done by taking all the values in the column "Time since posted" in a list and replacing the string " hour" or " hours" at each index with an empty string and then typecasting each element into an integer.

  18. h

    instagram-images-with-captions

    • huggingface.co
    Updated Nov 29, 2024
    + more versions
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    K Cosmos (2024). instagram-images-with-captions [Dataset]. https://huggingface.co/datasets/kkcosmos/instagram-images-with-captions
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 29, 2024
    Authors
    K Cosmos
    License

    https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/

    Description

    kkcosmos/instagram-images-with-captions dataset hosted on Hugging Face and contributed by the HF Datasets community

  19. n

    Instagram users in Pakistan

    • napoleoncat.com
    png
    Updated Apr 15, 2024
    + more versions
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    NapoleonCat (2024). Instagram users in Pakistan [Dataset]. https://napoleoncat.com/stats/instagram-users-in-pakistan/2024/04
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    pngAvailable download formats
    Dataset updated
    Apr 15, 2024
    Dataset authored and provided by
    NapoleonCat
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 2024
    Area covered
    Pakistan
    Description

    There were 18 490 001 Instagram users in Pakistan in April 2024, which accounted for 7.9% of its entire population. The majority of them were men - 65.3%. People aged 18 to 24 were the largest user group (8 900 000). The highest difference between men and women occurs within people aged 18 to 24, where men lead by 5 700 000.

  20. f

    U.S. Army vs. British Army Instagram Engagement Metrics Dataset

    • figshare.com
    xlsx
    Updated Jun 18, 2024
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    Abby Stover (2024). U.S. Army vs. British Army Instagram Engagement Metrics Dataset [Dataset]. http://doi.org/10.6084/m9.figshare.26060866.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 18, 2024
    Dataset provided by
    figshare
    Authors
    Abby Stover
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United Kingdom, United States
    Description

    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.

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Bright Data (2022). Instagram Dataset [Dataset]. https://brightdata.com/products/datasets/instagram
Organization logo

Instagram Dataset

Explore at:
.json, .csv, .xlsxAvailable download formats
Dataset updated
Apr 26, 2022
Dataset authored and provided by
Bright Datahttps://brightdata.com/
License

https://brightdata.com/licensehttps://brightdata.com/license

Area covered
Worldwide
Description

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

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