Use our Instagram dataset (public data) to extract business and non-business information from complete public profiles and filter by hashtags, followers, account type, or engagement score. Depending on your needs, you may purchase the entire dataset or a customized subset. Popular use cases include sentiment analysis, brand monitoring, influencer marketing, and more. The dataset includes all major data points: # of followers, verified status, account type (business / non-business), links, posts, comments, location, engagement score, hashtags, and much more.

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.

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.

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.

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.

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 28 January 2022.

--- 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 ---

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.

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.

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.

Includes two datasets published for the detection of fake and automated accounts.

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.

Explore the short-form video landscape on Instagram with this specialized dataset featuring Reels content. This collection includes millions of Reels posts from global creators, influencers, and brands, providing a focused view into one of Instagram’s fastest-growing content formats.

Key Features:

🎥 Reel-Specific Posts: Every entry in the dataset is confirmed to be an Instagram Reel, with associated metadata.

📊 Content & Engagement Metrics: Includes video captions, hashtags, view counts, like counts, comment counts, share counts, and timestamp data.

👤 Creator Information: Features public account data such as usernames, follower counts, bio snippets, and account category where available.

📈 Trend Discovery & Analysis: Perfect for analyzing video content performance, audio trends, visual themes, and influencer strategies on Reels.

🎯 Ready for Analysis: Delivered in clean CSV format, API, or custom formats, optimized for direct use in analytics, dashboards, machine learning models, or campaign planning.

This dataset is ideal for marketers, social strategists, and researchers looking to understand what drives engagement in short-form video content across the Instagram ecosystem.

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?

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.

🔍 ️⃣ 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.

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.

This dataset provides a collection of user reviews for the Threads mobile application from both the Google Play Store and the Apple App Store. It is designed to offer insights into user satisfaction, app performance, and to help identify emerging user patterns and sentiments. The data was gathered by scraping reviews from the respective app marketplaces.

Columns

• source: Indicates the origin of the review, such as 'Google Play' or 'App Store'.
• review_description: Contains the actual text of the review provided by the user.
• rating: Represents the numerical rating given by the user.
• review_date: Specifies the date when the review was submitted.

Distribution

The dataset is typically provided in a CSV file format. Specific row or record counts are not available for the entire dataset, but review counts are detailed for various rating ranges and daily periods. For instance, 15,559 reviews are rated between 4.80 and 5.00, while 11,338 reviews were recorded between 5th and 6th July 2023.

Usage

This dataset is ideal for: * Sentiment analysis to understand overall user sentiment towards the Threads app. * Investigating factors that lead to 1-star and 5-star ratings, offering insights into user satisfaction and dissatisfaction. * Evaluating the application's performance and identifying recurring themes in user feedback.

Coverage

The dataset's geographic scope is global, collecting reviews from users worldwide. The time range for the reviews spans from 6th July 2023 to 25th July 2023. The dataset was last updated on 26th July 2023. It captures feedback from users across two major mobile platforms, Google Play (92% of reviews) and Apple App Store (8% of reviews).

License

CC-BY-NC

Who Can Use It

• Data analysts and researchers interested in mobile app performance and user sentiment.
• App developers and product managers aiming to understand user feedback for app improvements.
• Organisations conducting market research on social media applications.

Dataset Name Suggestions

• Threads Mobile App Reviews 2023
• Instagram Threads User Feedback Dataset
• Threads App Store & Google Play Reviews
• Threads User Ratings and Sentiment Data

Attributes

Original Data Source: Threads, an Instagram app Reviews

This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions. Hence, the dataset can be used for interaction prediction and building a recommendation system. Furthermore, the data forms a dynamic network of interactions, and we can also perform network representation learning on the nodes in the network, which are users and applications.

Data Creation The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student at Sharif University of Technology at the time. The data team focused on a two-week period and randomly sampled 1/3 of the users with interactions during that period. They then selected install and update interactions for three months before and after the two-week period, resulting in interactions spanning about 6 months and two weeks.

We further subsampled and cleaned the data to focus on application download interactions. We identified the top 8000 most installed applications and selected interactions related to them. We retained users with more than 32 interactions, resulting in 280,391 users. From this group, we randomly selected 10,000 users, and the data was filtered to include only interactions for these users. The detailed procedure can be found in here.

Data Structure The dataset has two main files.

myket.csv: This file contains the interaction information and follows the same format as the datasets used in the "JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks" (ACM SIGKDD 2019) project. However, this data does not contain state labels and interaction features, resulting in associated columns being all zero. app_info_sample.csv: This file comprises features associated with applications present in the sample. For each individual application, information such as the approximate number of installs, average rating, count of ratings, and category are included. These features provide insights into the applications present in the dataset.

Dataset Details

Total Instances: 694,121 install interaction instances Instances Format: Triplets of user_id, app_name, timestamp 10,000 users and 7,988 android applications Item features for 7,606 applications

For a detailed summary of the data's statistics, including information on users, applications, and interactions, please refer to the Python notebook available at summary-stats.ipynb. The notebook provides an overview of the dataset's characteristics and can be helpful for understanding the data's structure before using it for research or analysis.

Top 20 Most Installed Applications | Package Name | Count of Interactions | | ---------------------------------- | --------------------- | | com.instagram.android | 15292 | | ir.resaneh1.iptv | 12143 | | com.tencent.ig | 7919 | | com.ForgeGames.SpecialForcesGroup2 | 7797 | | ir.nomogame.ClutchGame | 6193 | | com.dts.freefireth | 6041 | | com.whatsapp | 5876 | | com.supercell.clashofclans | 5817 | | com.mojang.minecraftpe | 5649 | | com.lenovo.anyshare.gps | 5076 | | ir.medu.shad | 4673 | | com.firsttouchgames.dls3 | 4641 | | com.activision.callofduty.shooter | 4357 | | com.tencent.iglite | 4126 | | com.aparat | 3598 | | com.kiloo.subwaysurf | 3135 | | com.supercell.clashroyale | 2793 | | co.palang.QuizOfKings | 2589 | | com.nazdika.app | 2436 | | com.digikala | 2413 |

Comparison with SNAP Datasets The Myket dataset introduced in this repository exhibits distinct characteristics compared to the real-world datasets used by the project. The table below provides a comparative overview of the key dataset characteristics:

The Myket dataset stands out by having an ample number of both users and items, highlighting its relevance for real-world, large-scale applications. Unlike LastFM, Reddit, and Wikipedia datasets, where users exhibit repetitive item interactions, the Myket dataset contains a comparatively lower amount of repetitive interactions. This unique characteristic reflects the diverse nature of user behaviors in the Android application market environment.

Citation If you use this dataset in your research, please cite the following preprint:

@misc{loghmani2023effect, title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks}, author={Erfan Loghmani and MohammadAmin Fazli}, year={2023}, eprint={2308.06862}, archivePrefix={arXiv}, primaryClass={cs.LG} }

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.