Launched in 2016, TikTok rose to be one of the most popular social app and video platform for global users. In 2021, TikTok had approximately 656 million global users. This figure was projected to increase by around 15 percent year-over-year, reaching 755 million users in 2022. TikTok global installs peaked at the end of 2019, with the app amassing over 318 million downloads. During 2020 and 2021, TikTok download trends experienced a slower growth, amassing 173 million downloads from users worldwide during the last quarter of 2021.

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.

As of February 2025, it was found that around **** percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed approximately **** percent of the platform's audience. The online audience of the popular social video platform was further composed of **** percent of female users aged between 25 and 34 years, and **** percent of male users in the same age group.

In 2023, the number of TikTok users in Malaysia was estimated to reach around ** million. The number was forecast to continuously increase between 2024 and 2029. Based on the forecast, the number of TikTok users in Malaysia will reach **** million by 2029.User figures, shown here with regards to the platform TikTok, have been estimated by considering company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This is a dataset of videos and comments related to the invasion of Ukraine, published on TikTok by a number of users over the year of 2022. It was compiled by Benjamin Steel, Sara Parker and Derek Ruths at the Network Dynamics Lab, McGill University. We created this dataset to facilitate the study of TikTok, and the nature of social interaction on the platform relevant to a major political event.

The dataset has been released here on Zenodo: https://doi.org/10.5281/zenodo.7926959 as well as on Github: https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok

To create the dataset, we identified hashtags and keywords explicitly related to the conflict to collect a core set of videos (or ”TikToks”). We then compiled comments associated with these videos. All of the data captured is publically available information, and contains personally identifiable information. In total we collected approximately 16 thousand videos and 12 million comments, from approximately 6 million users. There are approximately 1.9 comments on average per user captured, and 1.5 videos per user who posted a video. The author personally collected this data using the web scraping PyTok library, developed by the author: https://github.com/networkdynamics/pytok.

Due to scraping duration, this is just a sample of the publically available discourse concerning the invasion of Ukraine on TikTok. Due to the fuzzy search functionality of the TikTok, the dataset contains videos with a range of relatedness to the invasion.

We release here the unique video IDs of the dataset in a CSV format. The data was collected without the specific consent of the content creators, so we have released only the data required to re-create it, to allow users to delete content from TikTok and be removed from the dataset if they wish. Contained in this repository are scripts that will automatically pull the full dataset, which will take the form of JSON files organised into a folder for each video. The JSON files are the entirety of the data returned by the TikTok API. We include a script to parse the JSON files into CSV files with the most commonly used data. We plan to further expand this dataset as collection processes progress and the war continues. We will version the dataset to ensure reproducibility.

To build this dataset from the IDs here:

1. Go to https://github.com/networkdynamics/pytok and clone the repo locally
2. Run pip install -e . in the pytok directory
3. Run pip install pandas tqdm to install these libraries if not already installed
4. Run get_videos.py to get the video data
5. Run video_comments.py to get the comment data
6. Run user_tiktoks.py to get the video history of the users
7. Run hashtag_tiktoks.py or search_tiktoks.py to get more videos from other hashtags and search terms
8. Run load_json_to_csv.py to compile the JSON files into two CSV files, comments.csv and videos.csv

If you get an error about the wrong chrome version, use the command line argument get_videos.py --chrome-version YOUR_CHROME_VERSION Please note pulling data from TikTok takes a while! We recommend leaving the scripts running on a server for a while for them to finish downloading everything. Feel free to play around with the delay constants to either speed up the process or avoid TikTok rate limiting.

Please do not hesitate to make an issue in this repo to get our help with this!

The videos.csv will contain the following columns:

video_id: Unique video ID

createtime: UTC datetime of video creation time in YYYY-MM-DD HH:MM:SS format

author_name: Unique author name

author_id: Unique author ID

desc: The full video description from the author

hashtags: A list of hashtags used in the video description

share_video_id: If the video is sharing another video, this is the video ID of that original video, else empty

share_video_user_id: If the video is sharing another video, this the user ID of the author of that video, else empty

share_video_user_name: If the video is sharing another video, this is the user name of the author of that video, else empty

share_type: If the video is sharing another video, this is the type of the share, stitch, duet etc.

mentions: A list of users mentioned in the video description, if any

The comments.csv will contain the following columns:

comment_id: Unique comment ID

createtime: UTC datetime of comment creation time in YYYY-MM-DD HH:MM:SS format

author_name: Unique author name

author_id: Unique author ID

text: Text of the comment

mentions: A list of users that are tagged in the comment

video_id: The ID of the video the comment is on

comment_language: The language of the comment, as predicted by the TikTok API

reply_comment_id: If the comment is replying to another comment, this is the ID of that comment

The date can be compiled into a user interaction network to facilitate study of interaction dynamics. There is code to help with that here: https://github.com/networkdynamics/polar-seeds. Additional scripts for further preprocessing of this data can be found there too.

Use our TikTok Shop dataset to extract detailed e-commerce insights, including product names, prices, discounts, seller details, product descriptions, categories, customer ratings, and reviews. You may purchase the entire dataset or a customized subset tailored to your needs. Popular use cases include trend analysis, pricing optimization, customer behavior studies, and marketing strategy refinement. The TikTok Shop dataset includes key data points: product performance metrics, user engagement, customer reviews, and more. Unlock the potential of TikTok's shopping platform today with our comprehensive dataset!

This dataset contains information about TikTok videos, including user interactions and video details. It includes features such as video ID, username, video title, likes, comments, shares, views, and more. This dataset is useful for analyzing video performance and user engagement on TikTok.

File Information:

• Format: .csv
• Rows: 5
• Columns: 15
• Size: 1.97 KB

Columns:

• Video_ID: Unique identifier for each video.
• User_ID: Unique identifier for the user who posted the video.
• Username: Username of the user.
• Video_Title: Title or description of the video.
• Category: Category or type of the video.
• Likes: Number of likes the video received.
• Comments: Number of comments on the video.
• Shares: Number of shares of the video.
• Views: Number of views the video received.
• Upload_Date: Date when the video was uploaded.
• Video_Length: Length of the video in seconds.
• Hashtags: List of hashtags used in the video.
• User_Followers: Number of followers the user has.
• User_Following: Number of accounts the user is following.
• User_Likes: Number of likes the user has given. This dataset provides valuable insights into video performance and user engagement, making it useful for various analytical and predictive tasks.

Context

This dataset explores the relationship between digital behavior and mental well-being among 100,000 individuals. It records how much time people spend on screens, use of social media (including TikTok), and how these habits may influence their sleep, stress, and mood levels.

It includes six numerical features, all clean and ready for analysis, making it ideal for machine learning tasks like regression or classification. The data enables researchers and analysts to investigate how modern digital lifestyles may impact mental health indicators in measurable ways.

Dataset Applications

• Quantify how screen‑time, TikTok use, or multi‑platform engagement statistically relate to stress, sleep loss, and mood.
• Train regression or classification models that forecast stress level or mood score from real‑time digital‑usage metrics.
• Feed user‑specific data into recommender systems that suggest screen‑time caps or bedtime routines to improve mental health.
• Provide evidence for guidelines on youth screen‑time limits and platform moderation based on observed stress‑sleep trade‑offs.
• Serve as a teaching dataset for EDA, feature engineering, and model evaluation in data‑science or psychology curricula.
• Evaluate app interventions (e.g., screen‑time nudges) by comparing predicted versus actual post‑intervention stress or mood shifts.
• Cluster individuals into digital‑behavior personas (e.g., “heavy late‑night scrollers”) to tailor mental‑health resources.
• Generate synthetic time‑series scenarios (what‑if reductions in TikTok hours) to estimate downstream impacts on sleep and stress.
• Use engineered features (ratio of TikTok hours to total screen‑time, etc.) in broader wellbeing models that include diet or exercise data.
• Assess whether mental‑health prediction models remain accurate and unbiased across different screen‑time or platform‑use segments. # Column Descriptions
• screen_time_hours – Daily total screen usage in hours across all devices.
• social_media_platforms_used – Number of different social media platforms used per day.
• hours_on_TikTok – Time spent on TikTok daily, in hours.
• sleep_hours – Average number of sleep hours per night.
• stress_level – Stress intensity reported on a scale from 1 (low) to 10 (high).
• mood_score – Self-rated mood on a scale from 2 (poor) to 10 (excell # Inspiration This dataset was inspired by growing concerns about how screen time and social media affect mental health. It enables analysis of the links between digital habits, stress, sleep, and mood—encouraging data-driven solutions for healthier online behavior and emotional well-being. # Ethically Mined Data: This dataset has been ethically mined and synthetically generated without collecting any personally identifiable information. All values are artificial but statistically realistic, allowing safe use in academic, research, and public health projects while fully respecting user privacy and data ethics.

We used TikTok’s built-in account analytics to download and record video and account metrics for the period between 10/8/2021 and 2/6/2022. We collected the following summary data for each individual video: video views, likes, comments, shares, total cumulative play time, average duration the video was watched, percentage of viewers who watched the full video, unique reached audience, and the percentage of video views by section (For You, personal profile, Following, hashtags).
We evaluated the “success” of videos based on reach and engagement metrics, as well as viewer retention (how long a video is watched). We used metrics of reach (number of unique users the video was seen by) and engagement (likes, comments, and shares) to calculate the engagement rate of each video. The engagement rate is calculated as the engagement parameter as a percentage of total reach (e.g., Likes / Audience Reached *100).

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

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.

Unlock insights into high-performing content with this curated dataset of TikTok posts, each with over 50,000 plays. This collection surfaces the videos that resonate most with audiences—spanning creators, themes, and formats that drive virality.

📈 Performance Threshold: Only includes posts that have exceeded 50K views, ensuring a focus on high-engagement, trend-relevant content.

📱 Detailed Post Data: Captures video captions, play counts, likes, shares, comments, sound IDs, hashtags, and posting timestamps.

👤 Creator Metadata: Includes usernames, follower counts, bio snippets, and profile metrics to support creator analysis.

📊 Engagement Benchmarking: Useful for identifying viral content, measuring campaign performance, and refining creative strategies.

⚡ Trend Analysis Ready: Track how themes, hashtags, or sounds perform at scale within and across verticals.

🚀 Structured for Scale: Delivered in clean CSV format API, or custom format, ready for integration into analytics tools, dashboards, or model training environments.

This dataset is designed for marketers, agencies, analysts, and researchers looking to decode the mechanics of virality, identify top-performing content, and inform influencer strategy on TikTok. Whether you're building recommendation engines or planning your next campaign, this dataset offers a high-signal view into TikTok's most impactful content.

Analysis of ‘Top 1000 TikTok Influencers Ranking’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/prasertk/top-1000-tiktok-influencers-ranking on 13 February 2022.

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

Context

Find the top TikTok accounts.

Content

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.

Acknowledgements

Data source: https://hypeauditor.com/top-tiktok/

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

This dataset was initially used in the paper "The use and impact of TikTok in the 2022 Brazilian presidential election". It contains data from official TikTok accounts of the two main candidates running for the 2022 Brazilian presidential election, Lula (@lulaoficial) and Bolsonaro (@bolsonaromessiasjair). It was collected 576 posts of the candidates and more than 540 million interactions on these posts. Data encompass three periods of 2022: (i) Pre-campaign (Jun 30 to Aug 15); (ii) 1st round campaign (Aug 16 to Oct 1); and (iii) 2nd round campaign (Oct 2 - Oct 29). It contains two files. (i) Accounts: How many followers the candidate has, on a day-to-day basis, starting on Sept 5; and (ii) Posts and interactions: Individual data and metrics of each post, including date of the post, text, link for the post, number of plays, likes, comments and shares.

TikTok users have the ability to submit reports that identify videos and comments that contain user claims. In a social media platform like TikTok, report a claim typically refers to the feature that allows users to report content that they believe violates the platform's community guidelines or terms of service. When a user reports a claim over a video, they are flagging the content for reviewing by the platform's content moderation team. The team then assess the reported content to determine if it indeed violates the guidelines, and if so, they may take actions such as removing the content, issuing a warning to the user who posted it, or even suspending or banning the user's account who posted the video. Reporting a claim is an important tool for maintaining a safe and respectful environment on social media platforms.

However, this process generates a large number of reports that are challenging to consider in a timely manner. Therefore, TikTok is working on the development of a predictive model that can determine whether a video contains a claim or offers an opinion. With a successful prediction model, TikTok can reduce the backlog of user reports and prioritize them more efficiently.

The TikTok data team is developing a machine learning model for classifying claims made in videos submitted to the platform.

The target variable:

The data dictionary shows that there is a column called claim_status. This is a binary value that indicates whether a video is a claim or an opinion. This is the target variable. In other words, for each video, the model should predict whether the video is a claim or an opinion. This is a classification task because the model is predicting a binary class.

To determine which evaluation metric might be best, consider how the model might be wrong. There are two possibilities for bad predictions:

• False positives: When the model predicts a video is a claim when in fact it is an opinion
• False negatives: When the model predicts a video is an opinion when in fact it is a claim

In the given scenario, it's better for the model to predict false positives when it makes a mistake, and worse for it to predict false negatives. It is very important to identify videos that break the terms of service, even if that means some opinion videos are misclassified as claims. The worst case for an opinion misclassified as a claim is that the video goes to human review. The worst case for a claim that is misclassified as an opinion is that the video does not get reviewed and it violates the terms of service.

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.

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.

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.

App Reviews

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">

1. uber-request-a-ride-us- 73787 rows
2. waze-navigation-live-traffic-us- 26260 rows
3. facebook-us- 24200 rows
4. spotify-music-and-podcasts-us- 15580 rows
5. netflix-us- 11760 rows
6. pinterest-us- 10860 rows
7. X-us- 8160 rows
8. tiktok-us- 2542 rows
9. tinder-dating-chat-friends-us- 1060 rows
10. instagram-us- 300 rows

These reviews are from Apple App Store

Usage

This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of an app had the most positive feedback, the worst.
3. Use topic modelling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

As social platforms experience an influx of diverse content from users, the need to determine high-quality contributions becomes crucial, especially for educational purposes. This paper highlights the pivotal role of quality in assessing how educational-purposed user-generated content (UGC) shapes user experiences, fosters engagement, and establishes credibility. This study proposes a computational framework using a quasi-experimental evaluation through the sorting-based ELimination Et Choice TRanslating Reality, termed ELECTRE-SORT, with a dataset randomly generated from normally distributed user evaluations. Considering the diverse nature of contents, the method evaluates 16 educational-purposed UGC videos from different online media platforms (i.e. Facebook, YouTube, TikTok). These videos were categorized based on their concordance and discordance to three (3) main criteria: content quality, design quality, and technology quality. Employing the ELECTRE-SORT reveals that most UGC videos (i.e. 14 out of 16) fall into the “medium quality” category, possessing a considerable standard for the quality of educational purpose content. Their characteristics generally satisfy the quality attributes and can be used to guide the development of future relevant UGC videos. Finally, to demonstrate the robustness of the proposed approach, we presented a sensitivity analysis by designing different weight assignments to the quality attributes. Practical insights are outlined in this work.