These TikTok user statistics tell the whole story of the new social media giant and give you some insights into the app's future.

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.

Globally the average user spends 52 minutes on TikTok every day. About 90% of their worldwide users access TikTok on a daily basis.

Regional TikTok user statistics differentiate significantly. Each major region has also experienced growth a different times.

As of February 2025, the United States was the region with the largest TikTok audience by far, with almost 135.79 million users engaging with the popular social video platform. Indonesia followed, with around 107.7 million TikTok users. Brazil came in third, with almost 91.75 million users on TikTok watching short-videos. From Reels to Shorts: social short video takes the internet Between 2021 and 2022 some of the most popular social media platforms have been adding short-video features on the heels of TikTok’s popularity. YouTube Shorts, which rolled out to the global market in June 2021, reached two billion monthly active logged-in users in 2023. In comparison, Instagram’s short-video format Reels, which launched in August 2020, presented a higher view rate than regular videos on the platform between June 2021 and June 2022, as well as a higher likes rate than other content types on Instagram. TikTok business model TikTok is owned by the Beijing-based ByteDance, along with the short-video app Douyin (TikTok’s version for the Chinese market), video platform Xigua, and popular news app Toutiao. While the products intended for domestic market consumption operate in the Chinese digital ecosystem and have a plurality of established monetization methods such as a live-shopping events hosted by famous influencers, TikTok’s main revenue stream comes from online advertising. In 2022, TikTok was estimated to have generated around four billion U.S. dollars worldwide via online advertising.

Use our TikTok profiles dataset to extract business and non-business information from complete public profiles and filter by account name, followers, create date, or engagement score. You may purchase the entire dataset or a customized subset depending on your needs. Popular use cases include sentiment analysis, brand monitoring, influencer marketing, and more. The TikTok dataset includes all major data points: timestamp, account name, nickname, bio,average engagement score, creation date, is_verified,l ikes, followers, external link in bio, and more. Get your TikTok dataset today!

Teenagers make up the largest group of active users on TikTok.

We learn high fidelity human depths by leveraging a collection of social media dance videos scraped from the TikTok mobile social networking application. It is by far one of the most popular video sharing applications across generations, which include short videos (10-15 seconds) of diverse dance challenges as shown above. We manually find more than 300 dance videos that capture a single person performing dance moves from TikTok dance challenge compilations for each month, variety, type of dances, which are moderate movements that do not generate excessive motion blur. For each video, we extract RGB images at 30 frame per second, resulting in more than 100K images. We segmented these images using Removebg application, and computed the UV coordinates from DensePose.

Download TikTok Dataset:

Please use the dataset only for the research purpose.

The dataset can be viewed and downloaded from the Kaggle page. (you need to make an account in Kaggle to be able to download the data. It is free!)

The dataset can also be downloaded from here (42 GB). The dataset resolution is: (1080 x 604)

The original YouTube videos corresponding to each sequence and the dance name can be downloaded from here (2.6 GB).

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 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 the first half of 2024, TikTok received 2,606 law enforcement agency requests for user information from Malaysia. Indonesia ranked second with 778 user data requests. The global number of user data requests via governments during that period amounted to 8,516 requests in total.

In 2020, TikTok brought in $33.4 billion in revenue.

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!

The average adult TikTok user in America spends 33 minutes per day on the app.

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.

In 2023, the number of TikTok users in Malaysia was estimated to reach around 17 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 21.7 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).

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.

In the first half of 2023, TikTok received 16 law enforcement agency requests for user information from Brazil. During the first half of 2022,TikTok received 95 user data requests, which was the highest number registered in the country.

TikTok has a significantly larger female user base globally.

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.