TikTok has 136 million monthly active users in the US alone.

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

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

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.

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).

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.

There are currently over 1.5 billion active users on TikTok worldwide.

1) Data Introduction • The Dataset from TikTok contains 19,382 reports that users flagged as including "claim" in videos or comments, along with video length, transcription text, account status, and participation indicators, and is suitable for analyzing reporting reasons and viewer reactions by content.

2) Data Utilization (1) Dataset from TikTok has characteristics that: • This dataset consists of 12 columns, providing both the reported content type and the meta-participation index of the video. (2) Dataset from TikTok can be used to: • Claim Judgment Classification Model Development: By inputting video transcription text, participation indicators such as views, likes, shares, comments, and account authentication and sanctions information, the machine learning classification model can be automatically determined whether the content contains "claims." • Optimizing moderation tasks: Automate reporting priorities based on classification model predictability to speed up reporting processing and reduce supervision burden by selecting content that managers urgently need to review.

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

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!

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).

Tiktok network graph with 5,638 nodes and 318,986 unique links, representing up to 790,599 weighted links between labels, using Gephi network analysis software.

Source of:

Peña-Fernández, Simón, Larrondo-Ureta, Ainara, & Morales-i-Gras, Jordi. (2022). Current affairs on TikTok. Virality and entertainment for digital natives. Profesional De La Información, 31(1), 1–12. https://doi.org/10.5281/zenodo.5962655

Abstract:

Since its appearance in 2018, TikTok has become one of the most popular social media platforms among digital natives because of its algorithm-based engagement strategies, a policy of public accounts, and a simple, colorful, and intuitive content interface. As happened in the past with other platforms such as Facebook, Twitter, and Instagram, various media are currently seeking ways to adapt to TikTok and its particular characteristics to attract a younger audience less accustomed to the consumption of journalistic material. Against this background, the aim of this study is to identify the presence of the media and journalists on TikTok, measure the virality and engagement of the content they generate, describe the communities created around them, and identify the presence of journalistic use of these accounts. For this, 23,174 videos from 143 accounts belonging to media from 25 countries were analyzed. The results indicate that, in general, the presence and impact of the media in this social network are low and that most of their content is oriented towards the creation of user communities based on viral content and entertainment. However, albeit with a lesser presence, one can also identify accounts and messages that adapt their content to the specific characteristics of TikTok. Their virality and engagement figures illustrate that there is indeed a niche for current affairs on this social network.

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.

In a context where there is permanent electoral campaigning, an increasing number of political communication experts are trying to unravel the resources used by government officials and their parties to influence TikTok users. From a broad perspective, the subject matter is not new, but it is topical; nonetheless, this research discloses a gap in the literature by amalgamating the recognition of idiosyncratic attributes of the feminisation of political discourse on TikTok with the analysis of the reactions (text and emojis) that the audiovisual content imbued by this trend elicits in users. The purpose is to ascertain whether the inclusive tone of the feminised rhetorical style can be extrapolated to TikTok and, if so, whether its particular characteristics mitigate expressions of incivility. To do so, the initial content posted (first seven months) on TikTok by the Spanish political platform Sumar with its leader, Yolanda Díaz, featuring prominently in most of the videos, were selected for scrutiny. A mixed methodology analysis of audiovisual content and comments showed that the anti-polarisation rhetoric and storytelling contributed to neutralising the extreme forms of flaming, although Sumar did not use a strategy tailor-made to suit TikTok.

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!

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.

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

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.

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.

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.