As of February 2025, the United States was the region with the largest TikTok audience by far, with almost ****** million users engaging with the popular social video platform. Indonesia followed, with around ***** million TikTok users. Brazil came in third, with almost ***** 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 *** 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 **** billion U.S. dollars worldwide via online advertising.

TikTok, a social video app launched in 2016 used for sharing and creating video content, continues to grow in popularity. As of 2022, approximately 41 percent of users in the U.S. engaged with the popular social video app. Around seven in 10 respondents aged between 18 and 19 years were using TikTok, while usage among respondents aged between 20 and 29 years was approximately 56 percent.

TikTok global usage In 2021, TikTok had 1 billion active monthly users worldwide, a growth of 45 percent on the previous year. As of April 2021, the majority of global TikTok users spent less than one hour per week on the platform. Eleven percent of users spent five to ten hours a week on the app, and six percent were using it for ten hours or more per week.

Users' opinion: safety concerns and positive influenceOnline TikTok challenges and hoaxes have been a topic of discussion in recent years, with parents raising concerns about safety regarding content posted on the platform. As for U.S. adults’ opinions of TikTok, favorable attitudes towards the platform decrease with age. Overall, as of November 2021, 33 percent of users aged 18 to 34 years had a favorable opinion of the app, but just one in 10 users aged between 45 and 64 years felt the same. Around 20 percent of all adults had a very unfavorable view of TikTok. Despite the criticism, the platform hosts a large number of positive influencers, focusing on themes such as mental health awareness and body positivity. In 2021, one of the most followed TikTok educators and activists was Nicole Lilly Christou, with her profile @nikkililly reporting approximately 7.7 million followers in the United Kingdom alone.

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 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.7534952 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:

Go to https://github.com/networkdynamics/pytok and clone the repo locally

Run pip install -e . in the pytok directory

Run pip install pandas tqdm to install these libraries if not already installed

Run get_videos.py to get the video data

Run video_comments.py to get the comment data

Run user_tiktoks.py to get the video history of the users

Run hashtag_tiktoks.py or search_tiktoks.py to get more videos from other hashtags and search terms

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.

Based on a survey conducted among consumers in Vietnam, in the third quarter of 2023, over ** percent of Gen Z respondents confirmed using TikTok. Meanwhile, about ** percent of Gen X respondents said they were using the platform. TikTok is an integrated social media app for creating and watching short videos that has recently become one of the most popular social media platforms in Vietnam. TikTok widely used by Gen Z in Vietnam First launched in 2017, TikTok has generated millions of active viewers worldwide. The platform uses algorithms that select the content which users frequently interact with, and continuously suggests similar content matching the needs of viewers. In Vietnam, together with other social media apps, TikTok is well-received by the majority of Gen Z, not only for entertainment purposes but also for online shopping activities. Social media usage - personal information and safety concerns TikTok’s For You Page feature delivers aggregated content and modifies it in accordance with users' comment-like-share behaviors. In order to get access to some trending filters, users provide their data in exchange for a personalized experience. As a result, concerns regarding personal data and children’s safety have been a topic of discussion among social media users, especially Gen Z, with some considering quitting the apps due to privacy reasons. According to Tiktok, users below 18 years old will not have access to content assigned to a limited audience, which would help restrict the visibility of content that may not be appropriate for everyone.

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 the United States, a total of 28 percent of Shein shoppers also shopped on TikTok Shop. The brand with the second-highest percentage of overlap was Temu, with one in every four Temu shoppers having used TikTok Shop in the last 12 months.

TikTok, the popular social media platform, has also expanded into the realm of eCommerce, allowing users to set up online stores. Among the various categories on TikTok, Womenswear & Underwear emerges as the most prominent, hosting the highest number of online shops. It captures 5.64% of the total TikTok store count. Following closely, Beauty & Personal Care and Home Supplies hold significant shares as well, accounting for 5.59% and 5.13%, respectively.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

TikTok to Shake Up Southeast Asia with Billion-Dollar Investment

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The short video sharing platform market is experiencing explosive growth, driven by the increasing popularity of mobile devices, readily available high-speed internet, and the inherent appeal of easily consumable, engaging content. The market, estimated at $50 billion in 2025, is projected to maintain a robust Compound Annual Growth Rate (CAGR) of 25% through 2033. This expansion is fueled by several key trends: the rise of short-form video as a primary mode of content consumption, the proliferation of user-generated content (UGC) platforms, and the integration of short-video features into various social media applications. The diverse segments, encompassing both adult and minor user bases and encompassing applications like sharing via dedicated apps and websites, contribute to the market's dynamism. Leading players, including TikTok, Instagram Reels, YouTube Shorts, and Snapchat, are fiercely competitive, constantly innovating with features like augmented reality filters, creative editing tools, and advanced recommendation algorithms to enhance user engagement and attract new audiences. The market's geographical distribution is broad, with North America and Asia-Pacific currently leading in market share, but rapid growth is expected in other regions like Africa and South America as internet penetration expands. However, the market also faces challenges. Concerns regarding data privacy, content moderation, and the spread of misinformation on these platforms are significant restraints. The intense competition for users and advertising revenue necessitates continuous investment in technological advancements and marketing strategies to maintain a competitive edge. Furthermore, regulatory scrutiny and evolving content guidelines present ongoing hurdles. Despite these challenges, the overall growth trajectory for the short-video sharing platform market remains optimistic, driven by ongoing technological advancements, expanding user base, and the continued dominance of short-form video as a preferred mode of content consumption. The market's future success will depend on platforms’ ability to address the ethical and regulatory concerns while providing engaging and safe user experiences.

As of February 2025, video apps accounted for around 76 percent of global mobile data usage every month. Second-ranked social networking accounted for eight percent of global mobile data volume. The two categories, though, can easily overlap, as users can watch videos via video applications, as well as on social networking applications. Most popular social media platforms with video content Facebook, YouTube, and Instagram were among the most popular social networks in the world, as of October 2021. Each of these platforms allow to post, share, and watch video content on a mobile device. One of the fastest growing global brands, Tiktok, is also a social media platform where users can share video content. In September 2021, the platform reached 1 billion monthly active users. Leading types of mobile video content in the U.S. The United States was the third country in the world based on the number of smartphone users as of May 2021, with around 270 million users. Therefore, mobile content usage in the country was one of the highest in the world, and a big part of it was video content. As of the third quarter of 2021, more than 80 percent of survey respondents in the United States reported watching YouTube on their mobile devices. Social media videos were the second most popular type of content for mobile audiences, with almost six in 10 respondents watching videos on social media platforms like TikTok and Twitter.

As of June 2023, Gen Z, or those between the ages of 19 and 25 years old, made up the majority of the TikTok user base in Singapore, with 33.32 percent. This was followed by Millennials between the ages of 26 and 32 years, comprising around 28.65 percent of TikTok users.

TikTok usage in Singapore

Introduced in 2018 in Singapore, TikTok has rapidly grown to become one of the country's most widely used social media platforms. As of June 2023, it was the most downloaded app in the city-state, recording 782.33 thousand downloads. This rise was also reflected in user engagement, as Singaporean users spent a monthly average of 20.18 hours on TikTok in 2022. It enabled TikTok to reach a monthly revenue of 1.11 million U.S. dollars in December 2022. The platform's rapid development has made it a major hub for the younger generation and is shaping digital trends in the country.

The rise of TikTok Shop 

TikTok is no longer just a video-sharing platform; it has expanded its functionality to become a social commerce hub. TikTok Shop was launched in Singapore in August 2022. The marketplace is integrated into the TikTok platform, enabling consumers and creators to connect and engage directly with brands and sellers. In the city-state, where e-commerce holds a major position, this new feature is in line with the trend towards social commerce. Indeed, in February 2023, 27 percent of people surveyed in the country replied that they regularly made purchases on social media. Among them, 30 percent used TikTok for their purchases.

Stock Data of Top USA Companies: Apple, Tesla, Amazon

👨‍💻 Author: Azhar Saleem

"https://github.com/azharsaleem18" target="_blank"> https://img.shields.io/badge/GitHub-Profile-blue?style=for-the-badge&logo=github" alt="GitHub Profile"> "https://www.kaggle.com/azharsaleem" target="_blank"> https://img.shields.io/badge/Kaggle-Profile-blue?style=for-the-badge&logo=kaggle" alt="Kaggle Profile"> "https://www.linkedin.com/in/azhar-saleem/" target="_blank"> https://img.shields.io/badge/LinkedIn-Profile-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn Profile">
"https://www.youtube.com/@AzharSaleem19" target="_blank"> https://img.shields.io/badge/YouTube-Profile-red?style=for-the-badge&logo=youtube" alt="YouTube Profile"> "https://www.facebook.com/azhar.saleem1472/" target="_blank"> https://img.shields.io/badge/Facebook-Profile-blue?style=for-the-badge&logo=facebook" alt="Facebook Profile"> "https://www.tiktok.com/@azhar_saleem18" target="_blank"> https://img.shields.io/badge/TikTok-Profile-blue?style=for-the-badge&logo=tiktok" alt="TikTok Profile">
"https://twitter.com/azhar_saleem18" target="_blank"> https://img.shields.io/badge/Twitter-Profile-blue?style=for-the-badge&logo=twitter" alt="Twitter Profile"> "https://www.instagram.com/azhar_saleem18/" target="_blank"> https://img.shields.io/badge/Instagram-Profile-blue?style=for-the-badge&logo=instagram" alt="Instagram Profile"> "mailto:azharsaleem6@gmail.com"> https://img.shields.io/badge/Email-Contact%20Me-red?style=for-the-badge&logo=gmail" alt="Email Contact">

Dataset Description

This dataset provides daily stock data for some of the top companies in the USA stock market, including major players like Apple, Microsoft, Amazon, Tesla, and others. The data is collected from Yahoo Finance, covering each company’s historical data from its starting date until today. This comprehensive dataset enables in-depth analysis of key financial indicators and stock trends for each company, making it valuable for multiple applications.

Column Descriptions

The dataset contains the following columns, consistent across all companies:

• Date: The date of the stock data entry.
• Open: The stock's opening price for the day.
• High: The highest price reached during the trading day.
• Low: The lowest price during the trading day.
• Close: The stock’s closing price for the day.
• Volume: The total number of shares traded on that day.
• Dividends: Any dividends paid out on that day.
• Stock Splits: Records stock split events, if any, on that day.

Potential Use Cases

1. Machine Learning & Deep Learning:

   • Stock Price Prediction: Use historical prices to train models for forecasting future stock prices.
   • Sentiment Analysis and Price Correlation: Combine with external sentiment data to predict price movements based on market sentiment.
   • Anomaly Detection: Detect unusual price patterns or volume spikes using classification algorithms.

2. Data Science:

   • Trend Analysis: Identify long-term trends for each company or compare trends between companies.
   • Volatility Analysis: Calculate volatility to assess risk and return patterns over time.
   • Correlation Analysis: Compare stock performance across companies to study market relationships.

3. Data Analysis:

   • Historical Performance: Review historical data to understand growth trends, market impact of stock splits, and dividends.
   • Seasonal Patterns: Analyze data for seasonal trends or recurring patterns across years.
   • Investment Strategy Backtesting: Test various investment strategies based on historical data to assess potential profitability.

4. Financial Research:

   • Economic Impact Studies: Investigate how major events affected stock prices across top companies.
   • Sector-Specific Analysis: Identify performance differences across sectors, such as tech, healthcare, and retail.

This dataset is a powerful tool for analysts, researchers, and financial enthusiasts, offering versatility across multiple domains from stock analysis to algorithmic trading models.

The global mobile video platform market is experiencing robust growth, driven by the proliferation of smartphones, increasing internet penetration, and the rising consumption of video content across diverse demographics. The market, estimated at $150 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an impressive $450 billion by 2033. This expansion is fueled by several key trends: the increasing popularity of short-form video platforms like TikTok and Kuaishou, the continued dominance of established players like YouTube and Netflix, the emergence of innovative shopping video platforms integrating e-commerce directly into the viewing experience, and the growing demand for live streaming across various niches (gaming, entertainment, education). The market is segmented by user demographics (young, middle-aged, elderly) and platform type (on-demand, live, short-form, shopping, comprehensive). While the dominance of established players presents a challenge for new entrants, market fragmentation and continuous technological advancements provide ample opportunities for innovation and growth. Regional variations are expected, with North America and Asia Pacific leading the market due to higher smartphone penetration and internet access. However, growth in emerging markets in Africa and South America is anticipated to accelerate in the coming years, driven by increasing mobile phone adoption and improving internet infrastructure. Restraints to growth include concerns around data costs, internet accessibility in certain regions, and competition for user attention across various digital media platforms. The success of mobile video platforms hinges on delivering engaging and personalized content. Platforms are increasingly investing in AI-powered recommendation algorithms to enhance user experience and retention. The integration of AR/VR technologies is also expected to further enhance engagement and drive market expansion. The competitive landscape is characterized by both established giants and emerging players, leading to continuous innovation in content formats, monetization strategies, and technological advancements. This dynamic environment ensures that the mobile video platform market will continue its rapid growth, offering lucrative opportunities for investors and businesses alike. Strategic partnerships and mergers and acquisitions are likely to continue shaping the market landscape. Furthermore, the increasing focus on user privacy and data security will influence platform development and regulation in the coming years.

Analyzing the total sales figures provides insights into the performance of different categories within the TikTok eCommerce platform. Among them, Beauty & Personal Care emerges as the dominant force, contributing significantly to the total sales on TikTok Shops. With its appealing products and favorable market conditions, Beauty & Personal Care captures the attention of a substantial portion of users. Additionally, Womenswear & Underwear and Food & Beverages also make a notable impact on the platform's sales, accounting for 14.36% and 6.96% of the overall TikTok Shops sales landscape.

The Gross Merchandise Value (GMV) is an important metric that reflects the monetary value of transactions within the TikTok eCommerce platform. Analyzing the GMV amounts by category reveals that Beauty & Personal Care plays a dominant role, generating the highest GMV among all categories. It accounts for 22.50% of the total TikTok shop GMV. Following closely behind, Womenswear & Underwear and Menswear & Underwear exhibit significant GMV contributions as well, representing 12.56% and 8.06%, respectively.

The GMV generated by TikTok shops varies across different regions, reflecting the economic impact of the platform on a global scale. Among the regions we monitor, Thailand stands out as a leader in terms of GMV, indicating a prosperous TikTok eCommerce economy. Moreover, Vietnam and Malaysia demonstrate significant contributions to the total GMV, accounting for 20.79% and 14.68% of the global TikTok shop GMV, respectively. Beyond these regions, other notable areas such as Philippines, Indonesia and United States also make substantial GMV contributions.

As of June 2023, the Gen Z or those between the ages of 19 to 25 years old, made up the majority of TikTok user base in Malaysia, with 35.61 percent. This was followed by the Millennials between the ages of 26 to 32 years, comprising around 29 percent of TikTok users.

BackgroundTikTok is an important channel for consumers to obtain and adopt health information. However, misinformation on TikTok could potentially impact public health. Currently, the quality of content related to GDM on TikTok has not been thoroughly reviewed.ObjectiveThis study aims to explore the information quality of GDM videos on TikTok.MethodsA comprehensive cross-sectional study was conducted on TikTok videos related to GDM. The quality of the videos was assessed using three standardized evaluation tools: DISCERN, the Journal of the American Medical Association (JAMA) benchmarks, and the Global Quality Scale (GQS). The comprehensiveness of the content was evaluated through six questions covering definitions, signs/symptoms, risk factors, evaluation, management, and outcomes. Additionally, a correlational analysis was conducted between video quality and the characteristics of the uploaders and the videos themselves.ResultsA total of 216 videos were included in the final analysis, with 162 uploaded by health professionals, 40 by general users, and the remaining videos contributed by individual science communicators, for-profit organizations, and news agencies. The average DISCERN, JAMA, and GQS scores for all videos were 48.87, 1.86, and 2.06, respectively. The videos uploaded by health professionals scored the highest in DISCERN, while the videos uploaded by individual science communicators scored significantly higher in JAMA and GQS than those from other sources. Correlation analysis between video quality and video features showed DISCERN scores, JAMA scores and GQS scores were positively correlated with video duration (P

TikTok's eCommerce presence spans across different regions, with varying levels of adoption and participation. Among the regions we monitor, Indonesia emerges as a dominant force in terms of the number of TikTok shops, showcasing a thriving eCommerce ecosystem. Additionally, Thailand and Vietnam exhibit substantial online shop presence, accounting for 18.29% and 17.54% of the global TikTok store market, respectively. The popularity of TikTok shops is not limited to these few regions, as other notable regions such as Malaysia, United States and Philippines also contribute significantly.