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.

TikTok Viral Trends 2025

September 2025 Viral Video Insights

Overview

This dataset, titled TikTok Viral Trends 2025, provides a curated snapshot of 50 trending TikTok videos from September 2025, capturing the platform's dynamic content landscape. Sourced from real-time web analyses and social media insights (e.g., X posts, trend reports from reputable sources like Ramdam, NapoleonCat, and Tokchart), it focuses on viral videos across diverse categories such as Entertainment, Music, Comedy, Lifestyle, Beauty, Sustainability, and Technology. The dataset is designed for data scientists, researchers, and enthusiasts interested in analyzing social media trends, predicting virality, or exploring multimodal machine learning applications (e.g., NLP, time-series, or clustering). It stands out from existing Kaggle datasets by offering fresh, 2025-specific data with rich metadata, including engagement metrics, hashtags, and sound/trend associations.

Dataset Description

• Size: 50 records, each representing a trending TikTok video or aggregated trend data from September 2025.
• Format: CSV (tiktok_data.csv).
• Source: Aggregated from public web sources and social media posts, ensuring authenticity and compliance with data-sharing guidelines. Specific sources are cited per record (e.g., post:72, web:65).
• Update: Reflects trends as of September 16, 2025, making it more current than 2023-2024 TikTok datasets on Kaggle.

Columns

The dataset contains the following 12 columns: - video_id: Unique identifier for each video or trend (integer or hashtag-based). - author: Creator username or group (anonymized as "Unknown" where not specified). - description: Brief summary of the video content or trend, derived from source context. - upload_date: Approximate or exact posting date (YYYY-MM-DD). - views: Reported view count (e.g., millions, billions for hashtag aggregates; "N/A" if unavailable). - likes: Reported like count (e.g., thousands, millions; "N/A" if unavailable). - shares: Share count (often "N/A" due to limited public data). - comments: Comment count (often "N/A" due to limited public data). - hashtags: Key hashtags associated with the video or trend (e.g., #Kpop, #Viral). - category: Inferred content category (e.g., Entertainment, Music, Comedy, Lifestyle, Sustainability, Tech). - sound_or_trend: Associated audio track or challenge name driving the trend (e.g., "Soda Pop dance", "JUMP"). - source: Citation of data origin (e.g., post:72 for X post ID, web:65 for web source ID).

Key Features

• Diverse Categories: Includes K-pop (e.g., BLACKPINK, SEVENTEEN), dance challenges (e.g., Espresso Dance), AI-driven content (e.g., Identity Swap), comedy, lifestyle (e.g., SustainableSeptember), and beauty trends, reflecting TikTok's global appeal.
• High Engagement: Videos with reported metrics show millions of views (e.g., 29.4M for BLACKPINK’s JUMP) and likes, with hashtag trends like #Perfume reaching 39.3B views.
• Multimodal Potential: Supports text analysis (descriptions, hashtags), numerical analysis (views, likes), and categorical analysis (categories, sounds).
• Timeliness: Captures September 2025 trends, including seasonal (e.g., Autumn Cozy Challenge) and cultural moments (e.g., K-pop releases, viral memes).

Potential Use Cases

This dataset is ideal for a variety of machine learning and data analysis tasks on Kaggle, including but not limited to: - Virality Prediction: Use views, likes, and hashtags to train regression or classification models (e.g., XGBoost, neural networks) to predict video success. - Trend Analysis: Apply clustering (e.g., K-means) or topic modeling (e.g., LDA) to identify emerging content themes or regional differences. - NLP Applications: Analyze descriptions and hashtags with BERT or word embeddings to study sentiment, cultural trends, or influencer impact. - Time-Series Forecasting: Leverage upload_date and engagement metrics for temporal analysis of trend lifecycles. - Recommendation Systems: Build content recommendation models based on category, sound, or hashtag similarities. - Social Media Ethics: Explore AI-driven trends (e.g., deepfake Identity Swaps) for studies on misinformation or content authenticity.

Data Collection

• Methodology: Data was aggregated from public web sources (e.g., trend reports, news snippets) and X posts discussing viral TikTok content. No private or restricted data was used, ensuring ethical sourcing.
• Limitations: Some metrics (e.g., shares, comments) are "N/A" due to limited public availability. View and like counts are reported where available, with aggregates for trends (e.g., 686.4K videos for #Ominous). Exact metrics may vary slightly due to real-time fluctuations.
• Verification: All entries ...

Dataset

This dataset was created by Marcus Ong

Released under CC0: Public Domain

Contents

TikTok is the leading destination for short-form mobile video. The platform is built to help imaginations thrive. TikTok's mission is to create a place for inclusive, joyful, and authentic content–where people can safely discover, create, and connect.

The dataset was originally obtained from TikTok's trending API by a GitHub user named Ivan Tran. It contains metadata on engagement with user-created videos and user profile data. The original create time is in Unix timecode format and is extracted directly from the video id number. TikTok's API has become much more difficult to access recently, so more current data is harder to obtain. The hashtags column contains lists.

Context

US Supermarkets have seen a recent shortage of Feta Cheese due to a TikTok pasta that went viral. "https://www.fox5ny.com/news/viral-tiktok-video-recipe-prompts-feta-cheese-shortage"

The Brazilian music industry is already experiencing huge shifts in it's business model, TikTok changed young people playlists. Most of the biggest players in this market realized the day-light revolution of music going on, and are trying to influence as much as possible something many believe to be random: songs going viral.

Content

This data contains 10.000 rows, each describing a single video. Along with that, there are 14 columns: username, user id, video id, video desc, videotime, video length, video link, n likes, n shares, n comments, n plays, music name, music url

Acknowledgements

Thank you David Teather for developing a nice and easy-to-use API.

Popular TikTok Videos, Authors, and Musics

A Comprehensive Dataset for performing Trending Analysis

About this dataset

TikTok is one of the hottest social media platforms out there, and it's only getting bigger. If you're looking to get in on the action, this dataset is for you!

This dataset contains a collection of videos from TikTok, including information on the user who posted the video, the number of likes, shares, and comments the video received, as well as the video's length and description. With this data, you can see what types of videos are popular on TikTok and start planning your own viral content!

How to use the dataset

1. The dataset contains a collection of videos from the social media platform TikTok.
2. The videos include information on the user who posted the video, the number of likes, shares, and comments the video received, as well as the video's length and description.
3. The dataset also contains information on popular TikTok authors, including their unique ID, nickname, avatar thumbnail, signature, and whether or not their account is verified or private.
4. Additionally, the dataset includes a list of trending videos on TikTok, as well as the number of likes, shares, comments, and plays each video has received

Research Ideas

• Identifying popular TikTok authors to target for scraping videos and liked videos
• Finding trending videos on TikTok for further analysis
• Generating a list of videos from the TikTok app that are tagged with the #funny hashtag

Acknowledgements

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: tiktok_collected_liked_videos.csv | Column name | Description | |:---------------|:---------------------------------------------------------| | user_name | The name of the user who posted the video. (String) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of shares the video has received. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

File: tiktok_collected_videos.csv | Column name | Description | |:---------------|:---------------------------------------------------------| | user_name | The name of the user who posted the video. (String) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of shares the video has received. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

File: tiktok_funny_hashtag_videos.csv | Column name | Description | |:--------------------------|:-----------------------------------------------------------| | author_nickname | The author's nickname. (String) | | author_avatarThumb | The author's avatar thumbnail. (String) | | author_signature | The author's signature. (String) | | author_verification | Whether or not the author's account is verified. (Boolean) | | author_privateAccount | Whether or not the author's account is private. (Boolean) | | author_followingCount | The number of people the author is following. (Integer) | | author_followerCount | The number of people following the author. (Integer) | | author_heartCount | The number of hearts the author has. (Integer) | | author_diggCount | The number of diggs the author has. (Integer) | | music_title | The title of the music. (String) | | music_playUrl | The play url of the music. (String) | | music_coverThumb | The cover thumbnail of the music. (String) | | music_authorName | The author name of the music. (String) | | music_originality | The originality of the music. (String) | | music_duration | The duration of the music. (String) |

File: trending_authors.csv | Column name | Description ...

The TikHarm dataset is a curated collection of TikTok videos designed to train models for classifying harmful content. The dataset is in the format of UCF101, and it is specifically focused on content accessible to children, with the aim of distinguishing between different types of potentially harmful material.

Data Collection:

Data was gathered from TikTok, targeting videos that are accessible to children to ensure the dataset reflects the type of content they are likely to encounter.

Data Labeling:

Collected videos were manually labeled into four predefined categories: - Harmful Content: Videos that depict violence, dangerous actions that children might imitate, or other harmful behavior. - Adult Content: Videos containing sexual content or other material deemed inappropriate for children. - Safe: Videos that are appropriate and safe for children to view: popular cartoon, etc. - Suicide: Videos that depict, suggest, or discuss suicidal behavior or ideation.

Dataset Statistics:

These tables present the duration statistics for each subset and class within the TikHarm dataset.

This comprehensive dataset is invaluable for developing robust video classification models to automatically detect and categorize harmful content on social media platforms.

This dataset contains information on TikTok users' reports of videos and comments that include user claims. These reports flag content for moderator review, generating a significant volume of user reports that need timely attention.

TikTok is developing a predictive model to determine whether a video contains a claim or offers an opinion. A successful prediction model will help reduce the backlog of user reports and enable more efficient prioritization.

This dataset is intended for exploratory data analysis (EDA), statistical analysis, and predictive modeling. It has been created for pedagogical purposes and aims to facilitate learning and research in data analysis and machine learning

TikTok: What's trending and why?

A dataset for studying user preferences in social media

About this dataset

How do you measure the success of a video on social media? Is it the number of likes? The number of shares? The number of comments?

This dataset contains information on videos posted to the social media platform TikTok. The data includes the video ID, description, creation time, length, number of likes, shares, and comments, as well as a link to the video.

With this data, you can explore what factors make a video popular on TikTok and learn more about user preferences on this rapidly growing social media platform

How to use the dataset

This dataset can be used to study user preferences in social media. The data includes the number of likes, shares, comments, and plays for each video, as well as the video's description, length, and link

Research Ideas

• Identifying trends in social media
• Analyzing user preferences in social media
• Predicting future trends in social media

Acknowledgements

Dataset by TikTok

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: omnibuslaw_videos.csv | Column name | Description | |:---------------|:---------------------------------------------------------| | createTime | The date and time the video was posted. (DateTime) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of times the video has been shared. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

File: tiktok_liked_videos.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of times the video has been shared. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) | | user_name | The username of the person who posted the video. (String) |

File: trending.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | user_name | The username of the person who posted the video. (String) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of times the video has been shared. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

File: washingtonpost_videos.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | user_name | The username of the person who posted the video. (String) | | n_likes | The number of likes the video has received. (Integer) | | n_shares | The number of times the video has been shared. (Integer) | | n_comments | The number of comments the video has received. (Integer) | | n_plays | The number of times the video has been played. (Integer) |

This dataset contains information on over 19,000 TikTok videos, sourced from the Google Advanced Data Analytics course. It includes details on video duration, transcriptions, engagement metrics (views, likes, shares, comments), and author attributes like verification and ban status. Use this dataset to explore video trends, analyze social media engagement, or build machine learning models for content recommendation and trend prediction.

Key Features: - Claim Status: Status of claims on the videos. - Duration: Length of videos in seconds. - Engagement Metrics: Likes, shares, views, downloads, and comments. - Transcriptions: Textual transcriptions for content analysis. - Author Information: Verification and ban status of video creators.

TikTok Discourse on Ukraine Invasion

6 Million User's 16K Videos, 12M Comments

By [source]

About this dataset

This dataset provides unprecedented insight into public opinion and discourse related to a major foreign policy event: the hypothetical invasion of Ukraine in 2022. Through this dataset, researchers have access to 16 thousand TikTok videos, spanning 6 million unique users, as well as 12 million associated comments. Explore discourse themes on the platform and investigate how opinions are shaped by political events through sentiment analysis. As further research develops, compare findings from this dataset with similar datasets from other social media platforms to better illuminate the nature of digital public opinion and its potential influence on national policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an opportunity to gain a broad understanding of how users engage with and contribute to the conversation around a major political event on the TikTok platform. Here are some tips on how you can use this dataset:

• Analyze User Engagement: You can study user engagement by exploring the comment threads associated with each video in the dataset, examining trends for particular user types or locations, or exploring any features that could have predictive value in terms of engagement levels.
• Compare User Participation: You can compare user participation from different countries or regions by analyzing comments and likes over time in relation to nationality. This would allow you to better understand where conversations about this particular event is most popular, and which countries/regions are more likely to have an opinion about it.
• Explore Topics & Narratives: By taking advantage of NLP techniques such as sentiment analysis and topic modeling on comments data, you will be able to uncover common themes amongst videos with shared narratives related the event in question

By leveraging these tools, you will be able to extract meaning from this massive dataset and gain insightful information into individual users’ behavior as well as overall discourse around the invasion of Ukraine in 2022

Research Ideas

• Cultural attitudes towards the invasion of Ukraine in 2022: This dataset can be used to determine public attitudes towards the event by analyzing both the comments and videos from users, providing an alternative means of studying cultural predispositions than traditional polls or surveys.
• Influence of online communities on discussing issues: This dataset can be used to study how online communities influence people’s mindset and opinions on a certain topic. By analyzing how conversations change across different platforms, academics may be able to determine what makes certain communities more effective at forming consensus around issues compared to others.
• Interpersonal dynamics among users regarding significant events: Analyzing this data can shed light into how conversations turn into heated debates between two groups of users, establishing either agreement or dissent over a particular topic matter related to the invasion in 2022 as well as identifying which individuals are influential among certain circles for sparking engagement with their ideas or statements about their views towards said event

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: video_ids.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

🔥 What Makes Content Go Viral?

This dataset is designed to help data scientists, analysts, and researchers understand, analyze, and predict viral content across major social media platforms. It captures realistic engagement behavior, sentiment signals, and content attributes that influence virality in today’s digital ecosystem.

🌐 Platforms Covered

The dataset includes multi-platform data from: - TikTok - Instagram - X (Twitter) - YouTube Shorts

Each platform is represented with consistent metrics, making cross-platform comparison easy and reliable.

🧠 Dataset Features (Columns Explained)

🆔 Post Metadata

• post_id – Unique identifier for each post
• platform – Social media platform name
• content_type – Video, image, carousel, or text
• topic – Content category (Entertainment, Tech, Sports, etc.)
• language – Post language (EN, UR, HI, ES, FR)
• region – Geographic region of the post

⏰ Time & Trend Signals

• post_datetime – Date and time of posting Useful for time-series analysis, peak engagement detection, and trend forecasting.

#️⃣ Hashtags & Sentiment

• hashtags – Multiple trending hashtags per post
• sentiment_score – Emotional tone score (-1 = negative, +1 = positive)

Ideal for NLP tasks, sentiment analysis, and hashtag impact studies.

📈 Engagement Metrics

• views – Total views
• likes – Likes received
• comments – Number of comments
• shares – Number of shares

These metrics allow deep analysis of user interaction patterns.

⚙️ Engineered Features

• engagement_rate – Combined engagement normalized by views
• is_viral – Binary label indicating viral content

Perfect for machine learning models and classification tasks.

YouTube Shorts & TikTok Trends 2025

Overview

A global dataset capturing short-form video performance across YouTube Shorts and TikTok in 2025.
It includes over 50,000 video records, available in both raw and machine learning–ready formats.
Designed for reproducible EDA, dashboarding, and baseline ML modeling on social media engagement dynamics.

Files Included

All files are UTF-8 encoded, cleaned, and schema-aligned for direct analysis.

Key Columns (ML-Ready File)

• Identifiers: video_id, platform, country, category, creator_tier
• Engagement Metrics: views, likes, comments, shares, saves, completions
• Derived Ratios: engagement_rate = (likes + comments + shares) / views, plus save_rate, share_rate, comment_rate
• Signals: velocity indicators, rolling statistics, seasonality flags

Recommended Uses

• EDA: Analyze short-form engagement trends by country, platform, or content type
• ML Modeling: Classify trend_label or predict engagement_rate and views
• Dashboarding: Visualize global video trends and creator performance
• Market Research: Study cultural and regional patterns of viral content

Notes

• trend_label is a snapshot trend proxy; baseline models typically reach 25–35% accuracy without temporal features.
• publish_date_approx is derived and coarse — for trend direction only.
• The dataset contains metadata only (no media content).

If you find this dataset helpful, supporting it with an upvote helps others discover it too ✨

This dataset is web-scraped from popular short video platforms like YouTube Shorts, TikTok, and Instagram Reels. It captures user interaction data, including views, likes, comments, shares, and watch duration, along with multimodal features from video content like text (titles, descriptions), image (visual characteristics), and audio (sound properties). The data has been processed and flattened into a structured CSV format with 17,654 Rows.

Context

Who are the Top 100 TikTokers from Pakistan? Who to follow? how many followers they have? How many videos they have uploaded? This dataset answer all of these questions

Content

Top 100 TikTokers from Pakistan. The dataset contains their TikTok IDs, Names, Number of videos and Followers Count

Inspiration

Can you help us understand what contents our youth is consuming and how many hours we spend on TikTok as a Nation?

TikTok Dataset:

This is the dataset published in CVPR 2021 introduced in the paper: Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos

Project Website Code

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.

TikTok Dataset Directory Structure:

TikTok_dataset

| - 00001             (Sequence#)
|  | - images
|  |  | - 0001.png       (Frame#)
|  |  | - 0002.png
|  |  | - ....
|  | - masks
|  |  | - 0001.png
|  |  | - ....
|  | - densepose
|  |  | - 0001.png
|  |  | - ....
| - 00002
| - ...
| - 00340


TikTok_Raw_Videos

| - seq-00001-00009       (first sequence#-last sequence#)
|  | - dance-name.txt
|  | - video-link.txt
|  | - YouTube.mp4
| - ...
| - seq-00329-00340

Terms of usage and License:

The code and the TikTok dataset is supplied with no warranty and University of Minnesota or the authors will not be held responsible for the correctness of the code and data.

The code and the data will not be transferred to outside parties without the authors' permission and will be used only for research purposes. In particular, the code or TikTok dataset will not be included as part of any commercial software package or product of this institution. This work and dataset is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Citation:

If you find our dataset useful please consider citing us: ``` @InProceedings{Jafarian_2021_CVPR_TikTok, author = {Jafarian, Yasamin and Park, Hyun Soo}, title = {Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {12753-12762}}

@misc{jafarian2021selfsupervised, title={Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos}, author={Yasamin Jafarian and Hyun Soo Park}, year={2021}, eprint={2103.03319}, archivePrefix={arXiv}, primaryClass={cs.CV}}

This dataset contains metadata from TikTok videos in the beauty and personal care niche. The data is structured to analyze video performance, user interaction, and content features, with specific metrics such as play count, share count, comments, and video details. It also includes user-level attributes like follower count, region, and engagement metrics, enabling analysis of influencer activity and content trends in this domain.

Source: Public TikTok profiles collected via Apify (a web scraping tools).

Inspiration: Explore how users engage with TikTok content and profiles. Use this data to create predictive models or track trends in social media engagement.

Please upvote if you like this dataset

TikTok, known in China as Douyin (Chinese: 抖音; pinyin: Dǒuyīn), is a short-form video hosting service owned by Chinese company ByteDance. It hosts a variety of short-form user videos, from genres like pranks, stunts, tricks, jokes, dance, and entertainment with durations from 15 seconds to ten minutes. TikTok is an international version of Douyin, which was originally released in the Chinese market in September 2016. TikTok was launched in 2017 for iOS and Android in most markets outside of mainland China; however, it became available worldwide only after merging with another Chinese social media service, Musical.ly, on 2 August 2018.

TikTok and Douyin have almost the same user interface but no access to each other's content. Their servers are each based in the market where the respective app is available. The two products are similar, but features are not identical. Douyin includes an in-video search feature that can search by people's faces for more videos of them and other features such as buying, booking hotels and making geo-tagged reviews. Since its launch in 2016, TikTok and Douyin rapidly gained popularity in virtually all parts of the world. TikTok surpassed 2 billion mobile downloads worldwide in October 2020.

In this dataset you will find the details about top 1000 tiktokers all over the world.

This dataset provides information about the top 100 TikTok accounts worldwide in 2025, ranked based on their popularity. The data has been manually curated and includes essential metrics that reflect the performance and engagement of TikTok creators. It can be used for various purposes such as trend analysis, content strategy development, or understanding the growth of social media influencers.

Features Included: Rank: Ranking based on follower count. Uploads: The total number of videos uploaded by the account. Views: Total views generated by the account's videos. Followers: Number of followers for the account. Following: Number of accounts the user is following. Username: The username of the TikTok account.

This dataset is suitable for data analysis, machine learning model development, and studying trends in social media content.