This dataset contains comprehensive information about TikTok posts, originally fetched from RapidAPI. It provides valuable insights into various aspects of TikTok content, including details about the videos, their creators, and audience engagement metrics.

Here's a breakdown of the columns included in this dataset:

video_id: A unique identifier for each TikTok video. author: The username or handle of the TikTok account that posted the video. description: The textual description or caption provided by the creator for the video. (Note: This column contains some missing values.) likes: The number of likes the video has received. comments: The number of comments on the video. shares: The number of times the video has been shared. plays: The total number of plays or views the video has accumulated. (Note: This column contains some missing values.) hashtags: A list of hashtags used in the video's description, which helps categorize content and improve discoverability. (Note: This column contains some missing values.) music: Information about the background music or sound used in the video. create_time: The timestamp indicating when the video was created or published. (Note: This column contains some missing values.) video_url: The direct URL to the TikTok video. fetch_time: The timestamp when the data for the video was fetched from the API. (Note: This column has a high number of missing values.) views: Another metric for the number of views. (Note: This column has a high number of missing values and appears to overlap with plays.) posted_time: The time the video was posted. (Note: This column has a high number of missing values and appears to overlap with create_time.) Potential Uses of This Dataset:

Content Analysis: Analyze popular TikTok content by examining descriptions, hashtags, and engagement metrics. Trend Identification: Identify trending topics, music, and creators on TikTok. Audience Engagement Studies: Understand how different types of content generate likes, comments, shares, and plays. Creator Analysis: Study the posting habits and performance of various TikTok creators. Social Media Research: Conduct research on the dynamics of content dissemination and user interaction on short-form video platforms. Notes on Data Quality:

The description, plays, hashtags, and create_time columns have some missing values, which may require handling (e.g., imputation or removal) depending on your analysis. The fetch_time, views, and posted_time columns are largely empty, suggesting they may not be reliable for comprehensive analysis. It is recommended to primarily rely on create_time for timestamps and plays for engagement metrics. This dataset can be a valuable resource for anyone looking to explore the vast and dynamic world of TikTok content and user engagement.

Explore the fascinating world of TikTok with our comprehensive TikTok User Profiles Dataset. Whether you're a marketer, researcher, or enthusiast, this dataset provides a wealth of information on public TikTok profiles, allowing you to extract valuable business and non-business insights. You have the flexibility to purchase the complete dataset or tailor it to your specific needs by utilizing a range of filtering options.

Key Data Points:

• Timestamp
• Account name
• Nickname
• Bio
• Average engagement score
• Creation date
• Verification status
• Likes count
• Followers count
• External link in bio
• and many more

Popular Use Cases: Unleash the potential of this dataset for a variety of applications, including:

Sentiment Analysis: Gain deep insights into user sentiment by analyzing profiles' content, engagement, and interactions. Brand Monitoring: Track mentions of your brand, products, or services across TikTok, understanding how users perceive and engage with your offerings. Influencer Marketing: Identify potential influencers by assessing their follower count, engagement, and overall impact, helping you make informed collaboration decisions. Audience Insights: Understand your target audience by examining user bios, locations, and other profile details, aiding in tailoring your content and strategies.

Source: BrightData

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.

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!

A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. Videos and related metadata were downloaded using a third-party TikTok Scraper using the search term #coronavirus. Videos were reviewed for content and data were entered on a spreadsheet.

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

This dataset 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.

TikTok Video Analytics Dataset

Sample TikTok video dataset with comprehensive engagement metrics and metadata. Each row represents a single TikTok video with content and detailed analytics. This is a sample dataset. To access the full version or request any custom dataset tailored to your needs, contact DataHive at contact@datahive.ai.

  Files Included

train.csv – TikTok video analytics data

  What's included

Video URLs and identifiers Comprehensive engagement… See the full description on the dataset page: https://huggingface.co/datasets/datahiveai/Tiktok-Videos.

Context

TikTok, known in China as Douyin (Chinese: 抖音; pinyin: Dǒuyīn), is a video-focused social networking service owned by Chinese company ByteDance Ltd. 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.[7][8][9][10] TikTok is an international version of Douyin, which was originally released in the Chinese market in September 2016.[11] 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. As of October 2020, TikTok surpassed over 2 billion mobile downloads worldwide.[Source: Wikipedia]

This dataset belongs to the TikTok app available on the Google Play Store. The Dataset mostly has user reviews and the various comments made by the users.

Content

The content of the various columns is listed below. Please find the description for each column.

Acknowledgements

TikTok image - TikTok

The Excel data titled “TikTok’s User Comment on Skincare Product” contains 200 user comments regarding consumers’ and viewers’ opinions and perceptions of skincare products on the TikTok platform, specifically focusing on the local brand Camille Beauty. This dataset includes opinions, perceptions, reviews, and sentiments. It is stored in Excel format for text analysis, sentiment analysis, and social media marketing research. The dataset aims to provide insights and an overview of consumer behavior and public perception of the local skincare brand Camille Beauty on the TikTok digital platform.

Research

This dataset has been used in the my research https://www.semanticscholar.org/reader/ef5d55a1c2977f202bf17f809ef60e3e8203eb4a. This dataset is available for access and can be used for further research purposes with proper reference to the aforementioned study.

Context

This dataset includes user reviews from the TikTok Tokopedia Seller Center. Each review is accompanied by a sentiment label indicating whether the review is negative or positive. It provides valuable insights for sentiment analysis, particularly useful for e-commerce platforms aiming to understand customer feedback.

Content

• content = Textual review content provided by users, likely in informal Indonesian language.
• sentiment = Categorical label indicating the sentiment of each review, with possible values of positive or negative.

🎬 YouTube Shorts & TikTok Trends (2025)

Author: Tarek MasryoLicense: CC BY 4.0 A structured snapshot of short-form video activity across YouTube Shorts and TikTok during 2025 (Jan–Aug).Built for content intelligence, analytics dashboards, and ML baselines (classification/regression).

  What’s inside

This repository ships:

Two loadable dataset configs (via datasets.load_dataset): default → ML-ready table (cleaned + modeling-friendly) raw → raw video-level table (wider… See the full description on the dataset page: https://huggingface.co/datasets/tarekmasryo/youtube-tiktok-trends-dataset-2025.

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.

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.

Dataset

This dataset was created by Faisal

Released under Other (specified in description)

Contents

Tiktok

## Overview

Tiktok is a dataset for object detection tasks - it contains Chu Va Hinh annotations for 611 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

TikTok Political Stance Dataset (Taiwan 2025)

  Dataset Summary

The TikTok Political Stance Dataset (Taiwan 2025) is the first publicly available Traditional Chinese political stance detection dataset collected from TikTok during Taiwan's 2025 Great Recall movement. The dataset contains 9,302 annotated TikTok comments together with rich video-level and comment-level annotations. Unlike conventional stance detection datasets that assume a single target per instance… See the full description on the dataset page: https://huggingface.co/datasets/LunaTsai/tiktok-political-stance-dataset-taiwan-2025.

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.

Introducing a comprehensive and meticulously curated dataset: "European Interest Groups' Social Media Engagement Dataset." This dataset offers a panoramic view of the digital footprint and social media presence of various interest groups within Europe. Encompassing a diverse range of platforms including Twitter, Facebook, Instagram, TikTok, and YouTube. This are the variables:

1. Name: The name of the organization

2. twitter_link: The link of twitter if it is

3. facebook_link: The link of facebook if it is

4. instagram_link: The link of instagram if it is

5. tiktok_link: The link of tiktok if it is

6. linkedin_link: The link of linkedin if it is

7. youtube_link: The link of youtube if it is

With a focus on transparency and relevance, this dataset presents a wealth of information that delves into the strategies, content, and reach of interest groups across these dynamic online platforms. Researchers, policymakers, and analysts can explore trends, patterns, and correlations between online activities and real-world influence, shedding light on the evolving landscape of digital interaction within the realm of European interest groups.

Dataset

This dataset was created by Shaquille William

Released under CC0: Public Domain

Contents