Please cite the following paper when using this dataset: N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A.Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” arXiv [cs.CY], 2024. Available: http://arxiv.org/abs/2406.07693 Abstract This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

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.

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.

We used TikTok’s built-in account analytics to download and record video and account metrics for the period between 10/8/2021 and 2/6/2022. We collected the following summary data for each individual video: video views, likes, comments, shares, total cumulative play time, average duration the video was watched, percentage of viewers who watched the full video, unique reached audience, and the percentage of video views by section (For You, personal profile, Following, hashtags).
We evaluated the “success” of videos based on reach and engagement metrics, as well as viewer retention (how long a video is watched). We used metrics of reach (number of unique users the video was seen by) and engagement (likes, comments, and shares) to calculate the engagement rate of each video. The engagement rate is calculated as the engagement parameter as a percentage of total reach (e.g., Likes / Audience Reached *100).

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.

📲 Example Dataset: TikTok Scraper Tool

👉 Start Scraping TikTok: TikTok Scraper Tool

  ✨ Key Features

⚡ Instant Transcription – Turn any TikTok video into an AI-ready transcript
🎯 Metadata – Get the title, language description, and video hashtags
🔗 URL-Based Access – Just drop in a TikTok video URL to start scraping
🧩 LLM-Ready Output – Receive clean JSON ready for agents, RAG, or AI tools
💸 Free Tier – Use up to 100 queries during the beta period
💫 Easy… See the full description on the dataset page: https://huggingface.co/datasets/MasaFoundation/TikTok_Most_Shared_Video_Transcription_Example.

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.

A ranked dataset of the most viral TikTok videos in 2024, based on total views and creator engagement.

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.

Description of video features and demographics of TikTok videos uploaded by older adults by valence of content a.

📲 Example Dataset: TikTok Scraper Tool

👉 Start Scraping TikTok: TikTok Scraper Tool

  ✨ Key Features

⚡ Instant Transcription – Turn any TikTok video into an AI-ready transcript
🎯 Metadata – Get the title, language, description, and video hashtags
🔗 URL-Based Access – Just drop in a TikTok video URL to start scraping
🧩 LLM-Ready Output – Receive clean JSON ready for agents, RAG, or AI tools
💸 Free Tier – Use up to 100 queries during the beta period
💫 Easy… See the full description on the dataset page: https://huggingface.co/datasets/MasaFoundation/Tiktok_Chatgpt_Prompt_Guide.

Replication Data for: How effective are TikTok misinformation debunking videos? Data, Preregistration, Qualtrics, Scripts, Videos

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

A structured dataset comparing viral view thresholds and timeframes across major platforms, including TikTok, YouTube (long-form & Shorts), Instagram Reels, Facebook, Twitter (X), LinkedIn Video, and LinkedIn Posts.

As social platforms experience an influx of diverse content from users, the need to determine high-quality contributions becomes crucial, especially for educational purposes. This paper highlights the pivotal role of quality in assessing how educational-purposed user-generated content (UGC) shapes user experiences, fosters engagement, and establishes credibility. This study proposes a computational framework using a quasi-experimental evaluation through the sorting-based ELimination Et Choice TRanslating Reality, termed ELECTRE-SORT, with a dataset randomly generated from normally distributed user evaluations. Considering the diverse nature of contents, the method evaluates 16 educational-purposed UGC videos from different online media platforms (i.e. Facebook, YouTube, TikTok). These videos were categorized based on their concordance and discordance to three (3) main criteria: content quality, design quality, and technology quality. Employing the ELECTRE-SORT reveals that most UGC videos (i.e. 14 out of 16) fall into the “medium quality” category, possessing a considerable standard for the quality of educational purpose content. Their characteristics generally satisfy the quality attributes and can be used to guide the development of future relevant UGC videos. Finally, to demonstrate the robustness of the proposed approach, we presented a sensitivity analysis by designing different weight assignments to the quality attributes. Practical insights are outlined in this work.

In early 2023, a study measured the effectiveness of TikTok advertising campaigns in driving awareness and viewership of shows, movies, and live events on broadcast, cable, and steaming channels. It was found, among others, than ** percent of TikTok campaigns that advertised such content, contributed to incremental tune-ins. The median cost per tune-in stood at **** U.S. dollars.

A dataset listing the top TikTok creators by follower count as of the end of 2024, including content themes and audience size.

This deposit provides the analyzed dataset (anonymized) and the R scripts to reproduce the figure/tables in our manuscript. Our paper examines the emotional cues and identity cues used in TikTok videos about (anti) vaccination.

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.

The table contains data from TikTok videos that portray dogs and their caregivers communicating with one another using soundboards. It includes the date each video was posted; the TikTok account on which each video appears; the description of each video by the account user; hashtags given to each video by the user; the number of views for each video; the number of likes, comments, and saves added to each video by its viewers; and the duration of each video. This data provided the authors of the study with a general overview of the talking-dog videos, including the videos' shared contemporariness, popularity and brevity. Identification of these qualities shaped the analysis of the videos, particularly with regard to their history and their figuration of human-canine relations. The paper concludes that, while the use of a soundboard may appear to offer direct insight into a dog's thoughts (historically precedented in canine performances dating back at least to the Middle Ages), this method paradoxically relies on extensive training and human interpretation, overshadowing other kinds of canine sonic expression. The authors suggest that such videos risk encouraging anthropomorphic views, making people less attentive to dogs’ nonverbal communication and more inclined to view them as infant-like rather than as distinct adult animals.