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.

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.

Dataset

This dataset was created by Phụng Trương Thu

Released under CC0: Public Domain

Contents

This dataset provides a comprehensive and diverse snapshot of social media users and their engagements across various popular platforms such as Instagram, Twitter, Facebook, YouTube, Pinterest, TikTok, and Spotify. With 100 rows of anonymized data, it offers valuable insights into the dynamic world of social media usage. 😀

Each row in the dataset represents a unique user with a designated User ID and Username to ensure anonymity. Alongside user-specific details, the dataset captures essential information, including the platform being used, the post's content, timestamp, and media type (text, image, or video). Additionally, it tracks engagement metrics such as likes, comments, shares/retweets, and user interactions, providing an overview of the user's popularity and social impact. 💬

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The dataset also includes pertinent user attributes, such as account creation date, privacy settings, number of followers, and following. The users' profiles are further enriched with demographic characteristics, including anonymized representations of their age group and gender. 🗨️

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Hashtags, mentions, media URLs, post URLs, and self-reported location contribute to understanding user interests, content themes, and geographic distribution. Moreover, users' bios and language preferences offer insights into their passions, activities, and linguistic communication on the platforms.

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 ✨

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 ...

Context

TikTok's platform is mostly fueled by viral videos of users doing outlandish, scary, or funny things. On the platform, these trend and meme videos typically come with a hashtag that includes the word challenge. But what is a TikTok challenge and how do you find or create them? Here's everything you need to know.

This TikTok book challenge was made by @haleyisfearless, . It asks you to show, your prettiest book,your tiniest book a book you highly suggest a book you're currently reading and one of your favorite books . In the most basic sense, these challenges originate from viral TikTok challenge isn't complete without its defining hashtag in the video's description

These TikTok challenges are the perfect way to ease into what can be an intimidating social media platform and help you find your fellow book lovers.

Acknowledgements

This dataset is generated entirely from TikTok , so we want to thank @haleyisfearless for building such this challange video

Inspiration

the goal of this project is to make Python script which takes a video as input and returns all texts visible on the video. the videos are titlok videos so texts can appear everywhere on screen, with different background, font size etc..

Amber Heard TikTok Data from 2022 under 57 hashtags. Videos with full Metrics and information fields. On the Disinformation Operation harming Human Rights Activist Amber Heard. Comments of each post are included in the scraper.

TikTok Hashtags: - Positive, Neutral, and Negative of 57 hashtags. Positive and Neutral: 1. amberheard 2. amberheardmera 3. amberheardisinnocent 4. amberheardaquaman 5. amberheardisasurvivor 6. amberheardisavictim 7. ibelieveamberheard 8. darvodepp 9. istandwithamber 10. istandwithamberheard 11. loveamberheard 12. wearewithyouamberheard 13. westandwithamberheard 14. standwithamberheard 15. teamah 16. teamamberheard 17. justiceforamberheard 18. johnnydeppisawifebeater 19. johnnydeppisguilty

Negative: 1. aclusupportsabusers 2. amberhearddoesnotspeakforme 3. amberheardforjail 4. amberheardforprison 5. amberheardisacriminal 6. amberheardisafraud 7. amberheardisanabuser 8. amberheardisapsycopath 9. amberheardisguilty 10. amberheardisoverparty 11. amberheardjohnnydepp 12. amberheardperjury 13. amberheardslawyersucks 14. amberheardtrial 15. amberheard💩 16. amberheard🤡 17. amberheard🤮 18. amberpoop 19. amberturd 20. boycottaquaman2 21. boycottloreal 22. boycottwarnerbros 23. boycottwarnerbrothers 24. deppheardtrial 25. deppvheardtrial 26. deppvsheard 27. fireamberheard 28. istandbyjohnnydepp 29. johnnydepp 30. johnnydeppamberheard 31. johnnydeppisinnocent 32. johnnydepptrial 33. johnnydeppvsamberheard 34. justiceforjohnnydepp 35. putamberheardinjail 36. recastmera 37. teamjd 38. teamjohnnydepp

Each Hashtag Feed shows 1000 videos per day of collections.

From Public Research Study: https://github.com/RescueSocialTech/Amber-Heard_Disinformation_Operations_Bots

This dataset is an extension of the TikHarm dataset, created to enhance multimodal harmful content detection on TikTok. It was developed as part of the MTikGuard system, a real-time moderation pipeline designed to protect young audiences from unsafe TikTok videos.

🔹 Purpose

The dataset supplements TikHarm with 775 additional annotated videos, collected from TikTok trending and targeted hashtag queries. These videos were selected to address class imbalance and content diversity gaps in the original dataset, improving model generalization for real-world deployment.

🔹 Content

Each video is labeled into one of four categories: - Safe - Adult Content - Harmful Content (e.g., dangerous challenges, graphic violence) - Suicide / Self-harm

🔹 Data Collection & Annotation

Collection: Automated crawling using Selenium and TikTok Content Scraper, coordinated via Apache Airflow and Apache Kafka.

Annotation: Conducted via a custom web-based tool, following detailed guidelines to ensure consistency and reliability. Multiple annotators reviewed each video, with disagreements resolved via majority voting.

Class balance: Oversampling of underrepresented categories (e.g., Suicide, Harmful Content) during collection.

🔹 Applications

Training and evaluating multimodal classification models for harmful content detection.

Benchmarking real-time content moderation pipelines.

Research on multimodal fusion strategies and multi-label classification.

🎬 Movie Success Prediction Dataset

This dataset was curated to support machine learning models that predict movie success based on a wide range of multi-modal features, including cast popularity, sentiment analysis, audio-visual cues, social media engagement, and metadata such as budget and IMDb rating.

📦 Dataset Overview

The dataset consists of 36 engineered features extracted from various sources:

• Cast and Crew Insights (e.g., popularity trends, number of cast members)
• Sentiment Analysis from YouTube Comments using VADER
• Audio Features from movie trailers using VGGish 3
• Video Features using ResNet-based frame analysis
• TikTok Popularity Signals (hashtags, views, engagement rate)
• Movie Metadata (e.g., budget, IMDb rating)

Each row represents one movie. The dataset is ideal for classification or regression tasks related to box office success, revenue prediction, or audience engagement forecasting.

📊 Feature Mapping

🛠️ Feature Engineering Highlights

• Audio features were extracted using the VGGish 3 model, widely used in speech emotion recognition tasks.
• Video features were obtained from a ResNet-based model analyzing brightness, scene change rate, colorfulness, and emotion cues.
• Sentiment scores were derived from YouTube comments using VADER, capturing positive, neutral, and negative sentiment proportions.
• TikTok engagement metrics were collected using hashtag data, capturing likes, views, shares, and overall engagement rate.
• Budget transformations such as log, square root, and squared values are included, along with an interaction feature with IMDb rating.

💡 Potential Use-Cases

• Predict box office revenue or success labels
• Analyze which audio-visual cues correlate with public interest
• Build early-stage predictors of movie success using trailers and social signals
• Inform marketing strategies using real-time sentiment and TikTok trends

📥 Data Sources

• IMDb for metadata
• YouTube (comments and trailers) for sentiment and audio/visual analysis
• TikTok for hashtag popularity and engagement stats
• In-house processing for video/audio feature extraction using ResNet and VGGish 3

🚀 Whether you're working on predictive modeling, multimedia analysis, or social signal correlation, this dataset provides a diverse feature set to explore what makes a movie successful.

Sustainability Social Media Posts Dataset

Description

This synthetic dataset contains 1,000+ social media posts related to sustainability and climate action across multiple platforms. The data captures engagement metrics, user information, content themes, and calls to action from climate and environmental advocacy posts spanning from August 2024 to August 2025.

Dataset Columns

Key Statistics

• Total Posts: 13,144 entries
• Date Range: August 2024 - August 2025
• Platforms: 7 different social media platforms
• Geographic Coverage: Global locations including major cities across continents
• Topic Categories: 50+ distinct climate and sustainability topics
• Sentiment Distribution: Mix of positive, negative, and neutral posts