his dataset contains a large collection of TikTok video metadata fetched using the TikTok Scraper API. It includes videos from multiple regions (e.g., US, India,) and categories (e.g., fyp, dance, comedy, food, travel, etc.). Each video entry

provides detailed information such as:

Video ID: Unique identifier for the video. Region: The region where the video is popular. Category: The keyword/category used to fetch the video (e.g., dance, comedy). Title: The title of the video. Duration: The length of the video in seconds. Play URL: Direct link to the video. Watermarked URL: Link to the watermarked version of the video. Cover Image: URL of the video's cover image. Music URL: Link to the music used in the video. Timestamp: The date and time when the data was fetched.

How This Dataset Can Be Helpful

Trend Analysis: Analyze trending videos across different regions and categories. Identify patterns in video popularity based on region, duration, or category.

Machine Learning: Train models to predict video popularity based on features like duration, region, and category. Build recommendation systems for TikTok videos.

Content Moderation: Use the dataset to analyze video content for moderation purposes.

Sentiment Analysis: Perform sentiment analysis on video titles to understand user preferences.

Cross-Region Insights: Compare video trends across different regions to understand cultural differences.

How to Use This Dataset Filter by Region: Analyze videos from a specific region (e.g., US or India).

Filter by Category: Focus on videos from a specific category (e.g., dance or comedy).

Trend Analysis: Identify trending videos based on timestamp and region.

Machine Learning: Use the dataset to train models for video popularity prediction or recommendation systems.

Trending on TikTok

We are probably all familiar with TikTok. People tend to spend hours each day scrolling through the millions of videos which are uploaded every single day. Not to mention the uploaders who are giving anything to get as many likes and followers as possible. But what makes one TikTok video a true hit or a miss? I give you an opportunity to figure this out ;)

I scraped the first 1000 trending videos on TikTok, using an unofficial TikTok web-scraper. Note to mention I had to provide my user information to scrape the trending information, so trending might be a personalized page. But that doesn't change the fact that certain people and videos got a certain amount of likes and comments.

I transformed the data into usable csv files and attached the actual videos as well.

What's in the files

Videos.zip This file contains the actual 1000 trending TikTok videos. Each filename corresponds to the id key in the trending.json file.

trending.json The raw scraped dataset. I figured splitting up the dataset resulted in messy errors. For example: a user might have one avatar while posting a video and another while posting the next video. This resulted in multiple users with the same name, id etc. except for the avatar. So I decided to post the raw data and I will show you how to translate this multi-level JSON structure to a single DataFrame in my first Notebook.

Acknowledgements

Many thanks to Andrew Nord the creator of the tiktok-scraper, and his contributers.

Inspiration

So what does make a TikTok video a true hit? Is it the moment when a video is uploaded? Or perhaps the amount of followers is an important factor? Maybe the hashtags or even the music being used?

So... are you the one who unlocks the mystery?

Dataset

This dataset was created by Marcus Ong

Released under CC0: Public Domain

Contents

This dataset captures the pulse of viral social media trends across TikTok, Instagram, Twitter, and YouTube. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

• Trend analysis 🔍
• Sentiment modeling 💭
• Understanding influencer marketing 📈

Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

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 ...

🚀TikTok Shop Viral Video Sales, Attribution & CAC Analytics Dataset

📊 100,000 Transactions • 180+ Features • Creator Economics • Multi-Touch Attribution • CAC Optimization Traditional e-commerce analytics models are failing. In modern social commerce ecosystems like TikTok Shop, virality, creator authority, and micro-retention curves dictate revenue—not standard search traffic.

This dataset provides a production-grade analytical environment containing 100,000 granular observations and 180+ features. It bridges the gap between unstructured social media video engagement (hooks, rewatches, drop-offs) and enterprise financial performance (ROAS, CPA, Net Profit Margins, Multi-Touch Attribution).

💡 Why This Dataset Beats Standard Datasets End-to-End Customer Path: Tracks the complete journey from video impression and 3-second hook to product conversion and return risk.

Modern Social Commerce Vectors: Includes influencer tiering, affiliate tracking, and viral video hook dynamics that traditional retail datasets ignore.

Advanced Financial Metrics: Measures true net margins after creator commissions, platform fees, shipping costs, and customer acquisition expenses.

Multi-Touch Attribution Engine: Features First-Touch, Last-Touch, Linear, Position-Based, and Data-Driven revenue attribution models.

🧬 Dataset Schema & Feature Vectors (180+ Features) The data is structured into 6 interconnected analytical vectors:

1. 🎬 Video Performance & Content Hook Dynamics Video runtime, unique impressions, likes, shares, saves, retention curves at 3s and 10s, exact drop-off second, rewatch ratio, viral score, and hook clarity ratings.

2. 👤 Creator & Affiliate Intelligence Creator tier (Micro, Mid, Mega), follower count, engagement velocity, historical conversion authority, affiliate commission tier, and influencer ROI impact.

3. 💰 Financials & Unit Economics Gross revenue, net profit margin, Cost Per Acquisition (CPA), Customer Acquisition Cost (CAC), Return on Ad Spend (ROAS), platform fees, and marketing ROI.

4. 📈 Multi-Touch Attribution Models First-click revenue, last-click revenue, linear attribution, time-decay attribution, position-based model, and machine learning attribution confidence scores.

5. 🛒 Funnel & Traffic Analytics Product page clicks, add-to-cart rate, checkout abandonment rate, profile visits, traffic origin, and scroll depth.

6. 📦 Operations, Logistics & Profitability Shipping duration, delivery failure rate, return reason codes, processing cost, refund impact, and net contribution margin.

🎯 Machine Learning & Predictive Modeling Targets Use this dataset to build models targeting real-world business challenges:

Is_Viral (Binary Classification): Predict whether a video will achieve viral reach based on early 3-second hook metrics.

Net_Profit_Margin (Regression): Predict true profitability after subtracting creator commissions, return rates, and CAC.

Attribution_Model_Winner (Multi-Class Classification): Classify which attribution model best reflects true conversion impact based on customer path complexity.

Customer_Return_Probability (Predictive Modeling): Predict item return risk using product category, video claims, and shipping delays.

🚀 Quick-Start Analysis Snippet (Python)

import pandas as pd import numpy as np

Load dataset

df = pd.read_csv("tiktok_shop_viral_analytics_100k.csv")

1. Calculate Virality vs. Net Profitability Ratio

df['CAC_Efficiency'] = df['Net_Revenue'] / (df['CAC'] + 1e-5)

2. Compare ROAS across Creator Tiers

creator_perf = df.groupby('Creator_Tier')[['ROAS', 'Net_Profit_Margin', 'CAC']].mean() print("=== Creator Economics Overview ===") print(creator_perf)

3. Attribution Discrepancy Index

df['Attribution_Delta'] = df['Data_Driven_Revenue'] - df['Last_Click_Revenue'] print(" === Revenue Misattributed by Last-Click Model ===") print(df['Attribution_Delta'].describe())

🛠️ Recommended Portfolio Projects Project 1: Executive Creator Economics Dashboard (Power BI / Tableau) Visualize ROAS vs. Creator Commission rates across 100K transactions.

Project 2: Multi-Touch Attribution Engine (Python / SQL) Compare First-Touch vs. Data-Driven Attribution to reallocate marketing spend.

Project 3: Viral Hook & Drop-off Predictor (XGBoost / LightGBM) Predict video conversion failure using early 3-second viewer retention signals.

📌 Community Engagement & License License: CC0: Public Domain (Free for personal, academic, and commercial portfolio use).

Support: If you find this dataset valuable for your research, portfolio, or machine learning experiments, please ⭐ UPVOTE to support future dataset releases!

Showcase:Feel free to share your EDA notebooks and dashboard links in the Code and Discussion tabs!

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.

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.

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) |

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.

As of January 2022, The United States was the country with the largest TikTok audience by far, with approximately 131 million users engaging with the popular social video platform. Indonesia followed, with around 92 million TikTok users. Brazil came in third, with 74 million users using TikTok to watch short-videos.

the Tik Tok app became popular in 2019 and during the pandemic it exploded in popularity and this made me inspired to create a profitability topic that Tik Tok is giving to sub-famous people to have monetary collection and have money to increase their content productivity and consequently improve your cinematography condition and your condition to have a better living condition for your monetization contribution and this inspired me to create about the thousand most profitable Tik tokers

About This Dataset This dataset is a sample from WebAutomation's TikTok Profiles Dataset, containing publicly available information from TikTok creator and business profiles. The sample includes approximately 100M TikTok profiles and provides a diverse snapshot of creators, influencers, brands, and organizations across multiple industries, languages, and regions. It is designed for researchers, marketers, data scientists, creator economy platforms, and businesses seeking insights into TikTok's rapidly growing ecosystem.

What's Included

Each row represents a single TikTok profile and may include the following fields: username — TikTok username tiktok_id — TikTok account identifier sec_uid — TikTok secure unique identifier display_name — Public display name bio — Profile biography external_url — Website or external link listed in the profile avatar_url — Profile image URL language — Primary detected profile language followers_count — Number of followers following_count — Number of accounts followed likes_count — Total profile likes received video_count — Number of published videos friends_count — Number of mutual connections (where available) digg_count — Number of liked videos is_verified — Verification status is_private — Indicates whether the account is private is_organization — Organization account indicator is_commerce_user — Commerce-enabled account indicator is_tt_seller — TikTok Shop seller indicator has_active_live_room — Indicates active livestream availability open_favorite — Public favorites visibility setting comment_setting — Comment permissions setting duet_setting — Duet permissions setting stitch_setting — Stitch permissions setting download_setting — Video download permissions setting created_at — Account creation date (where available) nickname_modified_at — Most recent nickname change timestamp username_modified_at — Most recent username change timestamp scraped_at — Date and time the data was collected

Additional fields may be included depending on the data collection period and TikTok profile attributes.

Data Collection

Data is collected from publicly accessible TikTok profile pages using WebAutomation's proprietary web data infrastructure. No private APIs, account credentials, or authentication-restricted information are used. All collected information is publicly available at the time of collection.

Scrape date of this sample: 6/7/2026

Use Cases Influencer discovery and creator segmentation Creator economy research Social media analytics Audience and engagement analysis Brand partnership identification Market intelligence and trend analysis Machine learning and recommendation systems Academic and industry research

Data Quality The sample dataset has undergone basic quality checks, including: Removal of duplicate profiles Validation of unique account identifiers Verification of scrape timestamps Standardization of profile-level metrics Filtering of incomplete or invalid records

Full Dataset

This is a sample only.

WebAutomation maintains significantly larger TikTok datasets covering creator profiles, engagement metrics, account attributes, business accounts, and public profile metadata at scale.

👉**Explore more datasets:** https://webautomation.io

For custom data delivery, API access, enrichment projects, or enterprise licensing, contact: victor@webautomation.io

About WebAutomation

WebAutomation.io is a web data and scraping company specializing in large-scale public datasets across social media platforms, marketplaces, review sites, and business directories. We provide structured datasets and data collection solutions for market research firms, analytics platforms, AI companies, enterprise teams, and data-driven organizations worldwide.

Multimodal Filipino Scam Video Dataset

A multimodal dataset for training and distilling models that detect online financial scams in short-form video. Each example is a YouTube or TikTok video reduced to the signals a detector needs: sampled frames, a speech transcript, on-screen OCR text, and the title / description, paired with a scam / legitimate label and a policy-grounded rationale.

This is Version 1: the raw, as-collected release. Labels and rationales come from a mix of automated ("agentic") verification, companion-JSON sources, and partial human review, so their quality is uneven (see Known limitations). A cleaned, re-verified version is planned.

What's inside

• 2,000 videos across 6 scam archetypes: 1,343 YouTube + 657 TikTok
• 1,000 scam and 1,000 legitimate (perfectly balanced)
• 59,656 extracted frames (PNG; avg about 30 per video)
• 257 audio tracks (WAV; giveaway category in v1); where speech is present a Whisper transcript is also embedded in each video's JSON
• One companion JSON per video
• Multilingual content (English, Filipino / Tagalog, and others)

Categories (scam archetypes)

Three global scam types (crypto, giftcard, giveaway) plus three Filipino-specific archetypes (ewallet, p2e, task).

Detection criteria (C1-C7)

Every scam label is grounded in seven policy-based criteria (the OptiScam C1-C7 scheme, adapted from the YouTube-policy criteria in Kulsum et al.). The criteria a video matches are recorded in the companion JSON, in the response "Criteria Hits" line and the human_review annotation.

• C1 Criminal claim: promotes illegal activity or stolen credentials as a money path.
• C2 Unbounded giveaway: free cash, phones, or load with no sponsor, qualifier, or limit.
• C3 Off-site redirect: pushes viewers to Telegram, WhatsApp, sketchy domains, or DM flows.
• C4 Fast-money lure: promises large returns with little effort or skill in a short window.
• C5 Harmful link: phishing URL, fake login page, or OTP / PIN / seed-phrase requests.
• C6 Get-rich-quick: mobile mining, task apps with guaranteed earnings, or no-loss trading bots.
• C7 Impersonation: mimics a real bank, celebrity, government agency, or known brand.

Extended criteria (Filipino scam archetypes)

On top of the global scam types, the dataset adds three Filipino-specific archetypes:

• P2E (Play-to-Earn): victims download a game or app and are told they earn crypto or cash by playing.
• Task (Telegram / Group Task): victims join a Telegram group or chat and are paid small amounts to complete "tasks".
• E-Wallet (GCash / Maya phishing): fake giveaways or support videos instruct viewers to send their GCash or Maya OTP, PIN, or login credentials.

Data collection and methodology

Sources: publicly posted short-form videos scraped from YouTube and TikTok (browser automation with Playwright and Selenium), across the six scam archetypes above plus matched legitimate videos on the same topics, with a Philippine / Filipino focus.

Per-video pipeline: 1. Frame sampling - up to about 60 keyframes per video are extracted and saved as PNG under images/. 2. Speech to text - audio is transcribed with OpenAI Whisper and stored as the whisper turn (raw WAV is shipped for the giveaway category). 3. On-screen text - timestamped OCR is captured as ocr_temporal_text and the ocr turn. 4. Metadata - the video title and description are recorded as the human turn. 5. Labeling - teacher LLM labelers (Google Gemma and Google Gemini) assign a scam or legitimate verdict, a rationale, and the matched C1-C7 criteria, stored as the response turn and the human_review annotation.

Verification and balancing: labels come from a mix of automated / agentic verification, companion-JSON label sources, and partial human review; each video keeps a provenance rank (0 agentic-verified, 1 companion-json, 2 none) in its category .transfer_manifest.json. The released set is balanced to 1,000 scam and 1,000 legitimate. Only public content is used and no personal data is deliberately collected.

Folder layout

Each video lives in category / {scam | legitimate} / video_folder / and contains: a video_folder.json (metadata + label), an images/ folder of frames named video_folder_N.png, and, for giveaway in v1, a video_folder.wav audio file. The video_folder name is either youtube_ID or tiktok_ID. Each category also includes a .transfer_manifest.json recording label provenance.

Companion ...

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.

https://s3-prod.adage.com/s3fs-public/20230807_celeb_run_agencies_3x2.jpg" alt="Celebs"> The dataset you provided appears to focus on TikTok celebrities and contains the following columns:

Celebrity: The name or handle of the TikTok celebrity. Followers: The number of followers the celebrity has, often represented in millions or billions. Following: The number of accounts the celebrity follows, which may be represented as thousands (K) or just a number. Likes: The total number of likes the celebrity’s videos have received, often represented in millions or billions. T.Videos: The total number of videos posted by the celebrity. Video Duration: The typical duration of their videos, which ranges from a few seconds (e.g., 10 - 15 seconds) to over a minute. Average Views: The average number of views their videos receive, often in millions. Net Worth: The estimated net worth of the celebrity, often represented in millions or billions of dollars. Most Viewed Video: The number of views for their most popular video, usually in millions or billions. Most Liked Video: The number of likes for their most popular video, represented in millions or billions. Video Category: The types or categories of videos the celebrity posts, such as comedy, dance, acting, challenges, etc.

With the rise of short-form video platforms such as TikTok, Instagram Reels, YouTube Shorts, and Snapchat, the music industry is yet again adapting to changing consumer consumption trends.

Here's the data for music used in trending videos in the second half of 2022.

Techsalerator’s YouTube & Video Data for Indonesia

Techsalerator’s YouTube & Video Data for Indonesia aggregates comprehensive insights from video platforms to provide a detailed view of the country’s digital video ecosystem. This dataset captures video metadata, creator activity, audience engagement, and content performance signals across Indonesia, helping organizations analyze content trends, viewer behavior, and digital media consumption patterns.

This dataset is designed to support media platforms, advertisers, researchers, NGOs, and policy analysts seeking insights into video engagement, creator growth, and content distribution across Indonesia.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Top 5 Key Data Fields

1. Video Metadata (Title, Category, Language, Upload Date)
   Identifies core attributes of videos including topic, format, and publication timing.

2. Channel & Creator Information
   Captures data on content creators, subscriber counts, upload frequency, and channel growth.

3. Engagement Metrics (Views, Likes, Comments, Shares)
   Measures audience interaction and content performance across videos.

4. Audience Demographics & Geography
   Provides insights into viewer location, age distribution, and engagement behavior.

5. Watch Time & Retention Metrics
   Tracks average viewing duration, retention rates, and drop-off points.

Top 5 YouTube & Video Market Trends in Indonesia

1. One of the World’s Largest Mobile Video Audiences
   Indonesia has massive mobile-first video consumption driven by affordable smartphones and data access.

2. High Demand for Bahasa Indonesia and Regional Language Content
   Content in Bahasa Indonesia dominates, alongside Javanese and other local languages.

3. Rapid Growth of Creator Economy and Influencer Culture
   Indonesian creators are among the most active in Southeast Asia.

4. Strong Engagement with Entertainment, Music, and Lifestyle Content
   Music, comedy, vlogs, and entertainment content perform exceptionally well.

5. Heavy Reliance on Social Media for Video Discovery
   Platforms like YouTube, TikTok, Instagram, and WhatsApp drive content distribution.

Top 5 Applications of YouTube & Video Data in Indonesia

1. Digital Advertising & Audience Targeting
   Enables large-scale, language-based, and behavior-based targeting.

2. Influencer Marketing & Brand Campaigns
   Supports discovery of high-performing creators across diverse niches.

3. Media & Entertainment Industry Insights
   Helps broadcasters, studios, and OTT platforms understand demand patterns.

4. Consumer Behavior & Cultural Research
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5. Education, Government & Public Awareness Campaigns
   Supports large-scale video-based communication initiatives across sectors.

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To obtain Techsalerator’s YouTube & Video Data for Indonesia, contact us at info@techsalerator.com with your specific data requirements. Custom datasets, historical records, and real-time analytics are available, with delivery within 24 hours and flexible access agreements upon request.

Included Data Fields

• Video Title & Description
• Channel Name & ID
• Upload Date & Time
• Video Category & Tags
• View Count
• Like, Comment, and Share Counts
• Subscriber Count
• Watch Time Metrics
• Audience Demographics
• Geographic View Distribution
• Engagement Rate
• Content Language
• Timestamp of Data Capture

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📩 Email: info@techsalerator.com

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

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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 .

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F051d845a31b5a7cfddb786e6c05a579d%2Ftiktoknonlite2.png?generation=1700450788125487&alt=media" alt="">

As per wikipedia https://en.wikipedia.org/wiki/TikTok

TikTok, whose mainland Chinese counterpart is Douyin (Chinese: 抖音; pinyin: Dǒuyīn), is a short-form video hosting service owned by ByteDance. It hosts user-submitted videos, which can range in duration from 3 seconds to 10 minutes. Since their launches, TikTok and Douyin have gained global popularity. In October 2020, TikTok surpassed 2 billion mobile downloads worldwide. Morning Consult named TikTok the third-fastest growing infotech brand of 2020, after Zoom and Peacock. Cloudflare ranked TikTok the most popular website of 2021, surpassing Google. TikTok's popularity has resulted in the platform having an increasing cultural impact worldwide.

These reviews were extracted from its [Google Store page NONLITE/ FULL]VERSION(https://play.google.com/store/apps/details?id=com.ss.android.ugc.trill).

Usage

This dataset should paint a good picture on what is the public's perception of the app over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of the app had the most positive feedback, the worst.
3. Use topic modeling to identify the pain points of the application.

(AND MANY MORE!)

Important Notes

1. I created a previous Tiktok dataset but didn't know that what I extracted was TIKTOK LITE
2. I am extracting more non-english reviews now to make the data more well rounded

Images

Images generated using Bing Image Generator