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!

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

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.

TikTok Video Metadata Dataset – 700+ Entries This dataset contains metadata for over 700 TikTok videos, designed for training and testing machine learning models aimed at predicting video popularity, engagement, and virality. It includes key features such as video duration, text descriptions, hashtags, timestamps, author statistics, sound IDs, and engagement metrics (views and likes).

Key Features:

Video Metadata: id_video, duration_seconds, text_part, hashtags

Author Stats: author_followers, author_likes

Engagement Metrics: views, likes

Sound & Time: id_sound, human_time

Hashtags & Descriptions: Provided as comma-separated strings for easy parsing

Possible Use Cases:

Engagement Prediction: Build regression or classification models to predict views and likes.

Content & Hashtag Analysis: Identify which hashtags and text content correlate with higher engagement.

Author Influence Study: Explore how author popularity impacts video performance.

Time-based Analysis: Investigate posting time patterns.

NLP Applications: Perform text mining on video captions and hashtags.

Data Notes:

Contains real TikTok video metadata from various topics and regions.

Some fields may be empty (e.g., missing text or hashtags).

Suitable for educational projects, hackathons, and initial research in social media analytics.

Suggested Tasks:

Predict likes or views using regression models.

Classify videos into "viral" vs. "non-viral" based on a views threshold.

Cluster videos based on hashtags or content themes.

Analyze the impact of video length and posting time on engagement.

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!

Time-Wasters on Social Media Dataset Overview The "Time-Wasters on Social Media" dataset offers a detailed look into user behavior and engagement with social media platforms. It captures various attributes that can help analyze the impact of social media on users' time and productivity. This dataset is valuable for researchers, marketers, and social scientists aiming to understand the nuances of social media consumption.

This dataset was generated using synthetic data techniques with the help of NumPy and pandas. The data is artificially created to simulate real-world social media usage patterns for research and analysis purposes.

Columns Description UserID: A unique identifier assigned to each user. Age: The age of the user. Gender: The gender of the user. Location: The geographical location of the user. Income: The annual income of the user. Debt: Tells If the is in Debt or Not. Owns Property: Indicates whether the user owns any property (Yes/No). Profession: The profession or job title of the user. Demographics: Additional demographic information about the user (Rural or Urban Life). Platform: The social media platform used by the user (e.g., Facebook, Instagram, TikTok). Total Time Spent: The total time the user has spent on the platform. Number of Sessions: The number of sessions the user has had on the platform. Video ID: A unique identifier for each video watched. Video Category: The category of the video watched (e.g., Entertainment, Gaming, Pranks, Vlog). Video Length: The length of the video watched. Engagement: The engagement level of the user with the video (e.g., Likes, Comments). Importance Score: A score representing the perceived importance of the video to the user. Time Spent On Video: The amount of time the user spent watching the video. Number of Videos Watched: The total number of videos watched by the user. Scroll Rate: The rate at which the user scrolls through content. Frequency: How frequently the user logs into the platform. Productivity Loss: The amount of productivity lost due to time spent on social media. Satisfaction: The satisfaction level of the user with the content consumed. Watch Reason: The reason why the user watched the video (e.g., Entertainment, Information). DeviceType: The type of device used to access the platform (e.g., Mobile, Desktop). OS: The operating system of the device used. Watch Time: The specific time of day when the user watched the video. Self Control: The user's self-assessed level of self-control while using the platform. Addiction Level: The user's self-assessed level of addiction to social media. Current Activity: The activity the user was engaged in before using the platform. ConnectionType: The type of internet connection used by the user (e.g., Wi-Fi, Mobile Data).

Usage This dataset can be utilized to:

Analyze patterns in social media usage. Understand demographic differences in platform engagement. Examine the impact of social media on productivity. Develop strategies to improve user engagement and satisfaction. Study the correlation between social media usage and various demographic factors.

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 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.

📱 About Dataset Overview This Social Media Engagement Dataset contains comprehensive engagement metrics from 5,000 social media posts across six major platforms: Instagram, Twitter, Facebook, LinkedIn, TikTok, and YouTube. The dataset spans over 2 years (2024-2025) and provides valuable insights into content performance, audience engagement patterns, and influencer analytics.

Dataset Contents The dataset includes 20 detailed features covering various aspects of social media engagement:

Post Information Post_ID: Unique identifier for each post Timestamp: Date and time when the post was published Platform: Social media platform (Instagram, Twitter, Facebook, LinkedIn, TikTok, YouTube) Content_Type: Type of content (Photo, Video, Reel, Tweet, Story, etc.) Category: Content category (Technology, Fashion, Food, Travel, Fitness, Education, Entertainment, Business, Lifestyle, Gaming, Health, Sports) Engagement Metrics Likes: Number of likes/reactions received Comments: Number of comments on the post Shares: Number of shares/retweets/reposts Views: Total number of views Saves: Number of bookmarks/saves Engagement_Rate: Calculated engagement rate percentage Account Information Follower_Count: Number of followers of the account Influencer_Tier: Classification (Nano, Micro, Mid-tier, Macro) Is_Verified: Whether the account is verified (True/False) Content Characteristics Hashtag_Count: Number of hashtags used Content_Length: Length in characters (text) or seconds (video) Sentiment: Sentiment analysis (Positive, Neutral, Negative) Has_Media: Whether post contains media (True/False) Temporal Features Hour_of_Day: Hour when the post was published (0-23) Day_of_Week: Day of the week (Monday-Sunday) Use Cases This dataset is perfect for:

📊 Predictive Analytics: Build ML models to predict engagement rates 📈 Data Visualization: Create insightful dashboards and charts 🤖 Machine Learning: Classification, regression, and clustering tasks ⏰ Time Series Analysis: Analyze posting patterns and optimal timing 🎯 Content Strategy: Optimize content strategy based on data insights 🔍 Sentiment Analysis: Study correlation between sentiment and engagement 📱 Platform Comparison: Compare performance across different platforms 💼 Influencer Marketing: Analyze influencer tier performance Technical Details Format: CSV Size: ~651 KB Rows: 5,000 Columns: 20 Time Period: January 2024 - December 2025 Missing Values: None Potential Research Questions What time of day generates the most engagement? Which platform has the highest engagement rates? How does content type affect performance? Does verified status impact engagement? What's the optimal hashtag count? How does sentiment correlate with engagement? Notes Engagement metrics are platform-realistic and proportional All data is synthetically generated for educational and research purposes Suitable for beginners and advanced data scientists

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

Gain a competitive edge with our comprehensive Advertising Dataset, designed for marketers, analysts, and businesses to track ad performance, analyze competitor strategies, and optimize campaign effectiveness.

Dataset Features

Sponsored Posts & Ads: Access structured data on paid advertisements, including post content, engagement metrics, and platform details. Competitor Advertising Insights: Extract data on competitor campaigns, influencer partnerships, and promotional strategies. Audience Engagement Metrics: Analyze likes, shares, comments, and impressions to measure ad effectiveness. Multi-Platform Coverage: Track ads across LinkedIn, Instagram, Facebook, TikTok, Twitter (X), Pinterest, and more. Historical & Real-Time Data: Retrieve historical ad performance data or access continuously updated records for real-time insights.

Customizable Subsets for Specific Needs Our Advertising Dataset is fully customizable, allowing you to filter data based on platform, ad type, engagement levels, or specific brands. Whether you need broad coverage for market research or focused data for ad optimization, we tailor the dataset to your needs.

Popular Use Cases

Targeted Advertising & Audience Segmentation: Refine ad targeting by analyzing competitor content, audience demographics, and engagement trends. Campaign Performance Analysis: Measure ad effectiveness by tracking engagement metrics, reach, and conversion rates. Competitive Intelligence: Monitor competitor ad strategies, influencer collaborations, and promotional trends. Market Research & Trend Forecasting: Identify emerging advertising trends, high-performing content types, and consumer preferences. AI & Predictive Analytics: Use structured ad data to train AI models for automated ad optimization, sentiment analysis, and performance forecasting.

Whether you're optimizing ad campaigns, analyzing competitor strategies, or refining audience targeting, our Advertising Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

GSDSI's Social Media Audience Data product aggregates user profile and behavioral data across six major social media platforms, providing a comprehensive view of digital audiences for targeting, enrichment, and analytics.

PLATFORM COVERAGE: - Facebook: 144M+ user profiles with demographic and interest data - Twitter/X: 200M+ user profiles with engagement and follower metrics - TikTok: 90M+ user profiles with content and audience attributes - Snapchat: 4.6M+ user profiles - Instagram: User profile and engagement data - Patreon: 16M+ creator and subscriber profiles

KEY DATA FIELDS: Each platform dataset includes platform-specific user identifiers, profile metadata, audience attributes, and behavioral signals. Records can be linked to offline identity data (name, email, phone, address) through GSDSI's identity resolution capabilities for omnichannel audience building.

USE CASES: - Audience targeting and lookalike modeling for digital campaigns - Social graph analysis and influencer identification - Consumer enrichment with social platform engagement signals - Cross-platform audience overlap analysis - Brand sentiment and audience intelligence

DELIVERY: Available as flat-file exports (CSV, delimited), via S3/SFTP delivery, or through GSDSI's Octopus DaaS platform for integrated audience building and activation. Custom segments and filtered extracts available on request.

COMPLIANCE: All social media data is sourced through publicly available information and privacy-compliant collection methodologies. GSDSI adheres to platform terms of service and applicable data protection regulations including CCPA and GDPR.

GSDSI has 30+ years of experience in data services, providing trusted, privacy-compliant data solutions to enterprise clients worldwide.

Compliance & Privacy: This data product is collected and distributed in compliance with applicable privacy regulations including the California Consumer Privacy Act (CCPA/CPRA) and GDPR where applicable. Social identity data is derived from publicly available and consented sources. GSDSI does not scrape private social media profiles and maintains documented sourcing methodologies, opt-out mechanisms, and data processing standards. Detailed compliance documentation available upon request. Contact compliance@gsdsi.com for due diligence inquiries.

Comprehensive social media dataset with 150,000 posts across 6 major platforms. Track sentiment evolution, engagement patterns, and viral content dynamics over 6 months.

150,000 posts | 31 features | 6 months 6 platforms | 15 topics | 10 languages 100% complete (0% missing values)

Key Features: • Multi-platform coverage (Twitter, Reddit, Instagram, YouTube, TikTok, Facebook) • Sentiment analysis (positive/negative/neutral scores) • Engagement metrics (53M likes, 8M shares, 4.5M comments, 1.5B views) • Virality indicators (viral coefficient, cross-platform spread) • User metrics (followers, verified status, account age) • Temporal patterns (hour, day of week, weekend effects)

Use Cases: Sentiment analysis & tracking Viral content prediction Platform comparison studies Optimal posting time analysis Influence marketing research NLP & topic modeling

Shofo TikTok General (Small)

  Overview

Shofo TikTok General (Small) is a dataset containing 50,000 TikTok videos with comprehensive metadata, transcripts, comments, and engagement metrics. This is a curated subset of Shofo's larger TikTok index, which contains hundreds of millions of indexed videos.

Size: ~50K videos (~500GB) Modality: Video + Audio + Text (transcripts, comments, captions) Source: TikTok

  Schema

Column Type Description

file_name… See the full description on the dataset page: https://huggingface.co/datasets/Shofo/shofo-tiktok-general-small.

Airline experience review warehouse (Xóm Air): ~215k passenger reviews of airlines, airports, lounges, and seats — multi-criteria satisfaction scores, routes, traveller types.

Dataset of 470 TikTok videos tagged with #digitaleuro, collected via Apify in October 2025. The dataset includes cluster assignments generated by k-means analysis in Tableau, engagement metrics (plays, likes, comments, shares), overlap status, assignment reasoning, up to 10 hashtags per video, and video URLs. Text captions have been excluded for copyright reasons.

About dataset:

Dataset provide a comprehensive view of public engagement and sentiment on TikTok regarding the 2024 U.S. Presidential election. The combination of temporal, quantitative, and qualitative features enables a deep learning model to perform sentiment analysis that informs predictions about which candidate—Donald Trump or Kamala Harris—is more favored by the public, thereby influencing the predicted election outcome.

Dataset Column Descriptions: VideoID: A unique identifier for each TikTok video (e.g., "video_1", "video_2"). It ensures that each video post is distinct and can be referenced individually in the dataset.

Date: The date on which the TikTok video was posted. This temporal information allows for time-based analysis, enabling the tracking of sentiment and engagement trends over the election cycle.

Likes: The number of likes a video has received, reflecting positive user engagement. Higher like counts may indicate stronger public support or agreement with the content of the video.

Shares: The number of times the video has been shared. Shares signify the virality of the content, indicating how widely it is being disseminated across TikTok. Higher share counts often reflect a video’s influence.

Comments: User-generated comments associated with each video. These comments are sentiment-rich and provide qualitative insights into public opinion on the candidates. The text is designed to reflect typical feedback from TikTok users, influenced by either Donald Trump or Kamala Harris.

Candidate: This column indicates the candidate associated with the sentiment expressed in the video. In the updated dataset, 49.8% of the records relate to Donald Trump, 48.3% to Kamala Harris, and a small percentage are labeled as Undecided. This labeling helps distinguish public sentiment toward each candidate and supports comparative analysis.

Dexfluence Indian Creator Index

Verified Indian influencer dataset across Instagram, YouTube, and TikTok with engagement rates, follower tier, niche classification, and authenticity scores.

  Dataset summary

141,000+ verified Indian creators indexed across Instagram, YouTube, and TikTok Top 5,000 by follower count included in this Hugging Face mirror (CC-BY 4.0) Each record includes: handle, name, platform, niche, follower count, engagement rate, country… See the full description on the dataset page: https://huggingface.co/datasets/Shikha180224/dexfluence-indian-creator-index.

📊 Dataset Description: Social Media Content, Engagement & Moderation:

~This dataset contains 40 social media posts collected from multiple platforms (Twitter, Facebook, Instagram, YouTube, TikTok). It provides a detailed view of how different types of content perform, how users engage with them, and how moderation systems respond.

🔑 Key Features:

~**Platform & Content:** Includes post type (Tweet, Story, Video, etc.), unique IDs, and timestamps.

~**User Information:** Follower counts and verification status.

~**Content Metadata:** Text, category, language, country, length, media type, and presence of external links.

~**Engagement Metrics:** Like, share, and comment counts, along with an overall engagement score.

Trust & Safety Signals:

~Misinformation Flag

~Fact-Check Source

~Moderation Action (e.g., Approved, Warning Label, Demonetized, Removed)

NLP & Behavioral Features:

~Sentiment Score (positive/negative tone)

~Toxicity Score (harassment/offensive likelihood)

~Political Leaning (Neutral, Liberal, Conservative, Conspiracy)

~Topic Tags (e.g., climate, vaccine, election, 5G)

~Virality Indicators: Viral score estimating likelihood of content going viral.

📌 Example Use Cases:

~**Fake News & Misinformation Research** – Train ML models to detect misinformation.

~**Content Moderation Systems** – Study how platforms label, remove, or demonetize harmful content.

~**NLP & Sentiment Analysis** – Analyze toxicity, bias, and sentiment across platforms.

~**Trend Analysis** – Compare engagement across topics (climate change, vaccines, elections, 5G).

~**Political Bias Detection** – Explore correlations between political leaning, engagement, and moderation.

📂 Dataset Size:

~40 posts

~25 features

~This dataset is a synthetic but realistic representation of social media activity. It can be useful for machine learning, data analysis, and visualization projects related to misinformation, user engagement, and platform moderation.

Social Media Manager compensation ranges by seniority on Growth.Talent. Sourced from real US listings. Social Media Managers build and manage a brand’s presence across Instagram, TikTok, LinkedIn, and other platforms. They create content, manage communities, and drive engagement.