Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

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.

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

Dataset Features

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

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

Popular Use Cases

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

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

This dataset consists of 734 entries representing social media activity and performance from a local SME (Micro, Small, and Medium Enterprise) across TikTok, Instagram, and Twitter platforms. It captures key metrics related to audience interaction and content strategy effectiveness, and is valuable for evaluating and optimizing digital marketing efforts for small businesses.

Area : Target location or customer region where the UMKM's content is directed. Category : The business content category (e.g., product promotion, education, seasonal campaign). Day : The day of the week the content was published. Month : The month the post went live. Platform : The social media platform used by the UMKM (TikTok, Instagram, or Twitter). Post Type : The format of the content posted: image, video, carousel, or text. Timestamp : The exact date and time when the content was posted. User : The username or business account that posted the content. Week : Week number within the year for time-based analysis. Year : The year the content was posted. Comments : Total number of comments received on the post. Engagement Rate : A calculated metric showing how engaging the content is (based on likes, comments, shares vs. reach/impressions). Hour : Hour of the day the post was published. Impressions : Number of times the content appeared on users' feeds. Likes : Number of likes the post received. Reach : Number of unique users who saw the content. Shares : Number of times users shared the content.

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This machine-generated dataset simulates social media engagement data across various metrics, including likes, shares, comments, impressions, sentiment scores, toxicity, and engagement growth. It is designed for analysis and visualization of trends, buzz frequency, public sentiment, and user behavior on digital platforms.

The dataset can be used to:

Identify spikes or drops in engagement

Analyze changes in sentiment over time

Build dashboards for digital trend tracking

Test algorithms for sentiment analysis or trend prediction

Social media datasets provide real-time insight into public opinion, trending topics, user behavior, sentiment, and global events as reflected on platforms like Twitter (X), Facebook, and Instagram. These datasets are crucial for marketing analysts, newsrooms, political strategists, crisis response teams, and brand managers to monitor discourse and take data-driven action. Extracted from live user-generated content, […]

Department of Labor Social Media information (description updated 2/17/2023)

Overview

The Student Social Media & Relationships dataset contains anonymized records of students’ social‐media behaviors and related life outcomes. It spans multiple countries and academic levels, focusing on key dimensions such as usage intensity, platform preferences, and relationship dynamics. Each row represents one student’s survey response, offering a cross‐sectional snapshot suitable for statistical analysis and machine‐learning applications.

Scope & Coverage

• Population: Students aged 16–25 enrolled in high school, undergraduate, or graduate programs.
• Geography: Multi‐country coverage (e.g., Bangladesh, India, USA, UK, Canada, Australia, Germany, Brazil, Japan, South Korea).
• Timeframe: Data collected via a one‐time online survey administered in Q1 2025.
• Volume: Configurable sample sizes (e.g., 100, 500, 1,000 records) based on research needs.

Data Collection & Methodology

1. Survey Design: Questions adapted from validated scales on social‐media addiction (e.g., Bergen Social Media Addiction Scale) and relationship conflict indices.
2. Recruitment: Participants recruited through university mailing lists and social‐media platforms, ensuring diversity in academic level and country.

3. Data Quality Controls:

   • Validation: Mandatory fields and range checks (e.g., usage hours between 0–24).
   • De‐duplication: Removal of duplicate entries via unique Student_ID checks.
   • Anonymization: No personally identifiable information collected.

Key Variables

Potential Analyses

• Correlation Studies: Examine associations between daily usage hours and mental‐health score or sleep hours.
• Predictive Modeling: Build classifiers to predict relationship conflicts based on usage patterns and platform type.
• Clustering: Identify user segments (e.g., “high‐usage high‐stress” vs. “moderate‐usage balanced”) across countries.

Limitations

• Self‐Report Bias: All measures are self‐reported and may be subject to social‐desirability effects.
• Cross‐Sectional Design: One‐time survey prevents causal inference.
• Sampling Variability: Recruitment via online channels may underrepresent students with limited internet access.

The hereby presented data are extracted from Meta, Tiktok and Twitter.

The data from my thesis. This data was collected using the Lifeguide Software and exported onto SPSS following data collection. The data was collected from young people aged 11-18 years old to explore the impact of different types of social media use.

AT A GLANCE Demographic, health, and mental health data from students across 48 U.S. states, born 1971–2003. Includes validated clinical screening instruments (PHQ-9 for depression, GAD-7 for anxiety) alongside detailed demographic and health variables. Key Highlights: - Population: Students from 48 U.S. states (birth years 1971–2003) - Instruments: PHQ-9 (depression), GAD-7 (anxiety) - Variables: Mental health symptoms, diagnoses, therapy/medication use, medical conditions, student status, demographics - Strengths: Validated clinical tools, broad geographic coverage PROJECT DESCRIPTION The dataset details symptom frequency over the preceding two weeks, depression and anxiety severity scores, and experiences of feeling overwhelmed, exhausted, and hopeless. Additional variables cover therapy/medication usage, medical conditions, student status (full-time or international), biological sex, and race/ethnicity. Research Applications: - Social media impact on student mental health - Student well-being trajectories and health service utilization - PHQ-9 and GAD-7 population-level analysis - Demographic disparities in mental health among students Subject Terms: social media, mental health, depression, anxiety, PHQ-9, GAD-7, college students, United States

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.

A global survey conducted in the second quarter of 2025 found that the main reason for using social media was to stay in touch with friends and family, cited by **** percent of users. Nearly *** in **** respondents reported using social platforms to fill spare time, while fewer than *** in **** said they used them to follow celebrities and influencers. The most popular social network Facebook dominates the social media landscape. The world's most popular social media platform turned 20 in February 2024, and it continues to lead the way in terms of user numbers. As of February 2025, the social network had over ***** billion global users. YouTube, Instagram, and WhatsApp follow, but none of these well-known brands can surpass Facebook’s audience size. Moreover, as of the final quarter of 2023, there were almost **** billion Meta product users. Ever-evolving social media usage The utilization of social media remains largely gratuitous; however, companies have been encouraging users to become paid subscribers to reduce dependence on advertising profits. Meta Verified entices users by offering a blue verification badge and proactive account protection, among other things. X (formerly Twitter), Snapchat, and Reddit also offer users the chance to upgrade their social media accounts for a monthly free.

Between 2019, and 2024, global social media advertising spending skyrocketed by 140%, surpassing an estimated $230 billion in the latter year.

According to a 2023 survey of adults in the United States, most respondents expressed concern regarding social media companies data collection practices. About ** percent of respondents were very concerned about how social media platforms collect their personal data, while ** percent were somewhat concerned. In contrast, only ** percent of respondents were not very concerned, and a mere **** percent of respondents were not at all concerned about their personal data being collected by these companies.

The number of social media users in Indonesia was forecast to continuously increase between 2024 and 2029 by in total **** million users (+***** percent). After the ninth consecutive increasing year, the social media user base is estimated to reach ****** million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of social media users in countries like Vietnam and Thailand.

These are the key social media statistics that you need to know.

Discover key social media statistics, including user growth, platform trends, engagement rates, demographics, and usage patterns!

Overview

The Social Media Post Dataset contains 60 entries of social media-style posts in 11 languages, covering trending topics like AI integration, remote work, digital transformation, DEI (Diversity, Equity, and Inclusion), sustainability, leadership, health, and global concerns. Designed for NLP research and AI-driven content generation, it provides both raw and enriched post versions to aid text analysis, sentiment classification, and engagement prediction.

Dataset Features

Use Cases

Natural Language Processing (NLP) – Training models for text classification, sentiment analysis, and language detection.
AI-Powered Content Generation – Enhancing post suggestions, engagement prediction, and language adaptability.
Social Media Insights – Understanding how different tones and languages affect engagement.
Multilingual AI Research – Developing models that handle diverse linguistic and cultural content.

Data Source & Collection

The dataset is synthetically generated based on real-world engagement trends from global platforms. It simulates diverse languages, tones, and topics, making it valuable for AI research, content analysis, and multilingual model training.