As of October 2025, **** percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed **** percent of the platform's audience. The online audience of the popular social video platform was further composed of **** percent of female users aged between 25 and 34 years and **** percent of male users in the same age group.

As of August 2021, the majority of content creators on TikTok were aged between 18 and 25 years old, as almost 53 percent of creators on the short-video platform belonged to this age group. TikTok creators under 13 years old represented roughly nine percent of the total. While TikTok does not present registration restrictions for users aged between 13 and 18 years old, users aged under 13 years old in the United States have to use the platform's "Younger Users" version.

TikTok, known as Douyin in its home market, was launched in China in September 2016. It quickly started to gain traction in China and parent company ByteDance launched an international version the...

#Coronavirus on TikTok:

Examining Factors Related to Reception of Content

By [source]

About this dataset

This dataset explores various factors associated with the reception of COVID-19 related content on TikTok. It not only captures overall levels of user engagement such as likes, comments, and views but also explores source credibility including information from healthcare professionals, news sources, patients, and other outlets. It further dives into demographic factors such as gender and age range as well as content type like humor or provision of clinical instruction. Finally, it takes a look at elements such as description of risk factors & symptoms along with modes of transmission established by the posts in question and prevention that was discussed within them. Moreover, there is a discernment component that breaks down user perception - rating the posts for level of misinformation (moderate/high/low). All these measures combined provide insights into how users are engaging with COVID-19 related misinformation on TikTok

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How to use the dataset

This dataset contains user engagement data and measures of source credibility related to COVID-19 misinformation on TikTok. It can be used to examine the factors associated with content reception, such as views, likes, comments, as well as factors relating to credibility, demographics and content type.

Using this dataset: - Explore the columns available in the dataset. There are a number of columns that measure user engagement (views, likes and comments) as well as source credibility (official source, healthcare professional etc.), demographic factors (gender, age group etc.), and content type (humor etc). Get familiar with all these columns so that you know what information is available for analysis.
- Decide what kind of analysis you want to perform. You can use this data for exploratory or explanatory work - depending on your aims or research question. For example if you want to see how source credibility affects user engagement then you would need descriptive statistical techniques such as correlation tests or regression analyses etc., whereas if you just want to gain an overall understanding of patterns in this data then exploratory techniques such as cross tabulations may be more suitable.

Research Ideas

• Developing a predictive model to identify which demographic and source characteristics are correlated with high user engagement for COVID-related posts on TikTok (e.g. views, likes, and comments).
• Investigating the difference in user engagement for posts from healthcare professionals vs non-professional sources to compare how different types of content are received by users on TikTok.
• Analyzing the sentiment of words related to masks and tests in order to gain insights into how content about this topic is perceived by users on TikTok (i.e., positive or negative sentiment)

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: tiktok_data_open.csv | Column name | Description | |:-------------------------------|:------------------------------------------------------------------------| | views | Number of views for the video. (Integer) | | likes | Number of likes for the video. (Integer) | | comments | Number of comments for the video. (Integer) | | official_source | Whether the source of the video is an official source. (Boolean) | | pub_hcp | Whether the source of the video is a healthcare professional. (Boolean) | | pub_news | Whether the source of the video is a news source. (Boolean) | | pub_patient | Whether the source of the video is a patient. (Boolean) | | pub_other | Whether the source of the video is another source. (Boolean) | | female ...

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.

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 150 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).

This dataset captures 535,000+ synthetic records tracking the complete lifecycle of 46 GenZ slang terms from 2020 to 2025. Watch how terms like "bussin," "rizz," "delulu," and "skibidi" emerge on platforms like TikTok, spread across demographics, peak in popularity, and eventually fade into obscurity.

**What Makes This Dataset Unique? ** 1. Lifecycle Modeling: Each term has realistic emergence → growth → peak → decline → legacy phases 2. Multi-Platform Tracking: Usage across TikTok, Twitter, Instagram, Reddit, YouTube, Twitch, and Discord 3. Sentiment Analysis: Positive/negative/neutral sentiment with numeric scores 4. Geographic Spread: 22 regions including US states and international locations 5. Engagement Metrics: Likes, shares, comments, and virality scores 6. Demographic Data: Age group distributions (13-17 through 41+)

Use Cases 1. Time Series Forecasting - Predict when slang terms will peak and decline in popularity 2. Viral Content Classification - Build models to predict which posts will go viral based on slang, platform, and engagement 3. Sentiment Analysis - Classify sentiment of internet language across different contexts and demographics 4. Trend Detection - Identify emerging slang terms before they reach mainstream adoption 5. Geographic Spread Analysis - Study how language diffuses from origin platforms/regions to worldwide usage 6. Lifecycle Survival Analysis - Model how long slang terms "survive" and what factors influence longevity 7. Cross-Platform Language Evolution - Analyze how terms spread from TikTok → Twitter → mainstream culture

This dataset contains survey responses related to consumer behavior in TikTok live streaming commerce, with a particular focus on the beauty and personal care sector in Indonesia. The data was collected in 2025 through an online questionnaire distributed via Google Forms over a three-month period. A total of 390 respondents participated, all of whom had prior experience purchasing beauty and personal care products through TikTok live streams.

The dataset includes demographic information (such as age, gender, and education level) as well as variables measuring consumer perceptions and behaviors. These variables capture persuasive linguistic style of live stream hosts, customer trust, customer engagement, and purchase intention. All constructs were measured using a 5-point Likert scale.

The dataset is suitable for quantitative explanatory research and can be analyzed using advanced statistical techniques such as Partial Least Squares Structural Equation Modeling (PLS-SEM). It provides valuable insights into the influence of host communication styles on consumer trust, engagement, and purchase decisions in live streaming commerce. Researchers and practitioners can use this dataset to explore digital retail dynamics, customer behavior, and strategies for enhancing engagement and sales effectiveness in TikTok commerce.

A line chart that shows % of U.S. teens ages 13 to 17 who say they ever use the following apps or sites

A stacked-bar chart that shows % of U.S. teens ages 13 to 17 who say they visit or use the following apps or sites …

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 number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 150 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).

Discover key social media attention span statistics, including user behavior, content retention, platform trends, and engagement patterns!

The number of Pinterest users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+3.14 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 9.88 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 150 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).

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 study investigates the intricate relationship between social media usage and sleep latency among individuals in Singapore. Conducted by the National University of Singapore, the research encompasses a diverse set of variables to understand how different patterns of social media consumption affect sleep quality. Below is a detailed explanation of each field in the dataset:

Participant ID: Unique identifier assigned to each participant in the study.
Age: The age of the participants.
Gender: The gender of the participants, categorized as Male, Female, or Other.
Chronotype: The natural inclination of a person's sleep and wake times, classified as Morning Lark, Evening Owl, or Neither.
Average Daily Social Media Use Time (minutes): The average amount of time participants spend on social media each day, measured in minutes.
Dominant Social Media Platform: The primary social media platform used by participants (e.g., Instagram, TikTok, Twitter).
Frequency of Social Media Checking (number of times per day): How often participants check their social media accounts daily.
Pre-Sleep Social Media Use Duration (minutes): The duration of social media use immediately before sleep, measured in minutes.
Type of Social Media Content Consumed: The nature of content consumed by participants on social media, such as Social Interaction, News, or Entertainment.
Sleep Latency (minutes): The time it takes for participants to transition from full wakefulness to sleep, measured in minutes.
Total Sleep Time (hours): The total amount of sleep participants get per night, measured in hours.
Sleep Efficiency (%): The ratio of total sleep time to time spent in bed, expressed as a percentage.
Sleep Quality Rating: Self-reported rating of sleep quality on a scale from 1 to 5.
Wake After Sleep Onset (WASO) (minutes): The duration of wakefulness after sleep onset, measured in minutes.
Number of Awakenings (during sleep): The number of times participants wake up during the night.
Melatonin Level (pg/mL): The level of melatonin in the participants' blood, measured in picograms per milliliter.
Cortisol Level (pg/mL): The level of cortisol in the participants' blood, measured in picograms per milliliter.
Day of Week: The day of the week when data was collected.
Blue Light Exposure Before Sleep (minutes): The duration of exposure to blue light from screens before sleep, measured in minutes.
Stress Level Rating: Self-reported rating of stress levels on a scale from 1 to 5.

👨‍💻 Author: Azhar Saleem

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Dataset Description

This dataset represents a comprehensive collection of vehicle listings from PakWheels.com, Pakistan's largest automobile website, as of 2024. It includes detailed information about various aspects of vehicles available for sale across Pakistan, including their prices, models, mileage, engine capacity, and age. This data offers a snapshot of the current automobile market in Pakistan, providing insights into vehicle valuation trends, consumer preferences, and market dynamics.

The dataset is designed for anyone interested in the Pakistani automobile market, whether they are buyers, sellers, car enthusiasts, analysts, or researchers. It provides a foundational dataset for a wide range of analytical and predictive tasks.

Columns Description

1. url: The URL of the vehicle listing on PakWheels.com. This can be used to reference the original listing for more detailed information or verification.
2. title: The title of the listing, which typically includes the make, model, and year of the vehicle.
3. price: The listed price of the vehicle in Pakistani Rupees. This is crucial for market analysis and price prediction models.
4. city: The city in Pakistan where the vehicle is located, useful for regional market analysis.
5. model: The manufacturing year of the vehicle. This is often a determinant of price depreciation.
6. mileage: The total mileage of the vehicle in kilometers, which affects the vehicle’s condition and price.
7. fuel_type: Type of fuel the vehicle uses (e.g., Petrol, Diesel, Hybrid), important for segment analysis based on fuel economy and environmental impact.
8. transmission: The type of transmission (e.g., Automatic, Manual), which influences the driving experience and market value.
9. registered: The city in which the vehicle is registered. This can impact the resale value and legality of the sale.
10. color: The color of the vehicle, which can be a factor in buyer preference.
11. assembly: Whether the vehicle was locally assembled or imported, affecting taxes, duties, and price.
12. engine_capacity: The engine capacity of the vehicle in cubic centimeters (cc), which is a key indicator of performance.
13. post_date: The date the listing was posted, useful for tracking market trends over time.
14. price_category: Categorical representation of the vehicle's price range (e.g., Low, Medium, High).
15. mileage_category: Categorical assessment of the vehicle's mileage (e.g., Low, Medium, High).
16. post_day_of_week: The day of the week the listing was posted, which might influence how quickly a vehicle sells.
17. vehicle_age: The age of the vehicle calculated from the model year to the current year, affecting depreciation and market value.

Usage and Potential Applications in Data Analysis

Market Analysis

• Pricing Trends: Analyze how the prices of vehicles change based on age, mileage, and other factors. This can help in un...

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.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 150 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 Twitter users in countries like Canada and Mexico.

The number of LinkedIn users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.4 million users (+5.23 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 209.26 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 150 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 LinkedIn users in countries like Canada and Mexico.

The number of Pinterest users in the United States was forecast to continuously increase between 2024 and 2028 by in total 5.1 million users (+5.25 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 102.2 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 150 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 Pinterest users in countries like Canada and Mexico.