How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

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 ...

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, 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 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.

During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis.

              Social media: trust and consumption

              Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media.

              What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis.
              Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers.
              Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools.

The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011).

The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

References:

Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. _{https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4}

Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. _{https://doi.org/10.1002/cpp.702}

Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. _{https://doi.org/10.1016/S0005-7967(98)00034-5}

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 global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

Impact of Social Media on Suicide Rates: Produced Results

Overview

This dataset explores the impact of social media usage on suicide rates, presenting an analysis based on social media platform data and WHO suicide rate statistics. It is an insightful resource for researchers, data scientists, and analysts looking to understand the correlation between increased social media activity and suicide rates across different regions and demographics.

Content

The dataset includes the following key sources:

WHO Suicide Rate Data (SDGSUICIDE): Retrieved from WHO data export, which tracks global suicide rates. Social Media Usage Data: Information from major social media platforms, sourced from Kaggle, supplemented with data from:

Facebook: Statista

Twitter: Twitter Investor Relations

Instagram: Facebook Investor Relations

Acknowledgements

We would like to acknowledge:

World Health Organization (WHO): For providing global suicide rate data, accessible under their data policy (WHO Data Policy). Kaggle Dataset Contributors: For social media usage data that played a crucial role in the analysis.

Usage

This dataset is useful for studying the potential social factors contributing to suicide rates, especially the role of social media. Analysts can explore correlations using time-series analysis, regression models, or other statistical tools to derive meaningful insights. Please ensure compliance with the Creative Commons Attribution Non-Commercial Share Alike 4.0 International License (CC BY-NC-SA 4.0).

Data Files

Impact-of-social-media-on-suicide-rates-results-1.1.0.zip (90.9 kB) Contains processed results and supplementary data.

Citations

If you use this dataset in your work, please cite:

Martin Winkler. (2021). Impact of social media on suicide rates: produced results (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4701587 https://zenodo.org/records/4701587

License

This dataset is released under the Creative Commons Attribution Non-Commercial Share Alike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt the material, provided proper attribution is given, it's not used for commercial purposes, and any derivatives are distributed under the same license.

Columns

Year: The year of the recorded data. Sex: Demographic indicator (e.g., male, female). Suicide Rate % Change Since 2010: Percentage change in suicide rates compared to the year 2010. Twitter User Count % Change Since 2010: Percentage change in Twitter user counts compared to the year 2010. Facebook User Count % Change Since 2010: Percentage change in Facebook user counts compared to the year 2010.

Data Bins

The dataset includes categorized data ranges, allowing for analysis of trends within specified intervals. For example, ranges for suicide rates, Twitter user counts, and Facebook user counts are represented in bins for better granularity.

Count Summary

The dataset summarizes counts for various intervals, enabling researchers to identify trends and patterns over time, highlighting periods of significant change or stability in both suicide rates and social media usage.

Use Cases

This dataset can be used for:

Statistical analysis to understand correlations between social media usage and mental health outcomes. Academic research focused on public health, psychology, or sociology. Policy-making discussions aimed at addressing mental health concerns linked to social media.

Cautions

The dataset contains sensitive information regarding suicide rates. Users should handle this data with care and sensitivity, considering ethical implications when presenting findings.

This comprehensive dataset offers a deep dive into the social media engagement metrics of nearly 4,000 posts from four of the world's leading news channels: CNN, BBC, Al Jazeera, and Reuters. Curated to provide a holistic view of global news interaction on social media, the collection stands out for its meticulous assembly and broad spectrum of content.

Dataset Overview: Spanning various global events, topics, and narratives, this dataset is a snapshot of how news is consumed and interacted with on social media platforms. It serves as a rich resource for analyzing trends, engagement patterns, and the dissemination of information across international borders.

Data Science Applications: Ideal for researchers and enthusiasts in the fields of data science, media studies, and social analytics, this dataset opens doors to numerous explorations such as engagement analysis, trend forecasting, content strategy optimization, and the study of information flow in digital spaces. It also holds potential for machine learning projects aiming to predict engagement or classify content based on interaction metrics.

Column Descriptors: Each record in the dataset is detailed with the following columns: - text: The title or main content of the post. - likes: The number of likes each post has garnered. - comments: The number of comments left by viewers. - shares: How many times the post has been shared.

Ethically Mined Data: The collection of this dataset was conducted with the highest ethical standards in mind, ensuring compliance with data privacy laws and platform policies. By anonymizing data where necessary and focusing solely on publicly available information, it respects both individual privacy and intellectual property rights.

Special thanks are extended to the Facebook platform and the respective news channels for their openness and the rich public data they provide. This dataset not only celebrates the vibrant exchange on social media but also underscores the importance of responsible data use and sharing in fostering understanding and innovation.

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.

Over the five years through 2025-26, industry revenue is forecast to expand at a compound annual rate of 20.3% to reach £12.5 billion. Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the past five years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform has over 30 million monthly users in the UK in 2025. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. In 2025, more social media platforms are using AI to boost user engagement. This improves click-through rates and drives higher advertising revenue. Industry revenue is expected to grow by 6.3% in 2025-26. Over the five years through 2030-31, social media platforms' revenue is projected to climb at an estimated 9.2% to reach £19.4 billion. Regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The tightening of regulations will raise industry compliance costs, weighing on profit margin. Older age groups present a new revenue opportunity for social media platforms if they can bridge the gap between passive TV consumption and interactive digital engagement. Augmented Reality (AR) technology will move beyond filters to become standard for immersive product trials, interactive ads, and virtual meetups

Same News Different Sources

How different sources report on the same events

About this dataset

Do you ever feel like you're being inundated with news from all sides, and you can't keep up? Well, you're not alone. In today's age of social media and 24-hour news cycles, it can be difficult to know what's going on in the world. And with so many different news sources to choose from, it can be hard to know who to trust.

That's where this dataset comes in. It captures data related to individuals' Sentiment Analysis toward different news sources. The data was collected by administering a survey to individuals who use different news sources. The survey responses were then analyzed to obtain the sentiment score for each news source.

So if you're feeling overwhelmed by the news, don't worry – this dataset has you covered. With its insights on which news sources are trustworthy and which ones aren't, you'll be able to make informed decisions about what to read – and what to skip

How to use the dataset

The Twitter Sentiment Analysis dataset can be used to analyze the impact of social media on news consumption. This data can be used to study how individuals' sentiments towards different news sources vary based on the source they use. The dataset can also be used to study how different factors, such as the time of day or the topic of the news, affect an individual's sentiments

Research Ideas

• Identify which news sources are most trusted by the public.
• Understand what topics are most important to the public.
• Understand how different news sources report on the same issue

Columns

File: news.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: news_api.csv | Column name | Description | |:--------------|:------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Source | The news source the article is from. (String) |

File: politics.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: sports.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: television.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: trending.csv | Column name | Description ...

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

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 database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

A global survey conducted in the third quarter of 2024 found that the main reason for using social media was to keep in touch with friends and family, with over 50.8 percent of social media users saying this was their main reason for using online networks. Overall, 39 percent of social media users said that filling spare time was their main reason for using social media platforms, whilst 34.5 percent of respondents said they used it to read news stories. Less than one in five users were on social platforms for the reason of following 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 three 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 four 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.

Many have argued that digital technologies such as smartphones and social media are addictive. We develop an economic model of digital addiction and estimate it using a randomized experiment. Temporary incentives to reduce social media use have persistent effects, suggesting social media are habit forming. Allowing people to set limits on their future screen time substantially reduces use, suggesting self-control problems. Additional evidence suggests people are inattentive to habit formation and partially unaware of self-control problems. Looking at these facts through the lens of our model suggests that self-control problems cause 31 percent of social media use.