Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

How much time do people spend on social media? As of 2019 and 2020, the average daily social media usage of internet users worldwide amounted to *** minutes per day, up from *** minutes in the previous year. Currently, the country with the most time spent on social media per day is the Philippines, with online users spending an average of ***** hours and ** minute on social media each day. In comparison, the daily time spent with social media in the U.S. was just *** hours and ***** minutes. Global social media usageCurrently, the global social network penetration rate is nearly ** percent. Western Europe had a ** percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with *** and ***** 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 flipside, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

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 ...

Systematic exposure to social media causes social comparisons, especially among women who compare their image to others; they are particularly vulnerable to mood decrease, self-objectification, body concerns, and lower perception of themselves. This study first investigates the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model. In the study, 974 women age 18–49 who were Instagram users voluntarily participated, completing a questionnaire. The results suggest associations between the analyzed psychological data and social comparison types. Then, artificial neural networks models were implemented to predict the type of such comparison (positive, negative, equal) based on the aforementioned psychological traits. The models were able to properly predict between 71% and 82% of cases. As human behavior analysis has been a subject of study in various fields of science, this paper contributes towards understanding the role of artificial intelligence methods for analyzing behavioral data in psychology.

During a 2025 survey, ** percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just ** 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 ** percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than ** 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.

How many people use social media? Social media usage is one of the most popular online activities. In 2025, over *** billion people were estimated to be using social media worldwide, a number projected to increase to over *** billion in 2030. 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 ** percent. This figure is anticipated to grow as less 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. The mobile-first market of 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 *** minutes per day on social media and messaging apps, an increase of ** 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 *** billion registered accounts and currently boasts approximately *** 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.

How does Truth Social compare to other social media platforms? There are around 2 million active Truth Social users.

This dataset contains 600 synthetic entries simulating social media activity across three major platforms: Twitter, Reddit, and Instagram. The data was generated to analyze trends, sentiments, and user engagement patterns based on hashtags and posts. It can be useful for researchers, data analysts, and machine learning enthusiasts interested in studying social media behavior.

Dataset Structure The dataset includes the following columns:

Date: The date of the post, ranging across a simulated timeline. Platform: The social media platform where the post was made (Twitter, Reddit, or Instagram). Hashtag: The main hashtag associated with the post, such as #AI, #MachineLearning, or #Python. Post Content: The text of the post, crafted to simulate common social media interactions. Sentiment: The sentiment of the post, classified as Positive, Neutral, or Negative. Likes: The number of likes the post received. Shares: The number of shares or retweets the post received. Potential Use Cases Sentiment analysis: Train machine learning models to detect sentiment in text. Hashtag popularity analysis: Determine which hashtags are most commonly used or generate the most engagement. Engagement trends: Explore correlations between post sentiment and engagement metrics (likes/shares). Platform comparison: Compare user behavior across different social media platforms. Acknowledgments This dataset is fully synthetic and was generated using Python. It does not contain any real user data and is intended for educational and research purposes.

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

1) Data Introduction • The Social Media Usage Dataset(Applications) features patterns and activity indicators that 1,000 users use seven major social media platforms, including Facebook, Instagram, and Twitter.

2) Data Utilization (1) Social Media Usage Dataset(Applications) has characteristics that: • This dataset provides different social media activity data for each user, including daily usage time, number of posts, number of likes received, and number of new followers. (2) Social Media Usage Dataset(Applications) can be used to: • Analysis of User Participation by Platform: You can analyze participation and popular trends by platform by comparing usage time and activity for each social media. • Establish marketing strategy: Based on user activity data, it can be used for targeted marketing, content production, and user retention strategies.

There is interest in using social media content to supplement or even substitute for survey data. O’Connor et al. (2010) report reasonably high correlations between the sentiment of tweets containing the word “jobs” and survey-based measures of consumer confidence in 2008-2009. Other researchers report a similar relationship through 2011 but after that time it is no longer observed, suggesting such tweets may not be as promising an alternative to survey responses as originally hoped. But, it’s possible that with the right analytic techniques, the sentiment of “jobs” tweets might still be an acceptable alternative. We explore this possibility by attempting to strengthen the original relationship and then extending the most successful approaches to more recent years. We classify “jobs” tweets into categories whose content is related to employment and categories whose content is not, to see if sentiment of the former correlates more highly with a survey-based measure of consumer sentiment. We use five sentiment-scoring tools, calculate daily sentiment three different ways, and use a measure of association less sensitive to outliers than correlation. None of these approaches improved the size of the relationship in the original or more recent data. We discuss the possibility that weighting and better understanding why users tweet might help recover the original relationship between the sentiment of tweets and survey responses. However, despite the earlier promise of tweets as an alternative to survey responses, we find no evidence that the original relationship was more than a chance occurrence.

This dataset provides detailed rankings and key metrics for 100+ social media platforms and sites in 2025. It includes information such as user base, popularity trends, and global reach. Ideal for analyzing social media growth, user engagement, and market trends. Whether you're a data scientist, marketer, or researcher, this dataset offers valuable insights into the evolving digital landscape.

In the last two decades, social media usage has surged, reaching nearly five billion users worldwide in 2022. Unfortunately, there is a rise in mental health issues during that same time. Through a two-phase data analysis, this project studies the patterns of mental health influenced by social media. Analyzing data from 479 individuals across various platforms, the study employs K-means clustering to categorize mental health states into three groups, each indicating varying levels of professional/intervention needs. In the subsequent supervised learning phase, predictive models, including the Naive Bayes model with an under-sampled dataset and the Decision Tree model with an oversampled dataset, were developed to determine mental health categories, achieving an accuracy of 60.42%. These models, developed with comprehensive predictors, offer valuable insights for future research and the need for interventions addressing mental health challenges linked to social media use. Table 1 displays the variables, their descriptions, and value types. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2Fd9e0fb90d862e58aba958a14b3b8dcea%2FScreen%20Shot%202023-12-14%20at%2012.27.20%20PM.png?generation=1702578478575969&alt=media" alt="">

Phase I : Unsupervised Learning Techniques K-means Clustering Model

Using the elbow method pictured below in plot 1, we could visualize the optimal number of clusters (K), and then perform the K-means clustering with the optimal K. Several values for K were considered, and models were created for K = 2, 3, 4, 5, 6, 7, and 8, which were then compared. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2Fa77706842d108c7fbee363c1192b763a%2FScreen%20Shot%202023-12-14%20at%2012.08.01%20PM.png?generation=1702577407983039&alt=media" alt="">

In table 4 we can see the comparison of the bss/tss ratios. K = 3 is the last model with a significant jump and therefore is the optimal model. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F9a44382d9c08a616bd0248f150b85526%2FScreen%20Shot%202023-12-14%20at%2012.08.20%20PM.png?generation=1702577436944201&alt=media" alt="">

In Table 5, we can observe the cluster centers for each variable within each cluster in the K-means clustering model with k = 3.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2Fdf92bc28b65f67d88efa3b8a96295dcc%2FScreen%20Shot%202023-12-14%20at%2012.09.13%20PM.png?generation=1702577557552624&alt=media" alt="">

Based on the above cluster centers, we could interpret the cluster groups as shown in the table 6 below: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F1d0624052cfc9ce50e7bc5b404d916d0%2FScreen%20Shot%202023-12-14%20at%2012.08.34%20PM.png?generation=1702577449886328&alt=media" alt="">

Phase II: Supervised Learning Techniques

Prediction Models

Data Input https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F51672c4d16a801532a3ac8017cf72958%2FScreen%20Shot%202023-12-14%20at%2012.16.16%20PM.png?generation=1702577897888133&alt=media" alt=""> Above in Image A, we can see a sneak peek of the dataset with the new variable 'MHScore,' indicating mental health state cluster groups.

The outcome variable (MHScore) is categorical and multi-class (3 Levels: 1,2,3). Therefore, the implemented models include Naïve Bayes (NB), Support Vector Machines (SVM), SVM with parameter changes, Decision Trees, and Pruned Decision Trees.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13828311%2F06827fe209b78ffbddee69b272a8cdfc%2FScreen%20Shot%202023-12-14%20at%2012.20.41%20PM.png?generation=1702578062241650&alt=media" alt="">

Table 11 summarizes the results of the best model from each predictive machine learning technique for accuracy, balanced accuracy, sensitivity, specificity, and precision for each class. Each model was developed using the same predictors from the dataset, including age, gender, relationship status, occupation, organization of employment, social media usage, the number of social media platforms used, the hours spent on social media, and the frequency of social media use. The higher accuracy observed in both the under-sampled and oversampled datasets indicates the importance of class equality.

This dataset provides detailed, platform-specific metrics for social media influencers, including follower growth, engagement rates, post frequency, and brand partnerships. It enables marketers and analysts to identify trends, benchmark influencer performance, and optimize campaign strategies across various content categories and regions.

This dataset provides granular, hourly influencer engagement metrics across major social media platforms, including detailed audience demographics and campaign associations. Brands and agencies can leverage this data for AI-powered analysis of peak activity times, audience response profiles, and campaign effectiveness, enabling data-driven marketing strategies and influencer selection.

This dataset provides detailed records of user interactions with social media posts, including likes, comments, and shares, across multiple platforms and campaigns. It enables in-depth analysis of engagement trends, audience demographics, and campaign effectiveness for social media analytics and marketing optimization.

These datasets consist of qualitative data collected through semi-structured in-depth interviews as well as a focus group from three different companies with seven industry experts.The data collected was to address the use of social media to enhance organisational learning and also to address the gap that exists in terms of the integration of organisational learning (OL) and social media and also address the lack of guidelines for organisations that would like to implement the use of social media to facilitate OL. The data were triangulated by comparing the results from the three companies.

Although researchers have devoted great effort to explore the antecedents of altruistic behaviours (a type of organisational citizenship behaviours), the important role of technical factors (social media) remains unclear. Drawing on social comparison and organisational support theory, this study explored how social media affordances influence employees’ altruistic behaviours from both positive and negative perspectives. In this study, 302 employees from organisations in China were surveyed. We found that social media affordances could facilitate employees’ perceived organisational support and social comparison of ability. Perceived organisational support positively mediated the relationship between social media affordances and altruistic behaviours. Although the evidence did not support the notion that social comparison of ability could directly dampen altruistic behaviours, a post-hoc analysis found that it could dampen the positive impact of perceived organisational support on developing altruistic behaviours. This study expanded previous research focusing on only positive or negative effects of social media use in the workplace by investigating the dual effects and the interaction effect in between. Here, we discuss the results and provide practical guidance for managers and organisations.

Aggregation of social comparisons.

It starts with a familiar flick of the thumb. A notification pops up during breakfast, a reel plays in the background while brushing teeth, and before we know it, half the morning has disappeared into a scroll. This isn’t just anecdotal, it’s a digital behavior woven into the daily routine...