Overall, 40 percent of U.S. online users aged 18 to 22 years reported feeling addicted to social media. During the April 2019 survey, five percent of respondents from that age group stated that they felt the statement "I am addicted to social media" described them completely.

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.

According to a 2023 survey conducted in the United Kingdom, 48 percent of social media users aged between 16 and 18 years reported feeling addicted to social media. Feelings of addiction were higher amongst female teens than male teens, with 57 percent of girls in the UK saying they thought they were addicted to online platforms. In general, social media can be difficult for teens to navigate, partly due to pressure to create the perfect image or partake in online challenges.

Overall, 34 percent of female online users in the United States reported feeling addicted to social media. During the April 2019 survey, 11 percent of female respondents stated that they felt the statement "I am addicted to social media" described them completely.

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

According to a survey on the lifestyles of Arab youth and their dependence on social media in April of 2023, ** percent of young people in the Middle East and North Africa (MENA) region had difficulty disconnecting from social media. Only ***** percent of survey participants said they do not find it difficult to disconnect from social media, while ** percent were neutral.

According to an online survey conducted in 2022 in the United States, 45 percent of teen girls said they felt 'addicted' to TikTok, or ended up using the platform for a longer period of time than they originally wanted. Of these respondents, 32 percent reported using the app on a daily basis. Almost half of respondents stated that they felt addicted to YouTube, or used it for longer than intended, with 37 percent of these respondents saying they used the platform daily.

YouTube gets an average of 14.3 billion total worldwide visits every month.

Users spend an average of 19.6 hours per month on TikTok alone. This works out to be approximately 39 minutes per day.

The average Twitter user spends 5.1 hours per month on the platform.

According to a March 2024 survey conducted in the United States, 32 percent of adults reported feeling that social media had neither a positive nor negative effect on their own mental health. Only seven percent of social media users said that online platforms had a very positive effect on their mental health, while 12 percent of users said it had a very negative impact. Furthermore, 22 percent of respondents said social media had a somewhat negative effect on their mental health. Is social media addictive? A 2023 survey of individuals between 11 and 59 years old in the United States found that over 73 percent of TikTok users agreed that the platform was addictive. Furthermore, nearly 27 percent of those surveyed reported experiencing negative psychological effects related to TikTok use. Users belonging to Generation Z were the most likely to say that TikTok is addictive, yet millennials felt the negative effects of using the app more so than Gen Z. In the U.S., it is also not uncommon for social media users to take breaks from using online platforms, and as of March 2024, over a third of adults in the country had done so. Following mental health-related content Although online users may be aware of the negative and addictive aspects of social media, it is also a useful tool for finding supportive content. In a global survey conducted in 2023, 32 percent of social media users followed therapists and mental health professionals on social media. Overall, 24 percent of respondents said that they followed people on social media if they had the same condition as they did. Between January 2020 and March 2023, British actress and model Cara Delevingne was the celebrity mental health activist with the highest growth in searches tying her name to the topic.

This sophisticated study delves into the evolving patterns of social media addiction in the USA over a decade. Conducted by a leading university, the research meticulously examines several key variables:

Year: The specific year during which the data was collected, ranging from 2013 to 2023, providing a comprehensive timeline of social media usage trends.
Usage Time (mins): The average daily duration of social media usage in minutes, offering insights into the increasing or decreasing engagement levels over the years.
Variable Reward Schedule: This field captures the extent of variable reward mechanisms employed by social media platforms, indicating how unpredictable rewards can affect user addiction levels.
Digital Dopamine Index: A measure of the digital dopamine effect, quantifying the psychological impact of social media interactions on users, which is a critical factor in understanding addiction behaviors.

These data support a study of the relationships between social media addiction, core beliefs, and mindfulness in Latinx college students.

According to a survey on the lifestyles of Arab youth and their dependence on social media in April 2023, ** percent of young adults in the Gulf Cooperation Council (GCC) countries strongly or somewhat agree that they find it difficult to disconnect from social media. This is higher than the regional average of the Middle East and North Africa (MENA) of ** percent who found it difficult to disconnect from social media.

Snapchat now boasts over 319 million daily active users. That means it’s one of the most engaging platforms. Snapchat currently has a total user base of 800 million.

Background: Facebook addiction is said to occur when an individual spends an excessive amount of time on Facebook, disrupting one’s daily activities and social life. The present study aimed to find out the level of Facebook addiction in the Nepalese context and briefly discuss the crimes associated with its unintended use. Methods: A descriptive cross-sectional study was conducted in the Department of Forensic Medicine of Lumbini Medical College. The study instrument was the Bergen Facebook Addiction Scale typed into a Google Form and sent randomly to Facebook contacts of the authors. The responses were downloaded in a Microsoft Excel spreadsheet and analyzed using Statistical Package for Social Sciences version 16. Results: The study consisted of 103 Nepalese participants, of which 54 (52.42%) were males and 49 females (47.58%). There were 11 participants (10.68%) who had more than one Facebook account. When different approaches were applied it was observed that 8.73% (n=9) to 39.80% (...

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.

Technology’s pull often feels impossible to resist. More than half of Americans report an almost daily reliance on smartphones, the internet, and social media, affecting everything from sleep to relationships. From health care settings where digital detox programs help patients reclaim balance, to corporate wellness initiatives that curb online burnout,...

Snapchat has a reach into 75% of the millenial and Gen Z audience.

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.