According to a survey conducted in the second quarter of 2022, global internet users aged between 16 and 24 spent *** hours and ** minutes with social media per day. At the same time they also spent *** hour and ** minutes with games consoles. Social media was the most time consuming media pastime for age groups 16 to 44, while internet user older than that spent more of their time watching linear TV.

A line chart that shows % of U.S. adults who say they ever use …

According to data collected during the first quarter of 2020, adults aged 18 to 34 spent more time browsing the web via smartphone than any other age group in the United States. Overall media consumption was highest among adults aged 50 to 64 during that period. Traditional media Traditional media is gradually losing its appeal to younger, more tech-savvy generations. While television consumption is ******* among adults who have not grown up with the internet or other digital channels, young Millennials and Gen Z tend to prefer non-linear forms of news and entertainment. Data on the median age of media users in the U.S. showed that the average age of TV viewers and print magazine readers was higher than that of internet users in 2020, and similar generational trends can be observed in many digitally developed markets globally. Impact of COVID-19 on media usage The onset of the coronavirus (COVID-19) pandemic boosted media consumption across the United States and worldwide in 2020. While the average time spent with traditional media ********* for the first time in over a decade, digital media consumption saw a particularly impressive spike that year due to remote working and schooling setups. In the following years, the gap between traditional and digital media consumption is expected to widen even further.

According to a survey conducted between April 2024 and March 2025, around 50 percent of internet users in the United States between 30 and 49 years said it was important to them to have mobile internet access in any place, compared to 44 percent of those between 18 and 29 years, who stated the same. Overall, nearly three in 10 respondents said they actively did something to protect their data. Additionally, 28 percent of young adults between 18 and 29 years said they were excited about using AI apps, such as ChatGPT, in their daily lives.

# 🌍 Global Social Media Demographics by Age & Gender (2025)

This dataset provides estimated global usage demographics for major social media platforms, differentiated by age and gender for early to mid-2025.

Ideal for market analysis, user behavior insights, and demographic visualization. Data compiled from public sources with AI assistance.

This statistic shows data on the share of consumers who have increased their media consumption worldwide over the past two years as of October 2017, broken down by age group. During the survey, ** percent of Millennials stated that they increased their consumption of content from subscription streaming video services.

A bar chart that shows % of U.S. adults who say they regularly get news on each social media site

Gen Z and Millennials are the biggest social media users of all age groups.

Survey data collected in Canada, 2019. n = 1539. Using, Age, Facebook use and meme understanding to determine differences between demographics in relation to Instagram use

A freeform chart that shows % of U.S. adults who say they regularly get news from each social media site

A line chart that shows % of U.S. adults who say they get news from ...

According to a survey conducted by Rakuten Insight in China in May 2025, cinema was the leading form of media consumed by approximately ** percent of respondents. Cinema was mostly chosen by 25 to 44 year olds, whereas respondents older than 54 years were the least likely to go to the cinema.

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.

According to a survey conducted by Rakuten Insight in Taiwan in May 2025, television was the leading form of media consumed by approximately ** percent of respondents. Social media was also among highly popular media types in Taiwan, favored mostly by those between 16 and 34 years of age.

I’ve compiled a list of the latest social media user statistics showing just how big social media has become and where it’s likely to go in the future.

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

63.9% of the world’s total population is active on social media.

Social Media Buying Dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19517213%2Fe2f9c167d7297b63c931dbc8b02d34ff%2FScreenshot%202024-04-14%20154826.png?generation=1713089935415579&alt=media" alt="">

Data Description

This dataset contains information about individuals and their response to a particular advertisement campaign on social media. The dataset includes the following columns:

• Age: Age of the individual in years.
• EstimatedSalary: Estimated salary of the individual.
• Purchased: Binary variable indicating whether the individual made a purchase (1) or not (0) after seeing the advertisement.

Column Descriptions

i. Age:

Data Type: Integer Description: Represents the age of the individual in years.

ii. EstimatedSalary:

Data Type: Integer Description: Indicates the estimated salary of the individual.

iii. Purchased:

Data Type: Integer (0 or 1) Description: Indicates whether the individual made a purchase (1) or not (0) after seeing the advertisement.

Additional Information

This dataset can be used to analyze the relationship between age, estimated salary, and purchase behavior in response to the advertisement. The dataset appears to be suitable for binary classification tasks, where the goal might be to predict whether an individual will make a purchase based on age and estimated salary. Exploratory data analysis (EDA) techniques can be applied to understand patterns and correlations within the dataset before building predictive models.

This dataset contains survey responses about social media usage patterns and their perceived effects on relationships and mental health. The data was collected from individuals primarily in the 18-25 age group.

Regional use of social media has a significant effect on the male and female social media statistics.