How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 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 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 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.

As of the third quarter of 2024, internet users spent six hours and 38 minutes online daily. This is a slight increase in comparison to the previous quarter. Overall, between the third quarter of 2015 and the third quarter of 2024, the average daily internet use has increased by 19 minutes. Most online countries Internet users between 16 and 64 years old in South Africa spent the longest time online daily, nine hours and 27 minutes, followed by Brazil and the Philippines. These figures include the time spent using the internet on any device. In Japan, internet users spent around three hours and 57 minutes online per day. Users in Denmark also spent relatively less time on the internet, reaching about five hours daily. Most common online activities According to a 2024 survey, more than six in 10 people worldwide used the internet to find information. Furthermore, the usage of communication platforms was also a common reason for going online, followed by online content consumption, such as watching videos, TV shows, or movies.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

Today the average time spent on social media is 2 hours and 24 minutes today for people aged 16 to 64.

The data was generated as part of a BA/Leverhulme Small Research Grant funded research project. The aim was to investigate how older people (65+) in the UK consume online information and what makes them vulnerable during this process. Researchers at the University of York recruited from across the UK male and female older people who are internet users in order to discuss how they use the internet to find information and how this information is used. COVID-19 online search was used to facilitate the discussion and tasks related to fake and real news were completed during the interview process. The interviews took place via zoom. The transcripts comprise in-depth interviews with older people (n=28) while they were searching for COVID-19-related information online.Amid COVID-19, as people spend more time online, their health-related information consumption practices are tested by threats such as fake news, scams and clickbait. Older people can be particularly vulnerable to these threats as they may not always have the IT skills to process information confidently. Hence, using information foraging as the theoretical lens, we investigate how older people consume online information in light of COVID-19-related uncertainty. This includes how they seek, use, share, dismiss and decide to follow online information and what makes them vulnerable during their online navigation. Screencast videography, an innovative approach to capture the lived experiences of internet users in real time, along with in-depth interviews, were employed. Twenty-eight internet users aged 65+ were recruited. Theoretical, practical and policy implications will be drawn from the findings. Participants were recruited via adverts through AgeUK, University's channels and through the researchers' professional networks. The participants were from across the UK, 65+ and internet users. In-depth interviews were conducted via zoom. During the interviews, participants were asked to share their screen and search for COVID-19-related information. Tasks about fake and real news were also shared with the participants in order to investigate how they deal with fake news online. All interviews were audio recorded, screens were captured during the online search activity and the audio recordings were professionally transcribed. Only the anonymised transcripts are included in this data collection along with the interview guide, the template for informed consent and the participant information sheet.

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

Percentage of individuals who used the Internet and percentage of Internet users by number of hours spent using the Internet in a typical week, excluding time spent streaming content and using video gaming services.

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.

The dataset is about telecom industry which tells about the number of customers who churned the service. It consists of 3333 observations having 21 variables. We have to predict which customer is going to churn the service.

Account.Length: how long account has been active.

VMail.Message: Number of voice mail messages send by the customer.

Day.Mins: Time spent on day calls.

Eve.Mins: Time spent on evening calls.

Night.Mins: Time spent on night calls.

Intl. Mins: Time spent on international calls.

Day.Calls: Number of day calls by customers.

Eve.Calls: Number of evening calls by customers.

Intl.Calls: Number of international calls.

Night.Calls: Number of night calls by customer.

Day.Charge: Charges of Day Calls.

Night.Charge: Charges of Night Calls.

Eve.Charge: Charges of evening Calls.

Intl.Charge: Charges of international calls.

VMail.Plan: Voice mail plan taken by the customer or not.

State: State in Area of study.

Phone: Phone number of the customer.

Area.Code: Area Code of customer.

Int.l.Plan: Does customer have international plan or not.

CustServ.Calls: Number of customer service calls by customer.

Churn : Customers who churned the telecom service or who doesn’t(0=“Churner”, 1=“Non-Churner”)

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

The dataset consists of interview transcripts with people who spend a lot of time playing video games. The interviewees include people who play video games competitively for at least 30 hours a week and people who have sought help for compulsive gaming. The interviews are follow-up interviews, and the same individuals were interviewed for the first time a year earlier. For the dataset containing the first round of interviews, see dataset FSD3678 archived at FSD. In the first part of the follow-up interviews, the interviewees were asked whether there had been any changes in their digital gaming habits compared to a year ago. The interviewees were also asked about any changes in their career, family and friends. Next, they were asked to give a day-by-day description of what a normal week of digital gaming was like for them and to describe in as much detail as possible one digital gaming experience from the previous month. Additionally, the interviews included questions about the interviewees' other hobbies and their satisfaction with their current job. In relation to gaming, the interviewees were asked whether they felt that they spent too much time playing digital games. Background information included, among others, the interviewee's gender, information on which interviewee group the interviewee was part of, and the date of the interview. The interview identifier makes it possible to compare data between each interviewee's first interview and follow-up interview. The data were organised into an easy to use HTML version at FSD.

"Mobile phone usage is a global phenomenon, with billions of people worldwide using smartphones for communication, entertainment, and information. Average daily screen time varies across countries, with some nations spending over 5 hours per day on their devices."

Envestnet®| Yodlee®'s Consumer Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: Analytics B2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis.

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

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

Pandemic has influenced all spheres of the humanity. COVID-19 impacted the education vertical in larger manner. Traditional classroom environment plays a very vital role in molding the life of an individual. Bond nurtured in the early ages of the life acts as the great moral support in the latter stages of the journey. As the pandemic has forced us into online education, this data collection aims to analyze the impact of online education. To check out the satisfactory level of the learners, review was conducted.

Gender – Male, Female Home Location – Rural, Urban Level of Education – Post Graduate, School, Under Graduate Age – Years Number of Subjects – 1- 20 Device type used to attend classes – Desktop, Laptop, Mobile Economic status – Middle Class, Poor, Rich Family size – 1 -10 Internet facility in your locality – Number scale (Very Bad to Very Good) Are you involved in any sports? – Yes, No Do elderly people monitor you? – Yes, No Study time – Hours Sleep time – Hours Time spent on social media – Hours Interested in Gaming? – Yes, No Have separate room for studying? – Yes, No Engaged in group studies? – Yes, No Average marks scored before pandemic in traditional classroom – range Your interaction in online mode - Number scale (Very Bad to Very Good) Clearing doubts with faculties in online mode - Number scale (Very Bad to Very Good) Interested in? – Practical, Theory, Both Performance in online - Number scale (Very Bad to Very Good) Your level of satisfaction in Online Education – Average, Bad, Good

radhakrishnan, sujatha (2021), “Online Education System - Review”, Mendeley Data, V1, doi: 10.17632/bzk9zbyvv7.1

Over the last decade, social media has emerged as a valuable tool for reflecting societal dynamics. As people spend more time online, their online traces become a window into their behaviour, choices, curiosity, whereabouts, and opinions. This study leverages a dataset of 486 million messages collected from Twitter (now X) between 2017 and 2020 to explore the potential of social media platforms as a mirror for migration trends, as reflected in the posts of users. We examine correlations between migration-related discussions on Twitter and official United Nations (UN) data, identifying significant alignments that validate the platform's utility in tracking migration dynamics. Through event detection, we highlight the responsiveness of social media to key migration events, demonstrating its value as a real-time sensor of societal reactions. Additionally, we investigate the impact of the COVID-19 pandemic on migration-related discourse, revealing shifts in public sentiment and focus during this global crisis. Our findings underscore the significant role of social media in complementing traditional data sources, providing timely insights into migration trends and public perceptions, and, in the case of COVID-19, the impact of exogenous shocks on online discourse.

Percentage of Canadians' time spent online and using video streaming services and video gaming services, in a typical week.

The data in question was generated using the Faker library and is not authentic real-world data. In recent years, there have been numerous reports suggesting the presence of bot voting practices that have resulted in manipulated outcomes within data science competitions. As a result of this, the idea for creating a simulated dataset arose. Although this is the first time that this dataset has been created, it is open to feedback and constructive criticism in order to improve its overall quality and significance.

NAME: The name of the individual. GENDER: The gender of the individual, either male or female. EMAIL_ID: The email address of the individual. IS_GLOGIN: A boolean indicating whether the individual used Google login to register or not. FOLLOWER_COUNT: The number of followers the individual has. FOLLOWING_COUNT: The number of individuals the individual is following. DATASET_COUNT: The number of datasets the individual has created. CODE_COUNT: The number of notebooks the individual has created. DISCUSSION_COUNT: The number of discussions the individual has participated in. AVG_NB_READ_TIME_MIN: The average time spent reading notebooks in minutes. REGISTRATION_IPV4: The IP address used to register. REGISTRATION_LOCATION: The location from where the individual registered. TOTAL_VOTES_GAVE_NB: The total number of votes the individual has given to notebooks. TOTAL_VOTES_GAVE_DS: The total number of votes the individual has given to datasets. TOTAL_VOTES_GAVE_DC: The total number of votes the individual has given to discussion comments. ISBOT: A boolean indicating whether the individual is a bot or not.

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Learn more

Context

Data Set Information:

The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

Content

The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.

"Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product Related Duration" represent the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. The values of these features are derived from the URL information of the pages visited by the user and updated in real time when a user takes an action, e.g. moving from one page to another. The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by "Google Analytics" for each page in the e-commerce site. The value of "Bounce Rate" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave ("bounce") without triggering any other requests to the analytics server during that session. The value of "Exit Rate" feature for a specific web page is calculated as for all pageviews to the page, the percentage that were the last in the session. The "Page Value" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. The "Special Day" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. The dataset also includes operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.

Acknowledgements

Sakar, C.O., Polat, S.O., Katircioglu, M. et al. Neural Comput & Applic (2018).

Inspiration

Education is the most powerful weapon which you can use to change the world. 😃