Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

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.

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_5fb7ffda1ffd756151b1650d4c64363c/view

📊 Social Media vs Productivity — Realistic Behavioral Dataset (30,000 Users)

This dataset explores how daily digital habits — including social media usage, screen time, and notification exposure — relate to individual productivity, stress, and well-being.

🔍 What’s Inside?

The dataset contains 30,000 real-world-style records simulating behavioral patterns of people with various jobs, social habits, and lifestyle choices. The goal is to understand how different digital behaviors correlate with perceived and actual productivity.

🧠 Why This Dataset is Valuable

• ✅ Designed for real-world ML workflows
  Includes missing values, noise, and outliers — ideal for practicing data cleaning and preprocessing.

• 🔗 High correlation between target features
  The perceived_productivity_score and actual_productivity_score are strongly correlated, making this dataset suitable for experiments in feature selection and multicollinearity.

• 🛠️ Feature Engineering playground
  Use this dataset to practice feature scaling, encoding, binning, interaction terms, and more.

• 🧪 Perfect for EDA, regression & classification
  You can model productivity, stress, or satisfaction based on behavior patterns and digital exposure.

🧾 Columns & Feature Info

📌 Notes

• Contains NaN values in critical columns (productivity, sleep, stress) for data imputation tasks
• Includes outliers in media usage, coffee intake, and notification count
• Target columns are strongly correlated for multicollinearity testing
• Multi-purpose: regression, classification, clustering, visualization

💡 Use Cases

• Exploratory Data Analysis (EDA)
• Feature engineering pipelines
• Machine learning model benchmarking
• Statistical hypothesis testing
• Burnout and mental health prediction projects

📥 Bonus

👉 Sample notebook coming soon with data cleaning, visualization, and productivity prediction!

Dataset Info:

MyDigitalFootprint (MDF) is a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modelling in mobile environments. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users by following the in-the-wild data collection approach: the data has been collected in the users' natural environment, without limiting their usual behaviour. Existing public datasets generally consist of a limited set of context data, aimed at optimising specific application domains (human activity recognition is the most common example). On the contrary, the dataset contains a comprehensive set of information describing the user context in the mobile environment.

The complete analysis of the data contained in MDF has been presented in the following publication:

https://www.sciencedirect.com/science/article/abs/pii/S1574119220301383?via%3Dihub

The full anonymised dataset is contained in the folder MDF. Moreover, in order to demonstrate the efficacy of MDF, there are three proof of concept context-aware applications based on different machine learning tasks:

1. A social link prediction algorithm based on physical proximity data,
2. The recognition of daily-life activities based on smartphone-embedded sensors data,
3. A pervasive context-aware recommender system.

For the sake of reproducibility, the data used to evaluate the proof-of-concept applications are contained in the folders link-prediction, context-recognition, and cars, respectively.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

The results might surprise you when looking at internet users that are active on social media in each country.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

56.8% of the world’s total population is active on social media.

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

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_185071db94c8146a103684ce7614a454/view

Facebook and YouTube are still the most used social media platforms today.

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

The average person has 8-9 social media accounts. This has doubled since 2013, when the average person just had 4-5 accounts.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

The dataset was carefully structured to encompass a broad range of demographic variables, such as age, gender, and occupation, which are critical for examining variations in responses to astroturfing among different social media user groups. Participants shared detailed insights into their mobile operating systems, usage habits, and satisfaction levels, offering valuable data for evaluating their digital engagement and technological proficiency—key factors influencing susceptibility to astroturfing. User preferences for various mobile operating system features were systematically measured using a 5-point Likert scale, ranging from ‘Very Poor’ (1) to ‘Excellent’ (5), assessing aspects such as graphical user interfaces and brand trust. This comprehensive dataset facilitated advanced modeling to detect patterns and shifts in public opinion resulting from astroturfing exposure, shedding light on its impact on user perception and behavior. Table 1 presents the demographic distribution and additional dataset details. Feature Description Unique Values Start Time of Form Fill Records the exact date and time a user begins filling out the survey. [Various timestamps] Timestamp of Form Submission Records the exact date and time each participant completed the survey, enabling analysis of responses over time. [Various timestamps] Email Address Used to uniquely identify respondents, ensuring that each participant could only submit the survey once. [Unique per respondent] Age Categorised into groups such as 18-24, 25-34, 35-44, and 45-54 years old, capturing demographic variations in mobile operating system preferences. ['18-24', '25-34', '35-44', '45-54'] Gender It includes diverse gender identities and shows preferences or tendencies associated with different genders. ['Male', 'Female'] Occupation Identifies the respondent's job role, providing insights into how professional life influences mobile operating system choices. ['Self-employed,' 'Student,' 'Professional,' 'Other'] Which mobile operating system do you currently use? Determines the current mobile OS of respondents, segmenting data for OS-specific analysis. ['Arnoid OS,' 'Orange,' 'Other'] How long have you been using your current mobile operating system? Measures the duration of usage to correlate with loyalty and user satisfaction. ['Less than 1 year', '1-2 years', '3-5 years', 'More than 5 years'] How often do you upgrade your mobile device? Indicates the frequency of hardware updates, which can impact software preferences and user experiences. ['Every year, 'Every 2 years', 'Less frequently'] Rate your overall satisfaction with your current mobile operating system Quantifies user contentment, providing a direct measure of OS success among users. Ratings from 1 to 5 What do you like most about your current mobile operating system? Collects qualitative data on user preferences, highlighting strengths of different OSes. [Various text responses] What do you dislike most about your current mobile operating system? Gathers user criticisms, which can guide potential areas for OS improvements. [Various text responses] Graphical User Interface Evaluates user ratings on the visual design and intuitiveness of their OS's interface. Ratings from 1 to 5 Ease of Use and Accessibility Features Assesses how user-friendly and accessible the OS is, which can be critical for adoption rates. Ratings from 1 to 5 Performance & Speed It rates the operating system's efficiency and responsiveness, which are important factors for user satisfaction. Ratings from 1 to 5 Audio Quality Surveys user satisfaction with the sound quality provided by their device's operating system. Ratings from 1 to 5 Camera Quality Assesses satisfaction with the camera performance, a significant feature for many users. Ratings from 1 to 5 Battery Life and Charging Speed Evaluates user satisfaction with their device's battery endurance and recharge rate. Ratings from 1 to 5 Best Value for Money Determines user perception of their OS's cost-effectiveness, which can influence purchasing decisions. Ratings from 1 to 5 Customer Support and Services Rates the quality and responsiveness of customer service provided by the OS brand. Ratings from 1 to 5 Screen Resolution and Quality Gauges user satisfaction with their device's display clarity and quality. Ratings from 1 to 5 Gaming Performance Assesses how well the operating system handles games, an important aspect for many users. Ratings from 1 to 5 Privacy & Security Evaluates user ratings on their OS's ability to protect personal information and prevent security breaches. Ratings from 1 to 5 Storage Options and Expandability Rates how well the OS manages storage and the ease of expanding it. Ratings from 1 to 5 Durability and Resistance to Damage (e.g., Water Resistance, Drop Protection) Surveys how users perceive their device's robustness, including resistance to environmental factors like water and drops. Ratings from 1 to 5 Offers the Best Integration...

In China, the number of college students using mobile phone based messaging and social networking applications like WeChat is increasing rapidly. However, there has been minimal research into the addictive nature of these applications and the psychological characteristics associate with their excessive use. There is also no published scale available for assessing excessive use of WeChat and similar applications. In the current study, we collected data from 1,245 college students in China (715 females) and developed the WeChat Excessive Use Scale (WEUS). We then assessed the relationship between excessive use of WeChat and excessive use of a social networking application-Weibo, problematic use of mobile phones, external locus of control, and social interaction skills. Our 10-item scale featured three factors, namely- "mood modification," "salience" and ''conflict"- critical factors in assessing different forms of addiction. The WEUS was found to be a reliable instrument in assessing excessive use of WeChat as it showed good internal consistency and correlated with other measures of problematic use social networking and mobile phone addiction. Our results showed that excessive users of WeChat are more likely to excessively use Weibo than they are to problematically use mobile phones. Our study also showed that greater excessive use of WeChat is associated with higher external locus of control and greater online social interaction skills. These results reveal that WeChat has unique and strong appeal among college students in China. Further, practitioners should consider dealing with malleable factors like locus of control and real life social skills in treating people with problematic messaging and social networking.

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

In order to develop appropriate tools (e.g. a mobile app) we explored through a participant survey the issues such as the kinds of media coverage that engage and inform voters, whether and how this varies by subgroups such as generation, and the aspects of campaigns that contribute to more positive views of the political process. As part of ExpoNet's objectives to understand news and information exposure in the contemporary environment, we worked to to enhancing the quality of representative democracy through giving better access to citizens to quality information and the tools necessary to evaluate the news they consumed. By providing information about the nature and quality of traditional and new media election coverage over time and its impact on individuals, our research will offer pointers towards how to mobilize informed engagement with campaigns and in elections. The advent of Web 2.0 - the second generation of the World Wide Web, that allows users to interact, collaborate, create and share information online, in virtual communities - has radically changed the media environment, the types of content the public is exposed to as well as the exposure process itself. Individuals are faced with a wider range of options (from social and traditional media), new patterns of exposure (socially mediated and selective), and alternate modes of content production (e.g. user-generated content). In order to understand change (and stability) in opinions and behaviour, it is necessary to measure to what information a person has been exposed. The measures social scientists have traditionally used to capture information exposure usually rely on self-reports of newspaper reading and television news broadcast viewing. These measures do not take into account that individuals browse and share diverse information from social and traditional media on a wide range of platforms. According to the OECD's Global Science Forum 2013 report, social scientists' inability to anticipate the Arab Spring was partly due to a failure to understand 'the new ways in which humans communicate' via social media and the ways they are exposed to information. And social media's mixed record for predicting the results of recent UK elections suggests better tools and a unified methodology are needed to analyze and extract political meaning from this new type of data. We argue that a new set of tools, which models exposure as a network and incorporates both social and traditional media sources, is needed in the social sciences to understand media exposure and its effects in the age of digital information. Whether one is consuming the news online or producing/consuming information on social media, the fundamental dynamic of consuming public affairs news involves formation of ties between users and media content by a variety of means (e.g. browsing, social sharing, search). Online media exposure is then a process of network formation that links sources and consumers of content via their interactions, requiring a network perspective for its proper understanding. We propose a set of scalable network-oriented tools to 1) extract, analyse, and measure media content in the age of "big media data", 2) model the linkages between consumers and producers of media content in complex information networks, and 3) understand co-development of network structures with consumer attitudes/behaviours. In order to develop and validate these tools, we bring together an interdisciplinary and international team of researchers at the interface of social science and computer science. Expertise in network analysis, text mining, statistical methods and media analysis will be combined to test innovative methodologies in three case studies including information dynamics in the 2015 British election and opinion formation on climate change. Developing a set of sophisticated network and text analysis tools is not enough, however. We also seek to build national capacity in computational methods for the analysis of online 'big' data. The survey responses were collected from an online panel run by Dynata. There are 1802 respondents across a range of responses to attitudes and practices of using social media. Demographic variables have also been included. Like other companies where online samples can be purchased, Dynata uses invitations of all types including e-mail invitations, phone alerts, banners and messaging on panel community sites to include people with a diversity of motivations to take part in research. Respondents are paid for completing surveys. In terms of quality control, Dynata checks for duplicate participants by evaluating variables such as email address, matches across several demographic data, and device-related data through use of digital fingerprint technology. Participants are then directed to our survey, programmed in Qualtrics, that is hosted on a server at the University of Exeter in order to comply with data protection and privacy guidelines.