During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 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 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 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.

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 ...

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.

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.

This comprehensive dataset offers a deep dive into the social media engagement metrics of nearly 4,000 posts from four of the world's leading news channels: CNN, BBC, Al Jazeera, and Reuters. Curated to provide a holistic view of global news interaction on social media, the collection stands out for its meticulous assembly and broad spectrum of content.

Dataset Overview: Spanning various global events, topics, and narratives, this dataset is a snapshot of how news is consumed and interacted with on social media platforms. It serves as a rich resource for analyzing trends, engagement patterns, and the dissemination of information across international borders.

Data Science Applications: Ideal for researchers and enthusiasts in the fields of data science, media studies, and social analytics, this dataset opens doors to numerous explorations such as engagement analysis, trend forecasting, content strategy optimization, and the study of information flow in digital spaces. It also holds potential for machine learning projects aiming to predict engagement or classify content based on interaction metrics.

Column Descriptors: Each record in the dataset is detailed with the following columns: - text: The title or main content of the post. - likes: The number of likes each post has garnered. - comments: The number of comments left by viewers. - shares: How many times the post has been shared.

Ethically Mined Data: The collection of this dataset was conducted with the highest ethical standards in mind, ensuring compliance with data privacy laws and platform policies. By anonymizing data where necessary and focusing solely on publicly available information, it respects both individual privacy and intellectual property rights.

Special thanks are extended to the Facebook platform and the respective news channels for their openness and the rich public data they provide. This dataset not only celebrates the vibrant exchange on social media but also underscores the importance of responsible data use and sharing in fostering understanding and innovation.

This dataset provides a comprehensive and diverse snapshot of social media users and their engagements across various popular platforms such as Instagram, Twitter, Facebook, YouTube, Pinterest, TikTok, and Spotify. With 100 rows of anonymized data, it offers valuable insights into the dynamic world of social media usage. 😀

Each row in the dataset represents a unique user with a designated User ID and Username to ensure anonymity. Alongside user-specific details, the dataset captures essential information, including the platform being used, the post's content, timestamp, and media type (text, image, or video). Additionally, it tracks engagement metrics such as likes, comments, shares/retweets, and user interactions, providing an overview of the user's popularity and social impact. 💬

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The dataset also includes pertinent user attributes, such as account creation date, privacy settings, number of followers, and following. The users' profiles are further enriched with demographic characteristics, including anonymized representations of their age group and gender. 🗨️

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Hashtags, mentions, media URLs, post URLs, and self-reported location contribute to understanding user interests, content themes, and geographic distribution. Moreover, users' bios and language preferences offer insights into their passions, activities, and linguistic communication on the platforms.

Social Networking Market Report is Segmented by Device Type (Smartphone, Tablet and More), Revenue Stream (Advertising, In-App Purchases and More), Platform Type (Traditional Social Networks, Media-Sharing Networks and More), User Demographics (13–24 Years, 25–34 Years and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Discover the latest social media statistics and trends for 2025 and how they impact businesses.

Online social networks enable individuals to present a version of themselves to their immediate social circle and beyond. Those presentations express cultural factors such as an individual's gender, location, political, philosophical, and religious values. However, obtaining such data is often challenging on the aggregate level as it typically involves negotiations with private entities and ownership restrictions. This study presents a dataset of 244,629,979 user accounts from the platform Vkontakte, an online social network collected in June of 2020. Vkontakte is a social media platform similar to Facebook that allows individuals to connect with other users, communicate with them through public and private messages, and create public personas. This dataset can perform cross-national and cross-cultural analyses of online values from a large portion of the world.

Social Media has become a part of our day-to-day routine, keeping users from across the world well-connected through digital platforms. With each passing year, social media is evolving at a rapid speed. With each passing year, the number of social media users is increasing at an immersive speed. Reports also suggest the number of social media users will reach a milestone of 5.85 billion in 2027.

In 2024, 62.6% of the world’s population will access social media, which clearly indicates the dominance of social media platforms in today’s world. In this article, we will examine social media statistics for 2024, uncovering monthly active users, daily time spent by users, most downloaded social media apps, etc.

Snapshot img

Social Media Listening Market Size 2025-2029

The social media listening market size is forecast to increase by USD 4.87 billion at a CAGR of 8.9% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing usage of social media platforms worldwide. With over 4.3 billion users as of 2021, social media has become a powerful tool for businesses to engage with their customers and gain valuable insights into consumer behavior and preferences. A key trend in this market is the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in social media listening solutions, enabling more accurate and efficient data analysis. However, this market is not without challenges. Data privacy and regulatory compliance are becoming increasingly important, with stricter regulations being implemented to protect user data.
Companies must ensure they have strong data security measures in place to comply with these regulations and maintain consumer trust. Additionally, the vast amount of data generated on social media requires sophisticated analytics tools to extract meaningful insights. As such, businesses seeking to capitalize on the opportunities presented by the market must invest in advanced analytics solutions and prioritize data security and privacy. By doing so, they can effectively navigate the challenges and stay ahead of the competition.

What will be the Size of the Social Media Listening Market during the forecast period?

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Social media listening has emerged as a crucial business tool, enabling organizations to gain valuable insights from the vast amount of data generated through social media activity. This data is analyzed using techniques such as topic modeling and sentiment scoring to understand consumer behavior, preferences, and trends. Social media geographics and demographics provide essential context, while social media reach and volume measure the scope and impact of conversations. Social media pulse and sentiment reflect the current sentiment and buzz surrounding specific topics, offering real-time insights into market dynamics and trends.
Social media listening software is a vital component of the global market for social media analytics. Social media influence is assessed through the size and engagement of an audience, providing valuable information for marketing and brand management strategies. The social media landscape and heatmap offer a comprehensive view of the social media ecosystem, helping businesses stay informed and adapt to evolving patterns.

How is this Social Media Listening Industry segmented?

The social media listening industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Software
  Services


End-user

  Retail and e-commerce
  IT and telecom
  BFSI
  Media and entertainment
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The software segment is estimated to witness significant growth during the forecast period. This segment encompasses platforms and tools that offer real-time, automated, and scalable capabilities to monitor and analyze social media conversations across various channels such as Twitter, Facebook, Instagram, LinkedIn, TikTok, and Reddit. Real-time monitoring is a key feature of these solutions, empowering brands to identify mentions, trends, and sentiment as they emerge. By staying abreast of evolving topics, businesses can respond promptly to customer concerns, capitalize on viral events, and maintain a strong online presence. Artificial Intelligence (AI) and Machine Learning (ML) technologies are integral to social media listening software, enabling advanced topic identification, sentiment analysis, and trend recognition.

These technologies enable businesses to gain valuable customer insights, inform product development, and enhance customer experience. Social media listening platforms also offer data visualization and reporting features, allowing businesses to analyze and present their findings in a clear and actionable manner. Additionally, they provide social media dashboards, alerts, and governance tools to ensure compliance with social media policies and ethical standards. In summary, social media listening software plays a pivotal role in the global market for social media analytics, offering real-time insights and advanced capabilities to help businesses navigate the complex social media landscape and engage effectively with their audience.

Get a glance at the market report of share of v

About Dataset

The "Time-Wasters on Social Media" dataset provides a comprehensive insight into user interactions and engagement with various social media platforms. This dataset encompasses a wide range of attributes that facilitate a thorough analysis of how social media affects users' time management and productivity. It serves as an essential resource for researchers, marketers, and social scientists who seek to delve into the intricacies of social media consumption patterns.

Generated through advanced synthetic data techniques using tools like NumPy and pandas, this dataset mimics real-world social media usage scenarios. Despite being artificially created, it accurately reflects genuine usage trends, making it a valuable asset for conducting research and analysis in the realm of social media behavior.

Columns Description

• UserID: Unique identifier assigned to each user.
• Age: The user's age. - Gender: The user's gender (e.g., male, female, non-binary).
• Location: Geographic location of the user.
• Income: The user's income level.
• Debt: Amount of debt the user has.
• Owns Property: Indicates whether the user owns property.
• Profession: The user's occupation or job.
• Demographics: Statistical data about the user (e.g., age, gender, income).
• Platform: The platform the user is using (e.g., website, mobile app).
• Total Time Spent: The total time the user spends on the platform.
• Number of Sessions: The number of times the user has logged into the platform.
• Video ID: Unique identifier for a video.
• Video Category: The category or genre of the video.
• Video Length: Duration of the video.
• Engagement: User interaction with the video (e.g., likes, comments, shares).
• Importance Score: A score indicating how important the video is to the user.
• Time Spent On Video: The amount of time the user spends watching a 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 often the user engages with the platform.
• Productivity Loss: The impact of platform usage on the user's productivity.
• Satisfaction: The user's satisfaction level with the platform or content.
• Watch Reason: The reason why the user is watching a video (e.g., entertainment, education).
• Device Type: The type of device the user is using (e.g., smartphone, tablet, desktop).
• OS: The operating system of the user's device (e.g., iOS, Android, Windows).
• Watch Time: The time of day when the user watches videos.
• Self Control: The user's ability to control their usage of the platform.
• Addiction Level: The user's level of dependency on the platform.
• Current Activity: What the user is doing while watching the video.
• Connection Type: The type of internet connection the user has (e.g., Wi-Fi, cellular).

Social Media Analytics Market size was valued at USD 8.84 Billion in 2024 and is projected to reach USD 46.49 Billion by 2032, growing at a CAGR of 25.43% from 2026 to 2032.Increasing Social Media Users Worldwide: The rapid growth of social media users, estimated to reach 4.9 billion in 2023 and 5.85 billion by 2027, is a primary driver of the Social Media Analytics job. This growth is fueling demand as organizations rely more on analytics to interpret massive amounts of user-generated data, optimize marketing strategies, and improve consumer engagement.Rising Importance of Data Driven Marketing Strategies: The growing relevance of data-driven marketing is driving up demand for social media analytics, as firms prioritize making educated decisions based on customer insights.

Impact of Social Media on Suicide Rates: Produced Results

Overview

This dataset explores the impact of social media usage on suicide rates, presenting an analysis based on social media platform data and WHO suicide rate statistics. It is an insightful resource for researchers, data scientists, and analysts looking to understand the correlation between increased social media activity and suicide rates across different regions and demographics.

Content

The dataset includes the following key sources:

WHO Suicide Rate Data (SDGSUICIDE): Retrieved from WHO data export, which tracks global suicide rates. Social Media Usage Data: Information from major social media platforms, sourced from Kaggle, supplemented with data from:

Facebook: Statista

Twitter: Twitter Investor Relations

Instagram: Facebook Investor Relations

Acknowledgements

We would like to acknowledge:

World Health Organization (WHO): For providing global suicide rate data, accessible under their data policy (WHO Data Policy). Kaggle Dataset Contributors: For social media usage data that played a crucial role in the analysis.

Usage

This dataset is useful for studying the potential social factors contributing to suicide rates, especially the role of social media. Analysts can explore correlations using time-series analysis, regression models, or other statistical tools to derive meaningful insights. Please ensure compliance with the Creative Commons Attribution Non-Commercial Share Alike 4.0 International License (CC BY-NC-SA 4.0).

Data Files

Impact-of-social-media-on-suicide-rates-results-1.1.0.zip (90.9 kB) Contains processed results and supplementary data.

Citations

If you use this dataset in your work, please cite:

Martin Winkler. (2021). Impact of social media on suicide rates: produced results (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4701587 https://zenodo.org/records/4701587

License

This dataset is released under the Creative Commons Attribution Non-Commercial Share Alike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt the material, provided proper attribution is given, it's not used for commercial purposes, and any derivatives are distributed under the same license.

Columns

Year: The year of the recorded data. Sex: Demographic indicator (e.g., male, female). Suicide Rate % Change Since 2010: Percentage change in suicide rates compared to the year 2010. Twitter User Count % Change Since 2010: Percentage change in Twitter user counts compared to the year 2010. Facebook User Count % Change Since 2010: Percentage change in Facebook user counts compared to the year 2010.

Data Bins

The dataset includes categorized data ranges, allowing for analysis of trends within specified intervals. For example, ranges for suicide rates, Twitter user counts, and Facebook user counts are represented in bins for better granularity.

Count Summary

The dataset summarizes counts for various intervals, enabling researchers to identify trends and patterns over time, highlighting periods of significant change or stability in both suicide rates and social media usage.

Use Cases

This dataset can be used for:

Statistical analysis to understand correlations between social media usage and mental health outcomes. Academic research focused on public health, psychology, or sociology. Policy-making discussions aimed at addressing mental health concerns linked to social media.

Cautions

The dataset contains sensitive information regarding suicide rates. Users should handle this data with care and sensitivity, considering ethical implications when presenting findings.

Global Social Media Platforms market size 2021 was recorded $207.48 Billion whereas by the end of 2025 it will reach $307.4 Billion. According to the author, by 2033 Social Media Platforms market size will become $674.774. Social Media Platforms market will be growing at a CAGR of 10.327% during 2025 to 2033.

Social Media Disaster-Related Discussions

Detecting Relevant Content with Trusted Judgments

By CrowdFlower [source]

About this dataset

Welcome to the disaster tweets dataset! This collection of tweets holds a wealth of information about global disasters and their effects on people, governments, and organizations all over the world. With over 10,000 tweets collected and carefully annotated with labels of whether they reported an actual disaster or not, this dataset provides unique insight into what these events look like in terms of social media conversations.

This information is derived from a variety of key terms related to disaster events, such as “ablaze” and “pandemonium” which was used to gather each individual tweet for analysis. The columns for each tweet include detailed metadata about the user who posted it along with variables such as keyword relevance and location. Alongside all these attributes is the core text belonging to each individual tweet- giving you access to all sorts of stories from natural disasters, contagious disease outbreaks or conflicts between nations that can be found in one place!

So whatever you're looking for - whether it's observations about first-hand accounts or conducting research on public sentiment during a major event - this dataset offers you an invaluable source full of timely information that could potentially save lives down the line. So take your journey through this data now and embark upon discovering what devastation looks like through social media!

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For more datasets, click here.

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How to use the dataset

This dataset contains tweets related to disaster events, including the keyword, location, text, tweetid and userid. It provides insights into how people interact with each other on social media during a disaster. Using this dataset you can gain valuable insight into the dynamics of online communication in disasters and provide an important point of reference for future disaster management initiatives.

Research Ideas

• Analyzing the effectiveness of disaster relief and humanitarian aid efforts, by mapping tweets against public data of areas affected by disasters and donations made to help those affected.
• Developing advanced statistical models to predict the magnitude and impact of an oncoming natural disaster using keyword analysis in social media posts related to past disasters.
• Creating text-based classifiers to accurately detect disaster-related tweets in real-time, allowing emergency services providers early warning signs before a potential event occurs

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: socialmedia-disaster-tweets-DFE.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------------------------| | _golden | A boolean value indicating whether the tweet is a golden tweet or not. (Boolean) | | _unit_state | The state of the tweet (e.g. finalized, judged, etc.). (String) | | _trusted_judgments | The number of trusted judgments for the tweet. (Integer) | | _last_judgment_at | The date and time of the last judgment for the tweet. (DateTime) | | choose_one | The label assigned to the tweet (e.g. relevant, not relevant, etc.). (String) | | choose_one_gold | The gold label assigned to the tweet (e.g. relevant, not relevant, etc.). (String) | | keyword | The keyword associated with the tweet. (String) | | location | The location associated with the tweet. (String) | | text | The text content of the tweet. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit CrowdFlower.

Snapshot img

Social Networking Market Size 2025-2029

The social networking market size is forecast to increase by USD 312.3 billion, at a CAGR of 21.6% between 2024 and 2029.

Major Market Trends & Insights

North America dominated the market and accounted for a 41% growth during the forecast period.
By the Type - Advertising segment was valued at USD 80.70 billion in 2023
By the Distribution Channel - Google segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 318.56 billion
Market Future Opportunities: USD 312.30 billion 
CAGR : 21.6%
North America: Largest market in 2023

Market Summary

The market continues to expand its reach and influence across various industries, with businesses recognizing its potential for customer engagement and brand awareness. According to recent studies, there are approximately 4.66 billion active social media users worldwide, representing a 13% increase from 2020. This growth is driven by the increased internet penetration and the popularity of social media platforms for personal and professional use. Social media advertisements have become a significant revenue source, with businesses investing heavily in targeted campaigns to reach their audiences.
However, privacy concerns remain a challenge, with users increasingly cautious about sharing personal information online. Despite this, the market's continuous evolution and the emergence of new trends, such as live streaming and virtual events, ensure its ongoing relevance and importance for businesses.

What will be the Size of the Social Networking Market during the forecast period?

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The market exhibits consistent growth, with current usage accounting for approximately 3.6 billion users worldwide, representing a significant 4.5% increase year-over-year. Looking ahead, industry experts anticipate a continued expansion, with projections indicating a 5.2% annual growth rate. Notably, mobile devices account for over 90% of social media usage, underscoring the importance of optimizing platforms for this medium. Furthermore, businesses increasingly leverage social networking for marketing purposes, with advertising revenue reaching an estimated USD 84.3 billion in 2021. In comparison, the time spent on social media platforms per day has risen by 45 minutes since 2019, highlighting the growing influence of these channels on consumer behavior.
This trend is further accentuated by the integration of advanced features, such as live streaming, video content, and AI-driven recommendations, which enhance user engagement and monetization opportunities.

How is this Social Networking Industry segmented?

The social networking industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Advertising
  In-app purchase
  Paid apps


Distribution Channel

  Google
  Apple
  App Store Distribution


Service

  Communication
  Entertainment
  Socialization
  Marketing
  Customer service


Platform

  Website-based
  Mobile apps
  Hybrid platforms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The advertising segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving landscape of digital communication, the market continues to expand, driven by innovative technologies and user engagement. According to recent data, social networking platforms accounted for approximately 30% of the total time spent online in 2021, reflecting a significant 15% increase from the previous year. Furthermore, industry experts anticipate that social media usage will continue to grow, with an estimated 25% of the global population expected to use social media by 2025. Content moderation systems play a crucial role in ensuring a safe and inclusive online environment. These systems employ advanced techniques, such as natural language processing, conversational AI, and machine learning models, to filter out inappropriate content and maintain platform governance.

User engagement metrics, including time spent on platforms, user-generated content, and social interaction dynamics, are closely monitored to optimize user experience and foster community building strategies. Platform scalability and network security protocols are essential for accommodating the increasing user base and data privacy regulations. Spam filtering techniques and link pred

A growing number of international relations scholars argue that intergovernmental organizations (IGOs) promote peace. Existing approaches emphasize IGO membership as an important causal attribute of individual states, much like economic development and regime type. The authors draw up on social network analysis, arguing that conflicts between states are also shaped by relative positions of social power created by IGO memberships and characterized by significant disparity. Membership partitions states into structurally equivalent clusters and establishes hierarchies of prestige in the international system. These relative positions promote common beliefs and alter the distribution of social power, making certain policy strategies more practical or rational. The authors introduce new IGO relational data and explore the empirical merits of their approach during the period from 1885 to 1992. They demonstrate that conflict is increased by the presence of many other states in structurally equivalent clusters, while large prestige disparities and in-group favoritism decrease it.

Dataset Description

Dataset Overview

This dataset encompasses a meticulously compiled collection of 2000 posts from the official International Cricket Council (ICC) Facebook page. Each entry captures the dynamic interactions of cricket enthusiasts around the globe, presenting a unique opportunity to explore the trends, sentiments, and patterns within the cricket community. The data was ethically mined today, ensuring up-to-date insights into the latest discussions and opinions circulating among ICC followers.

Data Science Applications

The breadth and depth of this dataset offer a fertile ground for a variety of data science projects, including but not limited to: - Sentiment Analysis: Gauge the emotional tone and sentiment of the global cricket community towards events, matches, and players. - Trend Analysis: Identify emerging trends in discussions, such as rising popularity of players or reactions to cricketing events. - Engagement Analysis: Understand what type of content generates the most engagement in terms of likes, shares, and comments. - Network Analysis: Explore the social dynamics and influence patterns within the cricket fan community. - Natural Language Processing (NLP): Employ advanced NLP techniques to extract insights, themes, and patterns from textual content.

Column Descriptors

The dataset is structured into four key columns: - Comments: Number of comments on each post, reflecting the level of interaction and discussion each topic generates. - Likes: Number of likes on each post, indicating the overall popularity and approval from the community. - Shares: Number of shares for each post, showing the extent to which content is circulated beyond the immediate audience. - Text: The textual content of the post, providing rich qualitative data for textual analysis and insight generation.

Ethically Mined Data

This dataset was ethically mined with strict adherence to privacy and data use policies, ensuring that all information was collected in a manner that respects user privacy and platform guidelines. No personal user data was collected or used in the creation of this dataset.

Acknowledgements

We extend our gratitude to the International Cricket Council (ICC) and Facebook for fostering an engaging and vibrant community where fans from around the world can share their passion for cricket. Their platforms not only bring fans closer to the game but also provide valuable data that can be used to enhance our understanding of sports communities and fan engagement.

This dataset serves as an invaluable resource for data scientists, researchers, and cricket enthusiasts alike, offering insights into the global conversation surrounding one of the world's most beloved sports.

Egypt Internet Usage: Social Media Market Share: Desktop: Fark data was reported at 0.000 % in 24 Apr 2025. This stayed constant from the previous number of 0.000 % for 23 Apr 2025. Egypt Internet Usage: Social Media Market Share: Desktop: Fark data is updated daily, averaging 0.000 % from Mar 2024 (Median) to 24 Apr 2025, with 205 observations. The data reached an all-time high of 0.330 % in 18 Dec 2024 and a record low of 0.000 % in 24 Apr 2025. Egypt Internet Usage: Social Media Market Share: Desktop: Fark data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Egypt – Table EG.SC.IU: Internet Usage: Social Media Market Share.