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.

As of October 2025, 6.04 billion individuals worldwide were internet users, which amounted to 73.2 percent of the global population. Of this total, 5.66 billion, or 68.7 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2025. In the Netherlands, Norway, and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide—over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a 10-percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most considerable usage penetration, 98 percent. In comparison, the worldwide average for the age group of 15 to 24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

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 ...

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. 💬

https://media.giphy.com/media/3GSoFVODOkiPBFArlu/giphy.gif" alt="social">

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. 🗨️

https://media.giphy.com/media/2tSodgDfwCjIMCBY8h/giphy.gif" alt="socialcat">

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 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!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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.

this graphs was created in R and Ourdataworld:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1ad74af652d524e84410babe6ac5fe61%2Fgraph1.png?generation=1711651132634613&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F7c2b6427cb38f50eae417d741d09cd8d%2Fgraph2.png?generation=1711651140030127&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ffea08aaf9fe8038659f6a081729f1bb2%2Fgraph3.gif?generation=1711651145884218&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F6cbb7538ed8f73a5bfed936ef7396a6d%2Fgraph4.gif?generation=1711651153848054&alt=media" alt="">

Introduction:

The dawn of the internet era has heralded an unprecedented age of connectivity, transforming the way we live, communicate, and interact on a global scale. As of 2020, approximately 60% of the world's population had access to the internet, marking a significant milestone in the digital revolution. From facilitating seamless communication to enabling cross-border collaborations, the internet has become an indispensable tool in our daily lives. This essay explores the multifaceted impact of the internet across various domains, highlighting its role as a catalyst for global connectivity and innovation.

Communication and Collaboration:

One of the most profound implications of the internet is its ability to bridge geographical distances and facilitate instant communication. Platforms such as email, social media, and messaging apps have revolutionized how we interact with one another, transcending borders and time zones. Whether it's connecting with loved ones halfway across the globe or collaborating with colleagues on a project, the internet has made communication more accessible and efficient than ever before. Video conferencing tools have further enhanced remote collaboration, enabling teams to work seamlessly regardless of their physical location. As a result, businesses have embraced remote work models, unlocking new possibilities for flexibility and productivity.

Financial Inclusion and Remittances:

The internet has democratized access to financial services, empowering individuals to participate in the global economy irrespective of their location. Online banking, mobile payment apps, and digital wallets have revolutionized the way we manage our finances, offering convenience and security. Moreover, the internet has facilitated international money transfers, including remittances, which play a vital role in supporting families and economies worldwide. Platforms like PayPal, TransferWise, and Western Union have streamlined the process of sending and receiving money across borders, reducing transaction costs and increasing efficiency. This newfound accessibility to financial services has contributed to greater financial inclusion and economic empowerment, particularly in underserved communities.

Education and Knowledge Sharing:

The internet has democratized access to education, breaking down traditional barriers to learning and knowledge dissemination. Online courses, tutorials, and educational platforms have made quality education accessible to anyone with an internet connection. Whether it's acquiring new skills, pursuing higher education, or accessing resources for self-improvement, the internet offers a wealth of learning opportunities. Open educational resources (OERs) and Massive Open Online Courses (MOOCs) have revolutionized the way we approach education, fostering a culture of lifelong learning and skill development. Furthermore, online forums and communities provide avenues for knowledge sharing and collaboration, enabling individuals to learn from experts and peers across the globe. This democratization of education holds the promise of narrowing the digital divide and fostering global innovation and prosperity.

Cross-Border Social Connections:

The internet has transcended cultural and linguistic barriers, facilitating cross-border social connections and fostering a sense of global citizenship. Social media platforms have become virtual gathering spaces where people from diverse backgrounds can connect, share experiences, and engage in meaningful dialogue. Whether it's forming friendships with individuals from different countries or participating in online communities centered around shared interests, the internet has enriched our social interactions in unprecedented ways. Moreover, platforms like language exchange forums and cultural exchange programs promote intercultural understanding and empathy, bridging gaps between people of different nationalities and backgrounds. By facilitating cross-border social connections, the internet has the potential to foster a more inclusive and interconnected global comm...

Context

Social media are today a very popular way of exchanging information with other people via the Internet. It's hard not to notice that over the years new ones are created and old ones "die". The database below presents the popularity of various social networking sites since 2009, showing the percentage of their share in the social media market.

Content

The database saved in .csv form contains several columns. The first column contains the date (YYYY-MM) of the measurement period. Each subsequent column contains the percentage of share in the social media market, given as a percentage, rounded to 2 decimal places (if the share is less than 0.5%, the value 0 remains, even though it may constitute a very small percentage of the share). We have almost 180 rows, 15 years of data for monthly periods.

Source

The database comes from the Statcounter and is made available in the operation with CC BY-SA 3.0 license which allows to copy, use and disseminate data also for commercial purposes after providing the source.

Context

Social Media has been taking up everything on the Internet. People getting the latest news, useful resources, life partner and what not. In a world where Social media plays a big role in giving news, we must also know that news which affects our sentiments are going to get spread like a wildfire. Based on the Headline and the title, and according to the date given and the Social media platforms, you have to predict how it has affected the human sentiment scores. You have to predict the column “SentimentTitle” and “SentimentHeadline”.

Content

This is a subset of the dataset of the same name available in the UCI Machine Learning Repository The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine.

Dataset Information

The attributes for each of the dataset are : - IDLink (numeric): Unique identifier of news items - Title (string): Title of the news item according to the official media sources - Headline (string): Headline of the news item according to the official media sources - Source (string): Original news outlet that published the news item - Topic (string): Query topic used to obtain the items in the official media sources - Publish-Date (timestamp): Date and time of the news items' publication - Facebook (numeric): Final value of the news items' popularity according to the social media source Facebook - Google-Plus (numeric): Final value of the news items' popularity according to the social media source Google+ - LinkedIn (numeric): Final value of the news items' popularity according to the social media source LinkedIn - SentimentTitle: Sentiment score of the title, Higher the score, better is the impact or +ve sentiment and vice-versa. (Target Variable 1) - SentimentHeadline: Sentiment score of the text in the news items' headline. Higher the score, better is the impact or +ve sentiment. (Target Variable 2)

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

Social Influence on Shopping

Social Survey Data from 300,000 Millennials and Gen Z Members

By Adam Halper [source]

About this dataset

This dataset offers a comprehensive look into the shopping habits of millennials and Gen Z members, including valuable insights about how their choices are influenced by social media. By exploring the responses given to survey questions related to this topic, we can gain an understanding of how these generations' interests, beliefs and desires shape their decisions when it comes to retail experiences. With 150 million survey responses from our 300,000+ millennial and Gen Z participants, we can uncover powerful insights that could help influencers, businesses and marketers more accurately target this demographic. Our data includes important information such as questions asked during the survey, segment types targeted by those questions and corresponding answers gathered with detailed counts/percentages - making this dataset incredibly useful for anyone wanting an in-depth understanding of what drives the purchasing behavior of today's youth

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The first step in using this dataset is to take a look at each column: Question, Segment Type, Segment Description, Answer, Count & Percentage. The Question column will provide background on what exactly each survey question was asking - allowing you to get an overall view of what kind of topics were being surveyed in relation to millennials' shopping habits & social media influence. You will then be able to follow up with analysis based on the respective Segment Types & Descriptions given (such as income levels), which leads us into analyzing answers from both Count & Percentage columns combined - providing absolute numbers vs relative ones for further analysis (such as percentages).

Afterwards you'll need an advanced data analysis program such as SPSS or R-Studio - depending on your technical ability - though all most basic spreadsheet programs should suffice, excluding Matlab supported ones due its excessive complexity for something simple like this.. After selecting your preferred program inputting our file with all 150 million survey responses may take some time based on your computers processing capabilities but once loaded you'll be ready for endless possibilities! Now it's time get running with pulling out key insights you require utilizing various different tools found within these platforms whether it be linear regression or guided ANOVA testing which ever technique fits best should help lead navigate through uncovering deeper meaning in your ultra specific question!

As a final precaution while diving through waters filled surprises also keep note any adjustments needed potentially due overfitting or multicollinearity otherwise could cause major issues skew end results unfit requiring start whole process anew! Good luck delving deep discovering millennial behavior related digital world!

Research Ideas

• Identifying which type of segment is most responsive to engaging shopping experiences, such as influencer marketing, social media discounts and campaigns, etc.
• Analyzing the answers given to survey questions in order to understand millennial and Gen Z's opinion about social influence on their shopping habits - what do they view positively or negatively?
• Using the survey responses to uncover any interesting trends or correlations between different segments - is there a particular demographic that values or uses certain types of social influence on their shopping habits more than others?

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: WhatsgoodlyData-6.csv | Column name | Description ...

According to Cognitive Market Research, the global Social Media Monitoring Tools market Size was USD 4854.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1941.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1456.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.5% from 2024 to 2031.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1116.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 242.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 97.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Software/Platform category held the highest Social Media Monitoring Tools market revenue share in 2024.

Market Dynamics of Social Media Monitoring Tools Market

Key Drivers for Social Media Monitoring Tools Market

Growing cloud-based solution usage in enterprises to propel market growth

Cloud-based social media monitoring technologies provide scalable, adaptable, and affordable solutions for tracking, evaluating, and managing media material across several channels by utilizing cloud computing infrastructure. With the use of these systems, which track and analyze massive amounts of media information in real-time using sophisticated data processing, storage, and analytics capabilities, businesses may get useful insights into competition intelligence, market trends, and brand perception. Sentiment analysis, trend tracking, and influencer identification features are available in cloud-based media monitoring systems like Meltwater, Talkwalker, and Brandwatch. In light of this, the increasing acceptance of media monitoring tools to facilitate strategic decision-making and improve brand performance in the digital sphere is being driven by the expanding use of cloud-based solutions among organizations, which improve accessibility, cooperation, and scalability.

Increasing social media usage to propel market growth

The Social Media Monitoring Tools Market is expected to grow at an exponential rate due in large part to the rise in social media usage. In order to remain competitive, businesses must keep an eye on and analyze social media activities given the billions of users across platforms such as Facebook, Instagram, Twitter, and LinkedIn. With the use of these technologies, businesses can monitor industry trends, client feedback, and brand mentions instantly. There is a growing need for strong monitoring solutions as customers utilize social media more and more for reviews, recommendations, and conversations. Businesses may improve customer service, manage reputational issues, and improve marketing tactics with the help of this boom in social media interaction, which offers great data. This means that as more people use social media, the market for social media monitoring tools is expanding due to the technologies' increased usage and innovation.

Restraint Factor for the Social Media Monitoring Tools Market

Data security and privacy issues to hinder market growth

The market for social media monitoring tools is growing slowly due to concerns about data security and privacy. Adoption of these tools may be constrained by the complicated compliance requirements imposed by regulations such as the CCPA and GDPR. Concerning breaches and misuse, users become more cautious about the way their data is gathered, stored, and used. Social media monitoring tools need to have strong security measures in place to secure user data due to their gather and examine huge quantities of private and sensitive data. Some firms are discouraged from fully utilizing these tools due to the possibility of data breaches or penalties for noncompliance. In order to establish compliance, assure trust, and promote wider adoption and use of soci...

Context

With roughly 2.89 billion monthly active users as of the second quarter of 2021, Facebook is the biggest social network worldwide. In the third quarter of 2012, the number of active Facebook users surpassed one billion, making it the first social network ever to do so. Active users are those who have logged into Facebook during the past 30 days. During the first quarter of 2021, the company stated that 3.51 billion people were using at least one of the company's core products (Facebook, WhatsApp, Instagram, or Messenger) each month.

Content

This data was collected by Facebook and was released in July 2021.

NEW UPDATE:

Show your skills off in the Social Media Extremism Challenge @ https://www.kaggle.com/competitions/social-media-extremism-detection-challenge! Try your luck at tackling this challenging classification problem! After the competition is completed, we will be adding 200+ hand-labelled entries to this dataset so stay tuned!

We would like to thank Assistant Professor Leilani H. Gilpin (UC Santa Cruz) and the AIEA Lab for their guidance and support in the development of this dataset. —*Aditya Suresh, Anthony Lu, Vishnu Iyer*

About this data: Social media has seen an increasing rise in the quantity and intensity of extremist content throughout various different services. With cases such as the various different white supremacist movements across the world, recruitment for terrorist organizations through affiliated accounts, and a general sense of hate emerging through the modern era of polarization, it becomes increasingly vital to be able to recognize these patterns and adequately combat the harms of extremism digitally on a global scale.

Citations: Our dataset would not have been possible without the aid of an already preexisting dataset found on Kaggle, Version 1 of "Hate Speech Detection curated Dataset🤬" by Alban Nyantudre in 2023. The link can be found here: https://www.kaggle.com/datasets/waalbannyantudre/hate-speech-detection-curated-dataset/data. Accessed in 2025, it was truly essential to our work. With over 400,000 messages of real, cleaned posts, we would not have been able to source and label our data points without this crucial resource.

Classification: Our team hand labelled nearly 3,000 pieces of data from our sourced database of posts, filtering every on of them into a blanket tag of "EXTREMIST" and "NON_EXTREMIST." As many messages digitally utilize context in order to spread harmful rhetoric, we followed a general rule of classifying terms as extremist so long as they "provoked harm to a person or a group of people, whether it be through advocacy for violence, discrimination, or other hurtful sentiments, based off of a characteristic of the group."

Value of the data: This dataset can be utilized to create extremist sentiment analysis systems and machine learning algorithms, as it reflects on current linguistics, as stated by the source material for the data points themselves. In addition, it can be used as a benchmark for comparing with other extremism datasets and other extremist sentiment analysis systems.

Potential Errors: Although we feel very confident in our own labeling ability, a possibility of potentially wrong data points does exist due to the fact that these data points lack quantifiable identifiers and as such human errors are possible within the data. We do not believe this to occur often, but in full transparency is an issue that we endeavor to resolve in subsequent updates.

This dataset simulates a set of key economic, social, and environmental indicators for 20 countries over the period from 2010 to 2019. The dataset is designed to reflect typical World Bank metrics, which are used for analysis, policy-making, and forecasting. It includes the following variables:

Country Name: The country for which the data is recorded. Year: The specific year of the observation (from 2010 to 2019). GDP (USD): Gross Domestic Product in billions of US dollars, indicating the economic output of a country. Population: The total population of the country in millions. Life Expectancy (in years): The average life expectancy at birth for the country’s population. Unemployment Rate (%): The percentage of the total labor force that is unemployed but actively seeking employment. CO2 Emissions (metric tons per capita): The per capita carbon dioxide emissions, reflecting environmental impact. Access to Electricity (% of population): The percentage of the population with access to electricity, representing infrastructure development. Country:

Description: Name of the country for which the data is recorded. Data Type: String Example: "United States", "India", "Brazil" Year:

Description: The year in which the data is observed. Data Type: Integer Range: 2010 to 2019 Example: 2012, 2015 GDP (USD):

Description: The Gross Domestic Product of the country in billions of US dollars, indicating the economic output. Data Type: Float (billions of USD) Example: 14200.56 (represents 14,200.56 billion USD) Population:

Description: The total population of the country in millions. Data Type: Float (millions of people) Example: 331.42 (represents 331.42 million people) Life Expectancy (in years):

Description: The average number of years a newborn is expected to live, assuming that current mortality rates remain constant throughout their life. Data Type: Float (years) Range: Typically between 50 and 85 years Example: 78.5 years Unemployment Rate (%):

Description: The percentage of the total labor force that is unemployed but actively seeking employment. Data Type: Float (percentage) Range: Typically between 2% and 25% Example: 6.25% CO2 Emissions (metric tons per capita):

Description: The amount of carbon dioxide emissions per person in the country, measured in metric tons. Data Type: Float (metric tons) Range: Typically between 0.5 and 20 metric tons per capita Example: 4.32 metric tons per capita Access to Electricity (%):

Description: The percentage of the population with access to electricity. Data Type: Float (percentage) Range: Typically between 50% and 100% Example: 95.7%

In this day and age, people face a lot of stress due to the fast pace of life. Due to this, people in today's digital age, suffer from a plethora of ailments. It is universally accepted that a greater awareness of ailments and their corresponding symptoms leads to an increased lifespan and better quality of life. Early detection and screening can help doctors nip diseases in their natal stages. However, not everyone is aware of them, which makes it a global issue. The study of the degree of disease awareness amongst people belonging to different nations and continents is a matter of great interest. One method that is suitable for this purpose is using clinical data. But, this data is not readily available. However, today a plethora of platforms are available to people to share their thoughts and experiences. People post about many of the important events in their lives on social media. Their posts offer a microscopic view into their lives and thought processes. Based on this intuition, twitter data pertaining to various chronic and acute diseases has been collected. Tweets for 30 deadly ailments have been collected over a period of 3 months amounting to a total of 19 million. A feature extraction approach is proposed which is used to identify the disease awareness levels across different nations. Deriving the global awareness landscape for ailments can help to identify regions which are well aware and also those that need to get aware. Clustering has been used for this purpose.

This dataset provides a detailed analysis of emoji usage across various social media platforms. It captures how different emojis are used in different contexts, reflecting emotions, trends, and user demographics.

With emojis becoming a universal digital language, this dataset helps researchers, marketers, and data analysts explore how people express emotions online and identify patterns in social media communication.

📌 Key Features: 😊 Emoji Details: Emoji 🎭: The specific emoji used in a post, comment, or message. Context 💬: The meaning or emotion associated with the emoji (e.g., Happy, Love, Funny, Sad). Platform 🌐: The social media platform where the emoji was used (e.g., Facebook, Instagram, Twitter). 👤 User Demographics: User Age 🎂: Age of the user who posted the emoji (ranges from 13 to 65 years). User Gender 🚻: Gender of the user (Male/Female). 📈 Additional Insights: Emoji Popularity 🔥: Frequency of each emoji’s usage across platforms. Trends Over Time 📅: How emoji usage changes based on trends or events. Regional Usage Patterns 🌍: How different cultures and regions use emojis differently. 📊 Use Cases & Applications: 🔹 Understanding emoji trends across social media 🔹 Analyzing emotional expression through digital communication 🔹 Exploring demographic differences in emoji usage 🔹 Identifying platform-specific emoji preferences 🔹 Enhancing sentiment analysis models with emoji insights

⚠️ Important Note: This dataset is synthetically generated for educational and analytical purposes. It does not contain real user data but is designed to reflect real-world trends in emoji usage.

This dataset explores the connections between how people use technology in their daily lives and their overall well-being. It brings together information on screen time, social media and entertainment habits, sleep patterns, lifestyle choices, and different indicators of mental health such as mood, stress, anxiety, and depression.

The data offers a broad look at how digital behaviors—like time spent on phones, laptops, or social media—intersect with key aspects of wellness including sleep quality, physical activity, and mindfulness practices. Alongside these, lifestyle elements such as healthy eating and caffeine consumption are also included, allowing for a more holistic view of modern life.

With 5,000 participants, the dataset provides a rich opportunity to study patterns and relationships between technology use and wellness outcomes. Researchers and data scientists can use it to ask questions like: Does heavy screen time impact sleep and stress levels? Is social media linked to mood or anxiety? Can healthy habits offset the negative effects of technology use?

This dataset is well-suited for projects in mental health research, wellness analytics, and predictive modeling, and it encourages exploration into how technology both supports and challenges our well-being in today’s digital world.

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

Social data in digital form—including user-generated content, expressed or implicit relations between people, and behavioral traces—are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding “what the world thinks” about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them.“For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.” –Ursula Franklin1

There is a large body of research on utilizing online activity as a survey of political opinion to predict real world election outcomes. There is considerably less work, however, on using this data to understand topic-specific interest and opinion amongst the general population and specific demographic subgroups, as currently measured by relatively expensive surveys. Here we investigate this possibility by studying a full census of all Twitter activity during the 2012 election cycle along with the comprehensive search history of a large panel of Internet users during the same period, highlighting the challenges in interpreting online and social media activity as the results of a survey. As noted in existing work, the online population is a non-representative sample of the offline world (e.g., the U.S. voting population). We extend this work to show how demographic skew and user participation is non-stationary and difficult to predict over time. In addition, the nature of user contributions varies substantially around important events. Furthermore, we note subtle problems in mapping what people are sharing or consuming online to specific sentiment or opinion measures around a particular topic. We provide a framework, built around considering this data as an imperfect continuous panel survey, for addressing these issues so that meaningful insight about public interest and opinion can be reliably extracted from online and social media data.