As of October 2025, India had the largest Facebook audience worldwide, with over 403 million users. To put this figure into perspective, if India’s Facebook user base were a country, it would rank as the third most populous nation globally. Besides India, three other markets had more than 100 million Facebook users each: the United States, Indonesia and Brazil. 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.

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook’s advertising platform. Facebook’s advertising platform classifies its users as “lived in country X” if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) “migrants” in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.

We investigated the modulation of Facebook-related stimuli on response inhibition in Problematic Facebook Use (PFU) by recording the event-related potentials during an emotional Go/Nogo Task. Twenty-two Problematic Facebook users and 23 Non-problematic users were recruited based on their score on the Problematic Facebook Use Scale. The Go/Nogo task included Facebook-related, unpleasant, pleasant, and neutral pictures. The amplitudes of the Nogo-N2 and the Nogo-P3 were computed as measures of the detection of response conflict and response inhibition, respectively. Reaction times and accuracy were also measured.

As of October 2025, users aged 25 to 34 years made up Facebook's largest audience in the United States, accounting for **** percent of the social network's user base, with **** percent of those users being women. Overall, *** percent of users aged 35 to 44 years were women, and *** percent were men. How many people use Facebook in the United States? ******** is by far the most used social network in the world and finds a huge share of its audience in ****************** Facebook’s U.S. audience size comes second only to India. In 2023, there were over *** million Facebook users in the U.S. By 2028, it is estimated that around *** million people in the U.S. will be signed up for the platform. How do users in the United States view the platform? Although Facebook is widely used and very popular with U.S. consumers, there are issues of trust with its North American audience. As of November 2021, ** percent of respondents reported that they did not trust Facebook with their personal data. Despite having privacy doubts, a May 2022 survey found that ** percent of adults had a very favorable opinion of Facebook, and one-third held a somewhat positive view of the platform.

This fileset contains a series of screenshots taken from our facebook advertising account. A few days ago we noticed that some negative "SEO" tactics, for lack of a better term, were having a negative impact on the performance of ads and fan engagement on the facebook page that we've been building.

I developed a custom software package, which utilizes nueural networks I've developed, to identify a target demographic, and suggest advertising content for said target demographic.

After a short training period we were able to create advertisemsents on facebook that averaged a cost of 0.01 cents per like. We also had a fan page engagement of nearly 4 times that of major brands like Wal-Mart.

Shortly after we began to obtain success we started noticing problems with our page. Since we have a stalker issue, we determined that the issues with our page were likely related to him.

We assued this because we had a disproportinately high number of spammy, negative, and inapporpriate comments on our posts. Offline harassment of our staff by the stalker also increased significantly during this time.

Curiously, we believe that the incident with the stalker allowed us to ascertain some interesting observations about Facebook's algorithims, which I've outlined below.

We believe, after reseraching this issue, that Facebook's algorithims suffer from the following issues:

1. They are easily gamed. We think that Facebook's algorithims are hypersensitive to negative comments being made on a post, and conversely likely positive ones as well. If a post is hidden, the comments are negative, or if a user interacts with the post negatively in some way, then Facebook's algorithims will "punish" your page.

2. We think that a series of scripted fake bot accounts would easily cause the issues that we've been expriencing.

As you can see from the data provided, over 90% of our likes come from paid facebook advertisement, therefore we do not have a significant number of fake accounts on our page brought in by third party advertising because we didn't do any of that.

Moreover, we did not send any of our fans obtained via mailing lists, or offline contact to our facebook page, those fans participate with us via email and/or through our private Google+ community.

So it is safe to say that our problems have not been caused by purchasing a large amount of fake likes from any third party vendor.

In addition, because our likes were gained very quickly, at a rate of about 2.5k likes a day, we do not believe that we have suffered from changes in the general demographic of our Facebook fan base over time.

Yet almost immediately after we started expericing trolling issues with our page, we also noticed a dip in the number of fans our posts were shown to by Facebook, and the performance of our ads began to go down, even though the content on our page had not changed.

We attributed this to holes in Facebook's algorithims, and potentially to the excessive use of fake bot accounts by Facebook itself.

We cannot prove the latter satement, but there have been similar reports before. Reference - http://www.forbes.com/sites/davidthier/2012/08/01/facebook-investigating-claims-that-80-of-ad-clicks-come-from-bots/

This article from Forbes outlines how one startup company repoted that up to 80% of their Facebook likes were fake bot accounts even though they paid for advertising directly through Facebook.

Our reserach suggests that Facebook's advertising platform functions as follows: - An advertiser pays for likes with Facebook, and the quality of the content on their page is initially assessed by those who are liking the page, but once the page obtains a following, we believe that the quality of the content is assessed by how many people like the posts on the page directly after they are posted.

If a post gets hidden, marked as spammed, skipped over, whatever, then we beleive that Facebook kicks that post out of the newsfeeds. If this happens to a significant number of posts on the page, then we believe that Facebook places the page on an advertising black-list.

Once on this black-list ads will begin to perform poorly, and content will drop out of newsfeeds causing even the most active page to go silent.

We tested this by posting pictures of attractive blond women, which with our demographic would have normally obtained a large number of likes and we struggled to get even 10 likes at over 20k page likes when we would have previosuly obtained almost 100 likes without boosting at only 5k page likes.

Why this probably isn't seen more often: In most cases this probably takes a while to occur as pages become old and fans grow bored, but in our case, because we have a stalker trolling our page with what appears to be hundres of scripted bot accounts, the effect was seen immediately.

Our data suggests that it became a tug of war between our stalker's army of fake bot accounts (making spammy comments, hiding our posts from newsfeeds, etc) and the real fans that actually like our page (who were voting our conent up - i.e. liking it, etc).

If you look at the graph of page likes in the figures provided - you can see that the darker purple are the fans we obtained via facebook advertising, well over 90%. We believe that the light purple (the "organic" fans) is mostly comprised of our stalker's fake drone accounts. We have less than 20 family members and friends liking our page, when we began this experiment we asked them not to interact with our page or the content.

In conclusion: We still have a lot more work to do, but it is highly likely that many Facebook likes are either scripted bots, and/or that Facebook's "weighting" algorithims are very suceptible to gaming via negative "SEO" tactics. Conversely, they are likely sensitive to gaming via positive "SEO" tactics as well.

Of course we cannot say for certain where the Facebook accounts that like a page come from without acess to their internal systems, but the evidence does strongly suggest that Facebook might be plagued with a large quantity of bot accounts, and that their algorithim has to be sensitive to actions from live users, so that the quality of the content can be easily ascertained. Otherwise it would be pretty easy for an advertiser to game Facebook's system by paying for, and getting, a large quantity of likes for content that is not appealing to any significant group of people.

Again we have to reiterate that we have no solid proof of this, but our data strongly suggests that this is the case.

We have reported the issues to Facebook, but interestingly, after we made it clear that we were going to analyze and investigate the issues with our page, we have been suddenly and incessently plagued with a never ending stream of "technical difficulties" related to our advertising account.

If you'd like to collaborate on this project, please feel free to email me at Jamie@ITSmoleculardesign.com.

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.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

User Feedback Dataset from the Top 15 Downloaded Mobile Applications

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling.

List of Included Applications:

• TikTok
• Instagram
• Facebook
• WhatsApp
• Telegram
• Zoom
• Snapchat
• Facebook Messenger
• Capcut
• Spotify
• YouTube
• HBO Max
• Cash App
• Subway Surfers
• Roblox

Data Columns and Descriptions:

• review_id: Unique identifiers for each user feedback/application review.
• content: User-generated feedback/review in text format.
• score: Rating or star given by the user.
• TU_count: Number of likes/thumbs up (TU) received for the review.
• app_id: Unique identifier for each application.
• app_name: Name of the application.
• RC_ver: Version of the app when the review was created (RC).

Terms of Use:

This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:

1.Paper

M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3332644

2.Dataset

Asnawi, M. H., Pravitasari, A. A., Herawan, T., & hendrawati, T. (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Data set]. In The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling (1.0.0, Vol. 11, pp. 130272–130286). Zenodo. https://doi.org/10.5281/zenodo.10204232

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

Japan FB: GA: Non Tax Revenue data was reported at 5,842.888 JPY bn in 2018. This records an increase from the previous number of 5,327.415 JPY bn for 2017. Japan FB: GA: Non Tax Revenue data is updated yearly, averaging 5,880.483 JPY bn from Mar 1998 (Median) to 2018, with 21 observations. The data reached an all-time high of 12,782.393 JPY bn in 2011 and a record low of 3,708.568 JPY bn in 2004. Japan FB: GA: Non Tax Revenue data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.F021: Government Budget: Final: General Account: Revenue.

As of June 2025, 24.2 percent of Facebook users in the United States were aged between 25 and 34 years, making up Facebook’s largest audience in the country. Overall, almost 19 percent of users belonged to the 18 to 24-year age group. Does everyone in the U.S. use Facebook? In 2024, there were approximately 250 million Facebook users in the U.S., a figure which is projected to steadily increase, and reach 262.8 million by 2028. Social media users in the United States have a very high awareness of the social media giant. Expectedly, 94 percent of users had heard of the brand in 2025. Although the vast majority of U.S. social networkers knew of Facebook, the likeability of the platform was not so impressive at 68 percent. Nonetheless, usage, loyalty, and buzz around the brand remained relatively high. Facebook, Meta, and the metaverse A strategic rebranding from Facebook to Meta Platforms in late 2021 boded well for the company in Mark Zuckerberg’s attempt to be strongly linked to the metaverse, and to be considered more than just a social media company. According to a survey conducted in the U.S. in early 2022, Meta Platforms is the brand that Americans most associated with the metaverse.  

This dataset is crawled from three popular on-line social networks (OSNs), namely, Twitter, Facebook and Foursquare. We collected this dataset as follows. We first gathered a set of Singapore-based Twitter users who declared Singapore as location in their user profiles. From the Singapore-based Twitter users, we retrieve a subset of Twitter users who declared their Facebook or Foursquare accounts in their short bio description. In total, we collected 1,998 Twitter-Facebook user identity pairs (known as TW-FB ground truth matching pairs}, and 3,602 Twitter-Foursquare user identity pairs (known as TW-FQ ground truth matching pairs). To simulate a real-world setting, where a user identity in the source OSN may not have its corresponding matching user identity in the target OSN, we expanded the datasets by adding Twitter, Facebook and Foursquare users who are connected to users in the TW-FB ground truth matching pairs and TW-FQ ground truth matching pairs sets. Note that isolated users who do not have links to other users are removed from the data sets. After collecting the datasets, we extract the following user features using the OSNs' APIs. • Username: The username of the account. • Screen name: The natural name of the user account. It is usually formed using the first and last name of the user. • Profile Image: The thumbnail or image provided by the user to visually present herself. • Network: The relationship links between users.

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.

This study examines the effects of a series of significant algorithm changes within Facebook’s News Feed on user engagement with news content on the platform between 2011-2020. By tracking public announcements, industry research, and leaks to the press, we constructed a timeline of algorithm changes and collected data on 1 million news articles from The Guardian over the 10-year period, alongside their associated Facebook engagement metrics (likes, comments, shares, etc.) using the CrowdTangle API. Using time series analysis techniques including cross-correlation, Granger causality, and anomaly detection, we modeled this data to test for the relationship between significant algorithmic ranking updates to Facebook’s News Feed algorithms and user engagement with Guardian articles on the platform. Our results show that strategic interventions to the News Feed algorithm significantly impacted engagement with hard news items, whereas opinion, lifestyle, sports, and arts content were less affected. This study challenges the notion of algorithms as ‘black boxes’ by demonstrating how Facebook’s deliberate adjustments influence user engagement with news content. We conclude by outlining the limitations and challenges for systemic auditing of social media algorithms, advocating for greater data access, and discussing the opportunities afforded by the EU’s Digital Services Act to advance this research agenda.

Users aged ******** years old made up the largest audience on Facebook in the United Kingdom in October 2025, accounting for **** percent of all users. Overall, Facebook users aged 35 to 44 were the platform's second-largest demographic in the UK, followed by those aged 18 to 24. Dip in overall user numbers As of January 2025, there were over 55.9 million Facebook users in the UK, up from ***** million in September 2018, according to napoleoncat.com. However, since a peak of **** million users in May 2022, Facebook's audience has decreased slightly. Facebook’s user number issues have not just been limited to the United Kingdom, with figures released by the company highlighting issues in several key markets. There was a small growth in Europe from the first quarter of 2019 to the first quarter of 2020. The company counted *** million daily active users (DAU) in Europe during the first quarter of 2020. Facebook United Kingdom key source of European revenue Facebook UK Limited generated approximately *** million British pounds in revenue during 2015, a figure that skyrocketed  to *** million British pounds in 2016. According to Facebook UK, this was the results of the company commencing advertiser reseller services in April 2016. In 2019, Facebook UK Limited revenue reached roughly *********** British pounds. In the third quarter of 2024, the company’s total European revenue reached over **** billion U.S. dollars 

Data table referring to 500 Facebook posts related to dental caries, obtained through the CrowdTangle platform. Two independent investigators characterized the posts by their time of availability, author’s profile, motivation, the aim of content, content facticity, and sentiment. To determine differences and associations between dichotomized characteristics, the statistical analysis was performed by Mann-Whitney U and Chi-Square tests, and multiple logistic regression models. P values

Facebook, the world’s most popular social network, has billions of monthly active users globally of which less than three million are in China. The reason why Facebook reaches only around four percent of the Chinese population is because it is blocked in mainland China (excluding Hong Kong, Macau, and Taiwan) along with many other foreign social media giants like YouTube, Instagram, Pinterest and Twitter. Why is Facebook banned in China? The Chinese government has been blocking access to Facebook since July 2009, when a series of anti-government riots appeared in the Xinjiang province. The activists reportedly used Facebook as their main communications channel. Chinese authorities have no control over foreign social networks and see them as a threat to social and political stability, hence a long list of foreign internet sites, Facebook among them, are no longer easily accessible in China. Some people, however, access blocked websites by connecting to a virtual private network (VPN). What is the future of Facebook in China? Facebook’s most recent attempt to make a comeback in July 2018 was immediately prevented by the Chinese authorities. It is unlikely that China will allow it for as long as Facebook refuses to store its data servers within the country’s borders. Besides, keeping foreign social media banned is economically beneficial for such Chinese social networks as WeChat, Sina Weibo and Youku Tudou. WeChat, the most popular Chinese social media platform and messaging app, seems to successfully fill the “Facebook void” for at least a billion monthly active users.

In the second quarter of 2025, Facebook took action on 687 million fake accounts, down from one billion in the previous quarter. A record figure of approximately 2.2 billion fake profiles were removed by the social media platform in the first quarter of 2019. Meta considers fake accounts to be those that are created with malicious intent or created to represent a business, organization, or non-human entity. Facebook and inauthentic activity As Facebook is the most used social media platform worldwide, it is not surprising that the service is a target for inauthentic activity and potentially harmful content. Facebook's parent company, Meta, has regulations in place known as Facebook Community Standards that outline what is and is not permitted on the network. Spam content is an ongoing issue that the platform faces, with 1.4 million pieces of spam content being removed in the third quarter of 2022. Facebook’s ongoing popularity The vast majority of internet users have awareness of Facebook as a brand. Almost all social media users in the United States are aware of Facebook, and three-quarters of U.S. social media users have a Facebook account. Furthermore, despite the idea that Facebook is most popular among older generations, its largest U.S. demographic can be found with users aged 25 to 34 years.

The number of Facebook users in Central & Western Europe was forecast to decrease between 2024 and 2028 by in total 29.8 million users. This overall decrease does not happen continuously, notably not in 2026 and 2027. The Facebook user base is estimated to amount to 192.47 million users in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 Facebook users in countries like Eastern Europe and Russia.

The number of Facebook users in Australia had been increasing steadily throughout the years, with a forecast of approximately 11.5 million Australians having an account on the social media platform by the year 2022. This figure would represent more than 40 percent of the Australian population, making it the most used social media platform in the nation. Facebook - the most used social media platform Australia had one the highest active social media penetration rates globally, with most of the population having account on a social media platform. It is partially due to the country’s high internet penetration rate and heavy influence from the United States with regard to internet and social media trends. Furthermore, social networks were the most used platform of social media with instant messenger platforms such as WhatsApp and Facebook messenger coming in second place. Demographics of Facebook users Younger generations in Australia were much more active Facebook users with those between 18 to 29 years using the platform four times more per week than those over the age of 65. This was not to say that the elderly did not use Facebook but the data showed that those that do use social media platforms were most likely to have a Facebook account in comparison to other generations.