Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Facebook data is a valuable resource for businesses. It delivers essential information about your target audience. Over 2.9 billion people use Facebook worldwide. For example, you may learn about their demographics, hobbies, and online activities. Moreover, Facebook data may assist you in categorizing your target demographic. As a result, you may target your messaging to certain groups of people. This enhances the likelihood of your campaigns connecting with them. Furthermore, you may utilize Facebook data to monitor the effectiveness of your initiatives. This allows you to determine what is working and what is not. You can then make changes to enhance your outcomes. Facebook’s data is continually changing. Stay current with the newest trends and best practices. List To Data will help you get the most out of this important resource. Facebook number database is an invaluable tool for marketers looking to engage with their target demographic. This directory, often known as a contact list or dataset, contains crucial information such as user profiles and engagement metrics. This platform provides a wide reservoir of possible leads. Using this content, you may design tailored campaigns that increase engagement. Moving from general techniques to data-driven initiatives will help you achieve better outcomes. This information enables you to modify your messaging for higher response rates. Furthermore, this resource is updated regularly, guaranteeing that your campaigns always have new connections. Visit List To Data to get premium Facebook number databases and boost your company!

Use our Facebook Profiles dataset to explore public profile details such as names, profile and cover photos, work history, education, and photo galleries. Common use cases include people and company research, influencer discovery, and academic studies of career and education signals on Facebook. Over 31M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Profile URL Profile Name Facebook Profile ID Profile Photo Cover Photo Work History (Title, Company, Company ID, Company URL, Start/End Dates) College Education (Name, ID, URL) High School Education (Name, ID, URL) Photo Galleries And much more

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...

This is a codebook for data on the demographics of people who viewed, shared, and otherwise interacted with web pages (URLs) shared on Facebook, between January 1, 2017 and October 31, 2022. The data has about 68 million URLs, over 3.1 trillion rows, and over 71 trillion cell values. It results from a collaboration between Facebook and Social Science One (at IQSS at Harvard), originally prepared for Social Science One grantees and describes the "full" URLs dataset, including its scope, structure, and fields. This is version 10 of the codebook and data (released 4/13/2023), first described by Gary King and Nathaniel Persily at https://socialscience.one/blog/update-social-science-one.

The number of Facebook users in the United States was forecast to continuously increase between 2024 and 2028 by in total 12.6 million users (+5.04 percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach 262.8 million users and therefore a new peak 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).

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 ...

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 dataset contains information about posts made on Famous Cosmetic Brand's Facebook page from 1st of January to 31th of December of 2014. Each row represents a single post and includes the following attributes:

1. Page total likes: The total number of likes for the page at the time of the post. Example: 139441
2. Type: The type of post (e.g., photo, status, video, link). Example: Photo
3. Category: A categorical variable representing the content category of the post (the specific meaning of the categories is not defined in the provided data). Example: 2
4. Post Month: The month the post was published (likely represented numerically, e.g., 12 for December). Example: 12
5. Post Weekday: The day of the week the post was published (likely represented numerically, e.g., 1 for Monday). Example: 4
6. Post Hour: The hour of the day the post was published (likely in 24-hour format). Example: 3
7. Paid: A binary variable indicating whether the post was a paid advertisement (1 for yes, 0 for no). Example: 0
8. Lifetime Post Total Reach: The total number of unique people who saw the post during its lifetime. Example: 2752
9. Lifetime Post Total Impressions: The total number of times the post was displayed, regardless of whether it was clicked or seen. Example: 5091
10. Lifetime Engaged Users: The number of unique people who engaged with the post (e.g., liked, commented, shared, clicked). Example: 178
11. Lifetime Post Consumers: The number of unique people who clicked anywhere in the post. Example: 109
12. Lifetime Post Consumptions: The total number of clicks anywhere in the post. Example: 159
13. Lifetime Post Impressions by people who have liked your Page: The number of times the post was shown to people who liked the page. Example: 3078
14. Lifetime Post reach by people who like your Page: The number of people who like the page that saw the post. Example: 1640
15. Lifetime People who have liked your Page and engaged with your post: The number of people who liked the page that engaged with the post. Example: 119
16. comment: Number of comments on the post. Example: 4
17. like: Number of likes on the post. Example: 79
18. share: Number of shares of the post. Example: 17
19. Total Interactions: Total number of interactions with the post (likely sum of comments, likes, and shares). Example: 100

Citation: (Moro et al., 2016) S. Moro, P. Rita and B. Vala. Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, Elsevier, In press. Available at: http://dx.doi.org/10.1016/j.jbusres.2016.02.010

https://img.utdstc.com/icon/fe0/ab6/fe0ab67fa0de2b2681602db5708a076f50dd21106e0c2d38b9661875a37e235e:200" alt="Facebook">

Context

Millions of people use Facebook's social networking website every day to keep up with friends, upload photos, and share links. Suffice it to say, Facebook (FB) has had its ups and downs over the past few years, and so has its stock price. Ever since its initial public offering in May 2012, it has remained a high-profile company that consistently captures the public's imagination.

This dataset can be used as Facebook's stock Analysis and finding the relation with other factors.

Content

The dataset contains data from 8 May'12 to 13 Oct'21

Acknowledgements

Yahoo! Finance API.

Context

Facebook is a company that literally every kid is aware of. Its a household name. People from various age groups are there on this social media website. It has helped many in connecting with different people and also has helped some of the investors by earning them a good amount of money. This data set contains the details of the stock of Facebook Inc.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 2012 to 2020(August).

Facebook is the leading social network worldwide, and its accessibility through multiple mobile apps as well as its mobile website. In January 2021, over 98 percent of active user accounts worldwide accessed the social network via any kind of mobile phone.

Facebook in mobile-first markets India is thecountry with the largest Facebook audience by far, with 340 million users on the platform, followed the United States, Indonesia, and Brazil all of which have more than 100 million Facebook users each. With the exception of the United States, all of these are digital markets with mobile-first audiences. In many emerging markets, mobile is often the first online experience, providing online users with their first internet experience through inexpensive smartphones and mobile data contracts. In India and Indonesia, mobile by far surpasses desktop in terms of audiences and time spent.

Mobile Facebook access Due to the social network’s wide reach on mobile, it is unsurprising that Facebook consistently ranks as one of the most-downloaded app publishers worldwide. Some of the apps published by Facebook include the eponymous social networking app (and its low-bandwidth version, Facebook Lite), Facebook Messenger (also available as Messenger Lite), Facebook Pages Manager and Facebook Local. In the Google Play Store, Facebook Messenger, Messenger Lite and Facebook frequently rank among the top downloaded apps every month.

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.

There were 10 363 000 Facebook users in United Arab Emirates in May 2020, which accounted for 96.8% of its entire population. The majority of them were men - 70.8%. People aged 25 to 34 were the largest user group (5 100 000). The highest difference between men and women occurs within people aged 25 to 34, where men lead by 3 600 000.

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

As part of official investigations, government officials sometimes request data about people who use Facebook. The vast majority of these requests relate to criminal cases, such as robberies or kidnappings. In many of these cases, the government is requesting basic subscriber information, such as name and length of service. This report contain information requested by governments to Facebook.

Do you need a data science project in finance domain?

Do you want to work on freshly collected dataset?

Do you need a newly created dataset to work on so that your project can help you get a job?

Do you also need an example on how to perform exploratory data analysis on time series data?

Then look no where. This is because you are at the right place and at the right time.

This dataset was collected using pandas_datareader library. Data source is stooq. This dataset consist of the stock price of Meta/Facebook from January, 2019 till April, 2023. This data can be used to perform time series analysis on any stock price such as open, close, high or low.

I have also created a notebook showing how you can perform exploratory data analysis on time series data.

TASK: 1. Determine moving average. 2. Determine weighted moving average. 3. Determine cumulative moving average. 4. Determine exponential moving average.

Descriptive statistics of social media use of Facebook users and Facebook non-users.

Social mobilization is a process that enlists a large number of people to achieve a goal within a limited time, especially through the use of social media. There is increasing interest in understanding the factors that affect the speed of social mobilization. Based on the Langley Knights competition data set, we analyzed the differences in mobilization speed between users of Facebook and e-mail. We include other factors that may influence mobilization speed (gender, age, timing, and homophily of information source) in our model as control variables in order to isolate the effect of such factors. We show that, in this experiment, although more people used e-mail to recruit, the mobilization speed of Facebook users was faster than that of those that used e-mail. We were also able to measure and show that the mobilization speed for Facebook users was on average seven times faster compared to e-mail before controlling for other factors. After controlling for other factors, we show that Facebook users were 1.84 times more likely to register compared to e-mail users in the next period if they have not done so at any point in time. This finding could provide useful insights for future social mobilization efforts.

This is the dataset of Facebook reviews given on google play sore, I scraped 160 entries this can be very useful for gaining a better rating and product improvement. Facebook is one of the most used social media platforms. With this data, it will be beneficial for other small social media platforms to improve or follow people r requirements and become like facebook.