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).

171 million names (100 million unique) This torrent contains: The URL of every searchable Facebook user s profile The name of every searchable Facebook user, both unique and by count (perfect for post-processing, datamining, etc) Processed lists, including first names with count, last names with count, potential usernames with count, etc The programs I used to generate everything So, there you have it: lots of awesome data from Facebook. Now, I just have to find one more problem with Facebook so I can write "Revenge of the Facebook Snatchers" and complete the trilogy. Any suggestions? >:-) Limitations So far, I have only indexed the searchable users, not their friends. Getting their friends will be significantly more data to process, and I don t have those capabilities right now. I d like to tackle that in the future, though, so if anybody has any bandwidth they d like to donate, all I need is an ssh account and Nmap installed. An additional limitation is that these are on

The number of Facebook users in Indonesia was forecast to continuously decrease between 2024 and 2028 by in total 20 million users (-11.04 percent). According to this forecast, in 2028, the Facebook user base will have decreased for the fifth consecutive year to 161.16 million users. 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 Thailand and Vietnam.

This statistical dataset contains estimates on the number of active online Facebook users living outside of their country of origin within the European Union. The dataset includes information on Facebook users' age, gender, country of residence, and country of previous residence. The data is divided in the number of Monthly Active Users and Daily Active Users. The data was collected through standard CSV format via an advertising API platform by using an R Studio code, and the data collection was conducted twice a month from January to November 2021.

The dataset was originally published in DiVA and moved to SND in 2024.

Dataset used in Article "Identification and characterization of Facebook user profiles considering interaction aspects"

This table includes platform data for Facebook participants in the Deactivation experiment. Each row of the dataset corresponds to data from a participant’s Facebook user account. Each column contains a value, or set of values, that aggregates log data for this specific participant over a certain period of time.

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.

The LiLaH-HAG dataset (HAG is short for hate-age-gender) consists of metadata on Facebook comments to Facebook posts of mainstream media in Great Britain, Flanders, Slovenia and Croatia. The metadata available in the dataset are the hatefulness of the comment (0 is acceptable, 1 is hateful), age of the commenter (0-25, 26-30, 36-65, 65-), gender of the commenter (M or F), and the language in which the comment was written (EN, NL, SL, HR).

The hatefulness of the comment was assigned by multiple well-trained annotators by reading comments in the order of appearance in a discussion thread, while the age and gender variables were estimated from the Facebook profile of a specific user by a single annotator.

The number of Facebook users in India was forecast to continuously increase between 2024 and 2028 by in total **** million users (+*** percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach ****** 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 *** 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 Nepal and Pakistan.

About the Dataset: This dataset features Facebook comments related to Netflix, either posted on Netflix's own updates or about the platform. It is particularly suited for applications such as sentiment analysis or training large language models (LLMs).

While the data was originally collected via an API in JSON or column-based relational formats, it's important to note that LLMs typically perform better when processing text presented as coherent, sentence-based narratives. Therefore, transforming this raw data into structured sentences is a crucial preprocessing step for maximizing its utility in further analysis and modeling.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This database contains regional estimates of Facebook users based on data from the Facebook Marketing API. It includes information on the number of individuals aged 18 and older who have accessed Facebook in the past month, with data separated by region. These estimates are intended for trend identification and triangulation purposes and are not designed to match official census data or other government sources.

This data can be used as a proxy of internet access.

It should be noted that there could be duplicates across different regions, and the data is anonymized by Meta.

The metrics in this dataset measure users who engaged with posts with links to civic news URLs and the volume of their engagement. The dataset contains URL-level metrics from Facebook activity data for adult U.S. monthly active users, aggregated over the study period. Includes content views, audience size, content attributes, user attributes.

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.

Facebook is becoming an essential tool for more than just family and friends. Discover how Cheltenham Township (USA), a diverse community just outside of Philadelphia, deals with major issues such as the Bill Cosby trial, everyday traffic issues, sewer I/I problems and lost cats and dogs. And yes, theft.

Communities work when they're connected and exchanging information. What and who are the essential forces making a positive impact, and when and how do conversational threads get directed or misdirected?

Use Any Facebook Public Group

You can leverage the examples here for any public Facebook group. For an example of the source code used to collect this data, and a quick start docker image, take a look at the following project: facebook-group-scrape.

Data Sources

There are 4 csv files in the dataset, with data from the following 5 public Facebook groups:

• Unofficial Cheltenham Township
• Elkins Park Happenings!
• Free Speech Zone
• Cheltenham Lateral Solutions
• Cheltenham Township Residents

post.csv

These are the main posts you will see on the page. It might help to take a quick look at the page. Commas in the msg field have been replaced with {COMMA}, and apostrophes have been replaced with {APOST}.

• gid Group id (5 different Facebook groups)
• pid Main Post id
• id Id of the user posting
• name User's name
• timeStamp
• shares
• url
• msg Text of the message posted.
• likes Number of likes

comment.csv

These are comments to the main post. Note, Facebook postings have comments, and comments on comments.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Comment Id.
• timeStamp
• id Id of user commenting
• name Name of user commenting
• rid Id of user responding to first comment
• msg Message

like.csv

These are likes and responses. The two keys in this file (pid,cid) will join to post and comment respectively.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Matches Comments id.
• response Response such as LIKE, ANGRY etc.
• id The id of user responding
• name Name of the user responding

member.csv

These are all the members in the group. Some members never, or rarely, post or comment. You may find multiple entries in this table for the same person. The name of the individual never changes, but they change their profile picture. Each profile picture change is captured in this table. Facebook gives users a new id in this table when they change their profile picture.

• gid Group id
• id Id of the member
• name Name of the member
• url URL of the member

The metrics in this dataset measure users who viewed posts with links to civic news URLs. The dataset contains URL-level metrics from Facebook activity data for adult U.S. monthly active users, aggregated over the study period. Includes content views, audience size, content attributes, user attributes.

We collected data about Facebook pages (November 2017). These datasets represent blue verified Facebook page networks of different categories. Nodes represent the pages and edges are mutual likes among them. We reindexed the nodes in order to achieve a certain level of anonimity. The csv files contain the edges -- nodes are indexed from 0. We included 8 different distinct types of pages. These are listed below. For each dataset we listed the number of nodes an edges.

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).

The number of Facebook users in Malaysia was forecast to continuously decrease between 2024 and 2028 by in total 2.2 million users (-9.36 percent). According to this forecast, in 2028, the Facebook user base will have decreased for the sixth consecutive year to 21.33 million users. 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 further information concerning Indonesia and Singapore.

The metrics in this dataset measure users who potentially viewed posts with links to civic news URLs that were shared by one of their connections. The dataset contains URL-level metrics from Facebook activity data for adult U.S. monthly active users, aggregated over the study period. Includes potential audience size, content attributes, user attributes, political interest.