Meta Platforms had ****** full-time employees as of December 2024, down from ****** people in 2023. As of December 2023, more than ******* employees at tech companies worldwide were laid off throughout the year across more than 1,000 companies. Facebook: how it all beganIn 2003, a sophomore at named Mark Zuckerberg hacked into protected areas of the university's computer network in order to find photos of other students. He then would pair two of them next to each other on a program called “Facemash” and ask users to choose the more attractive person. At the beginning of 2004, Zuckerberg launched “The Facebook,” a social network dedicated to Harvard students, which later grew to encompass Columbia, Yale and Stanford. The popularity of this new service sky-rocketed and in mid-2004, Zuckerberg interrupted his studies and moved his operation to Palo Alto, California, in the heart of Silicon Valley. By 2006, Facebook was open to the general public. In 2020, the company reported almost ** billion U.S. dollars in revenue and a net income of ***** billion US dollars. It is also the most popular social network in the world, with *** billion monthly active users as of December 2020. Facebook employee diversity criticismLike many other tech companies, Facebook has been criticized for having a diversity problem. As of June 2020, tech positions, as well as management roles in U.S. offices were overwhelmingly occupied by men. Furthermore, almost ** percent of Facebook employees in the U.S. are White and only *** percent are African-American, which has sparked concern regarding representation and equal opportunities. Around **** percent of senior level positions are occupied by White employees and only *** percent by Hispanic-Americans.

Meta reported 67.32K in Employees for its fiscal year ending in December of 2023. Data for Meta | FB - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last August in 2025.

This statistic displays the number of individuals employed by Facebook UK in the United Kingdom (UK) from 2011 to 2019. The number of employees based in the UK has increased significantly since 2011, reaching an average of ***** employees monthly in 2019.

Facebook has come a long way since its humble beginnings in Spain in 2008, and its expaning strategy has reflected on the company's staff. As can be seen, the number of employees of the company rose from ** to ** workers from 2014 to 2019. It is a small number of the Facebook's global workforce but a significant increase for the Spanish subsidiary.

In 2023, Amazon.com was the top-ranked internet company based on number of employees. The e-commerce giant reported a workforce of more than **** million employees. Amazon has consistently topped the ranking as the online company with the biggest workforce, but the global COVID-19 pandemic has widened the gap as e-commerce has boomed since. During the same period, Meta (formerly Facebook Inc.) had a total of ****** full-time employees. Additionally, Google's parent company Alphabet had ******* full-time workers in 2024.

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...

In 2022, 6.5 percent of Meta employees in the United States identified as Hispanic and 4.9 percent identified as Black. Asian employees accounted for over 46.5 percent of the overall workforce, whilst white employees made up 37.6 percent of Meta's workforce.

This dataset is about companies in Catano. It has 1 row. It features 3 columns: employees, and Facebook link.

This dataset is about companies in Astoria. It has 161 rows. It features 3 columns: employees, and Facebook link.

As of June 2022, only 37.1 percent of all global Meta Platforms employees were women. The majority of employees were male. Overall, women made up 25.8 percent of tech roles and 60.5 percent of non-tech roles.

This dataset is about companies. It has 203,841 rows and is filtered where the sector is Information Technology. It features 3 columns: employees, and Facebook link.

Abstract

To gain a deeper understanding of the perspectives, challenges, and opportunities for small and medium sized businesses (SMBs) around the world during the COVID-19 pandemic, Facebook and partners collaborate to collect and share timely information with the broader community. The State of Small Business (SoSB) Survey surveys SMBs, employees, and consumers from approximately 30 countries across the globe. This combination of survey respondents allows us to evaluate how the impacts on SMBs, their employees, and their clients have developed throughout 2021.

Geographic coverage

Argentina Australia Belgium Brazil Canada Colombia Egypt France Germany Ghana India Indonesia Ireland Israel Italy Kenya Mexico Nigeria Pakistan Philippines Poland Portugal Russian Federation (the) South Africa Spain Taiwan Turkey United Kingdom of Great Britain and Northern Ireland United States of America (the) Vietnam

Analysis unit

The study describes small and medium-sized business owners, their employees and consumers.

Universe

The survey uses a random sample of SMB leaders with Facebook Page administrator privileges and of the general population of Facebook users. Therefore, the sample covered in the survey is representative of SMB leaders surveyable through Facebook at the country level.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey reaches a random sample of SMB leaders with Facebook Page administrator privileges and of the general population of Facebook users. A random sample of firms, representing the target population in each country, is selected to respond to the survey. To achieve better representation of the broader small business population on Facebook, Facebook also weights our results based on known characteristics of the Facebook Page admin population.

Mode of data collection

Internet [int]

Research instrument

Questions cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, access to finance, digital technologies, reduction in revenues, business closures, reduction of employees and challenges/needs of the business

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design. To achieve better representation of the broader small business population on Facebook, Facebookwe also weights our results based on known characteristics of the Facebook Page admin population.

Note: Response rates are calculated as the number of respondents who completed the survey divided by the total number of SMBs invited.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy: Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.Other factors beyond sampling error that contribute to such potential differences are frame or coverage error (sampling frame of Page owners does not include all relevant businesses but also may include individuals that don’t represent businesses), and nonresponse error.

Note that the sample is meant to reflect the population of businesses on Facebook, not the population of small businesses in general. This group of digitized SMEs is itself a community worthy of deeper consideration and of considerable policy interest. However, care should be taken when extrapolating to the population of SMEs in general. Moreover, future work should evaluate the external validity of the sample. Particularly, respondents should be compared to the broader population of SMEs on Facebook, and the economy as a whole.

This dataset is about companies in Chipata. It has 1 row. It features 3 columns: employees, and Facebook link.

As of June 2022, 37.1 percent of worldwide Meta employees were women, an increase of 0.5 percent in the previous year. Overall, almost 63 percent of the company were men. The company has reported diversity metrics since 2014, and whilst the share of women employed by the company has increased, men continue to account for the overall majority. Moreover, Meta have reported that women were more likely to accept remote job offers.

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.

This dataset is about companies in Ecuador. It has 2,057 rows. It features 3 columns: employees, and Facebook link.

At the end of the most recently reported year, microblogging and social networking company X (formerly Twitter) employed 7,500 people, up from 5,500 people in the previous year.

X/Twitter's corporate demography

In 2020, the majority of X/Twitter'semployees were male with a share of 57 percent and of a white ethnicity with 41 percent. African American and Latinx ethnicities were severely underrepresented with only 6.5 and 5.4 percent share respectively of all employees at Twitter.

Distribution of X/Twitter employees by gender and department in 2020 is revealing. In tech departments, over 73 percent of employees were male. Men also dominated the leadership departments with 62 percent. X/Twitter was founded by Jack Dorsey, Noah Glass, Biz Stone and Evan Williams in March 2006 and since then, a man has always held top positions of chairman and CEO. The only department at X/Twitter whereby women were represented well was in the Non-tech departments. In 2017, women held 53.7 percent of non-tech roles. Other social media companies The gender landscape at Facebook in 2020 followed a similar vein. The distribution of Facebook employees worldwide by gender and department revealed that men dominated the tech departments with a 76 percent share and senior level positions with a 66 percent share.

As of June 2022, 57.6 percent of employees in leadership roles at Meta were white, whilst 28.6 percent were Asian. Overall, 11.7 percent of employees in non-technical roles were Hispanic, and 11.2 percent were Black. Moreover, Asian employees accounted for the majority of employees in technical roles, making up 55.8 percent of employees in these positions.

Time period: June 1, 2020 through Election Day 2020Through its Cross-Platform Election Advertising Transparency Initiative (CREATIVE) funded in part through a grant from the NSF (Award Number 2235006), the Wesleyan Media Project is actively working on identifying and summarizing federal election content from the general election periods in federal cycles (September through Election Day). In late 2020, we received a request from the collaboration between Facebook and academics studying the 2020 election to obtain our classifications of advertising data relevant to the presidential election (defined by mentions of either candidate). We already had September through Election Day content in hand and worked backward to acquire June through August 2020 content relevant to presidential mentions solely in service of this request. The data provided here, which cover the June 1 to Election Day period from 2020, apply many of our methods employed in CREATIVE but precedes many of the refinements that we have made in identifying and classifying federal advertising to the content relevant to presidential ads. In addition, in the provided data we did not conduct a lot of fine tuning just for the presidential race. If we had built methods solely for delivery of presidential advertising in particular, we likely would have chosen different methods. We would like to remind researchers that the earlier (June through August) period contains, for many races, primary advertising rather than general election advertising, and we did not examine carefully the differences between these two.

This dataset is about companies in Pearl City. It has 24 rows. It features 3 columns: employees, and Facebook link.