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.

In February 2025, Meta's social media platforms were most popular with people aged ******** years in the United Kingdom. Overall, women between the ages of 25 to 34 years made up **** percent of Meta Platforms' total user base, whilst **** percent of all users were men belonging to the same age group. Just **** percent of Meta's UK users were aged between 64 years and above. Additionally, as of January 2024, there were over ** million social media users in the UK.

About this dataset

Context

The dataset tabulates the Meta population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Meta. The dataset can be utilized to understand the population distribution of Meta by age. For example, using this dataset, we can identify the largest age group in Meta.

Key observations

The largest age group in Meta, MO was for the group of age 65 to 69 years years with a population of 18 (14.06%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Meta, MO was the 20 to 24 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Meta is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Meta total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Age. You can refer the same here

As of Febraury 2025, the advertising audience in Romania across Facebook, Instagram, and Facebook Messenger was mainly composed of people of ages between 25 and 54 years old. At the same time, the audience consisted of more women than men.

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.  

As of December 2024, the combined share of reach of Facebook, Instagram, and Messenger social media platforms in the United Arab Emirates was highest among men aged between 25 and 34. These three platforms together accounted for ** percent of the advertising that reached this demographic.

About this dataset

Context

The dataset tabulates the population of Meta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Meta. The dataset can be utilized to understand the population distribution of Meta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Meta. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Meta.

Key observations

Largest age group (population): Male # 30-34 years (11) | Female # 55-59 years (12). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Meta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Meta is shown in the following column.
• Population (Female): The female population in the Meta is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Meta for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Gender. You can refer the same here

In February 2025 in Canada, Meta's social media platforms were most popular with people aged *************** Overall, men and women belonging to this age group made up over ** percent of Meta's audience in Canada, making up the largest share of the company's audience in the country.

In 2018, the word “metaverse” still belonged to the realm of science fiction and ambitious tech demos. Fast forward to 2025, and it’s now a vibrant and fast-expanding digital frontier where people are buying homes, attending classes, working, and socializing, all in virtual environments. From Meta’s persistent push into immersive...

In a small café in Austin, Texas, a 68-year-old grandmother shares reels of her garden with her granddaughter, who lives in Tokyo. Meanwhile, a high school student in Nairobi livestreams his gaming tutorial to friends across the world. Behind these everyday moments is Facebook, the digital backbone connecting over 3...

Data Type: Questionnaire Temporal Features: Annual Primary Unit of Analysis: Community Members, Community Organizations Counties: Wave 1 and Wave 2 County Data Study Component: Core Data Collected by All HCS Sites Primary Data Purpose: Community Engagement and Interaction Logs, Demographics Topics: Resources and Mapping

About this dataset

Context

The dataset tabulates the population of Meta by race. It includes the population of Meta across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Meta across relevant racial categories.

Key observations

The percent distribution of Meta population by race (across all racial categories recognized by the U.S. Census Bureau): 90.63% are white and 9.38% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Meta
• Population: The population of the racial category (excluding ethnicity) in the Meta is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Meta total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Race & Ethnicity. You can refer the same here

This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.

Demographic information for women included in the meta-analysis.

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.

As of October 2025, approximately 2.35 billion people worldwide used Facebook. Around 56.6 percent of the platform’s user base were male.

Business Analyst Metadata Table

AUD meta-analysis summary statistics

Data source

All data was collected from US Census official site: data.census.gov

Data Processing Nuances

The first row in all data files contains column descriptions. It should be ignored in the load, e.g.: df = pd.read_csv('ACSST5Y2018.S0101-Data.csv', skiprows=[1], low_memory=False)

Next, if you need county CFIPS, it can be exctracted from the GEO_ID column: df['CFIPS'] = df['GEO_ID'].apply(lambda x: int(x.split('US')[-1]))

Content List

1. County Demographics

American Community Survey (ACS) data derived from S0101 AGE AND SEX: - ACSST5Y2018.S0101-Data.csv - ACSST5Y2018.S0101-Column-Metadata.csv - ACSST5Y2019.S0101-Data.csv - ACSST5Y2019.S0101-Column-Metadata.csv - ACSST5Y2020.S0101-Data.csv - ACSST5Y2020.S0101-Column-Metadata.csv - ACSST5Y2021.S0101-Data.csv - ACSST5Y2021.S0101-Column-Metadata.csv

Includes basic info on population and age structure

2. Detailed County Demographics

American Community Survey (ACS) data derived from DP05ACS DEMOGRAPHIC AND HOUSING ESTIMATES: - ACSDP5Y2018.DP05-Data.csv - ACSDP5Y2018.DP05-Column-Metadata.csv - ACSDP5Y2019.DP05-Data.csv - ACSDP5Y2019.DP05-Column-Metadata.csv - ACSDP5Y2020.DP05-Data.csv - ACSDP5Y2020.DP05-Column-Metadata.csv - ACSDP5Y2021.DP05-Data.csv - ACSDP5Y2021.DP05-Column-Metadata.csv

Includes detailed info on demographic structure: age, race, sex, etc

3. Business Statistics

County Business Patterns (CBP) data derived from: - CB1800CBP All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2018 - CBP2018.CB1800CBP-Data.csv - CBP2018.CB1800CBP-Column-Metadata.csv - CB1900CBP All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2019 - CBP2019.CB1900CBP-Data.csv - CBP2019.CB1900CBP-Column-Metadata.csv - CB2000CBP All Sectors: County Business Patterns, including ZIP Code Business Patterns, by Legal Form of Organization and Employment Size Class for the U.S., States, and Selected Geographies: 2020 - CBP2020.CB2000CBP-Data.csv - CBP2020.CB2000CBP-Column-Metadata.csv

Includes info on number of establishments, payroll, and other metrics by different business size (less than 5 employees, 5 to 9 employees, etc).

4. Presence of computer and internet

American Community Survey (ACS) data derived from B28003 PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD: - ACSDT5Y2018.B28003-Data.csv - ACSDT5Y2018.B28003-Column-Metadata.csv - ACSDT5Y2019.B28003-Data.csv - ACSDT5Y2019.B28003-Column-Metadata.csv - ACSDT5Y2020.B28003-Data.csv - ACSDT5Y2020.B28003-Column-Metadata.csv - ACSDT5Y2021.B28003-Data.csv - ACSDT5Y2021.B28003-Column-Metadata.csv

5. Computer and internet types

American Community Survey (ACS) data derived from S2801 TYPES OF COMPUTERS AND INTERNET SUBSCRIPTIONS: - ACSST5Y2018.S2801-Data.csv - ACSST5Y2018.S2801-Column-Metadata.csv - ACSST5Y2019.S2801-Data.csv - ACSST5Y2019.S2801-Column-Metadata.csv - ACSST5Y2020.S2801-Data.csv - ACSST5Y2020.S2801-Column-Metadata.csv - ACSST5Y2021.S2801-Data.csv - ACSST5Y2021.S2801-Column-Metadata.csv

Financial overview and grant giving statistics of Meta A And William S Griffith Foundation