The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users 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).

1) Data Introduction • The Facebook Data is a social network analysis data that can be used to identify key user groups that can contribute to business growth and develop recommendation strategies, including Facebook users' activity patterns, interactions, likes, friendships, gender, and age.

2) Data Utilization (1) Facebook Data has characteristics that: • This dataset consists of numerical and categorical variables such as user ID, gender, age, number of friends, number of likes (mobile/web), number of friend requests, number of likes received/sent, and frequency of activities, allowing you to analyze user-specific behavioral characteristics and interaction patterns from multiple angles. (2) Facebook Data can be used to: • Core User Group Targeting and Recommendation Strategies: Use key characteristics such as gender, age, frequency of activity, friends and likes to identify user groups that have a significant impact on business growth and to develop customized content and advertising recommendation strategies. • Analysis of Usage Behavior and Platform Trends: Mobile and Web-based Good By analyzing data such as distribution, age and gender activity patterns, and friend relationship formation, you can visually explore changes in user usage behavior and major trends within the platform.

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.

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

This dataset tracks annual distribution of students across grade levels in F B Mccord Elementary School

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.

Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D’Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again. Resolution: geotiff is provided at Bing Tile Level 20

Context

COVIDcast displays signals related to COVID-19 activity levels across the United States, derived from a variety of anonymized, aggregated data sources made available by multiple partners.

One of COVIDcast streams displays results for a CMU-run symptom survey, advertised through Facebook.

Content

This dataset is gathered using the delphi-epidata API and contains covidcast_meta and covidcast datasources.

Presently the dataset contains fb-survey data signal which is based on CMU-run symptom surveys, advertised through Facebook. Using this survey data, CMU estimate the percentage of people in a given location, on a given day that have CLI (covid-like illness = fever, along with cough, or shortness of breath, or difficulty breathing), and separately, that have ILI (influenza-like illness = fever, along with cough or sore throat).

Dataset Description

Files are organized in folders based on the spatial resolution of fb-survey data (state, county, hrr, msa).

Each file contains the percentage of people in a given location, on a given day that have CLI or ILI. Data consists of raw and smoothed estimates and is gathered for all time values available at delphi-epidata.

Each file contains the following columns: - geo_value - location code - time_value - time unit (e.g. date) over which underlying events happened - direction - trend classifier (+1 -> increasing, 0 steady or not determined, -1 -> decreasing) - value - value (statistic) derived from the underlying data source - stderr - standard error of the statistic with respect to its sampling distribution, null when not applicable - sample_size - number of "data points" used in computing the statistic, null when not applicable

Additionally, the dataset contains the most recent covidcast_meta where you can find the summary statistics for fb-survey data.

PUG Animals

The PUG: Animals dataset contains 215,040 pre-rendered images based on Unreal-Engine using 70 animal assets, 64 environments, 3 sizes, 4 textures, under 4 camera orientations. It was designed with the intent to create a dataset with variation factors available. Inspired by research on out-of-distribution generalization, PUG: Animals allows one to precisely control distribution shifts between training and testing which can provide better insight on how a deep neural… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PUG_Animals.

As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. The data can also be explored via an interactive map - http://columbia.maps.arcgis.com/apps/View/index.html?appid=ce441db6aa54494cbc6c6cee11b95917 Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The United States COVID-19 Trends and Impact Survey (CTIS) was a voluntary survey of Facebook users in the United States conducted from April 2020 to June 2022. CTIS was intended to aid in pandemic forecasting and response at fine spatiotemporal detail. Through collaboration with Meta, it randomly sampled Facebook active users at a rate sufficient to provide roughly 35,000 responses per day, on average. Survey questions covered topics including COVID-like symptoms, behavior (such as social distancing), COVID testing, mental health, health-related beliefs, trust in officials and information sources, schooling, vaccination acceptance and hesitancy, and related subjects. Respondents provided their ZIP code. Demographic variables include age, gender, education, race/ethnicity, and occupation. Meta generated survey weights to correct for non-response and to match the US adult population age and gender distribution. The 27 datasets make up the microdata. Users should see the Microdata User Guide for documentation on the use and interpretation of the microdata files. Two zip files are available for public download: a monthly data zip file and a weekly data zip file. These include the aggregate data. To access these files, go to the "Download" tab and select "Other." Ensure you have enough storage space before proceeding, as the files are large.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. The data can also be explored via an interactive map - http://columbia.maps.arcgis.com/apps/View/index.html?appid=ce441db6aa54494cbc6c6cee11b95917 Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

Bees and the ecosystem services they provide are vital to urban ecosystems, but little is understood about their distributions, particularly in the Asian tropics. This is largely due to taxonomic impediments and limited inventorying, monitoring, and digitization of occurrence records. While expert collections (EC) are demonstrably insufficient by themselves as a data source to model and understand bee distributions, the boom of community science (CS) in urban areas provides an untapped opportunity to learn about bee distributions within our cities. We used CS observations in combination with EC observations to model the distribution of bees in Singapore, a small tropical city-state in Southeast Asia. To address the restricted spatial context, we performed multiple bias corrections and show that species distribution models performed well when estimating the distribution of habitat specialists with distinct range limits detectable within Singapore. We successfully modelled 37 bee species,..., Observations were obtained from both CS and EC databases (Table 1). EC observations were defined as those collected by individuals possessing specialised knowledge and experience in the study of bees, including formall-trained students in entomology and researchers associated with the National University of Singapore Insect Diversity Laboratory (PI Ascher), using targeted and standardised sampling methods (Ascher et al., 2019) supplemented by opportunistic sampling. This dataset was the primary basis for recent conservation assessments (Ascher et al., 2022). CS sources were defined as observations collected in large part by non-experts or non-formally trained individuals through open access repositories, such as iNaturalist (Robinson et al., 2020) and social media - specifically from the Facebook Group “The Bees and Wasps of Singapore†(https://www.facebook.com/groups/1450495321695805). Observations can be submitted by anyone to iNaturalist, which are then publicly available for other u..., , # Community science enhances modelled bee distributions in a tropical Asian city

Description of the data and file structure

Observations were obtained from both community science(CS) and expert-collected(EC) databases (Table 1). EC observations were defined as those collected by individuals possessing specialised knowledge and experience in the study of bees, including formally-trained students in entomology and researchers associated with the National University of Singapore Insect Diversity Laboratory (PI Ascher), using targeted and standardised sampling methods (Ascher et al., 2019) supplemented by opportunistic sampling. This dataset was the primary basis for recent conservation assessments (Ascher et al., 2022). CS sources were defined as observations collected in large part by non-experts or non-formally trained individuals through open access repositories, such as iNaturalist (Robinson et al., 2020) and social media - specifically from the Facebook Group “The Bees and Wasps ...

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.