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

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.

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 is a collection of 12,478 social media comments found on the official Facebook pages of ten Philippine newspapers, The Philippine Daily Inquirer, Manila Bulletin, The Philippine Star, The Manila Times, Sunstar Cebu, Sunstar Davao, Cebu Daily News, The Freeman, Sunstar Davao, MindaNews, and The Mindanao Times, spanning the years 2015, 2017 and 2019. The comments contain terms related to the Moro identity and the Mamasapano Clash, the Marawi Siege and the establishment of BARMM in the southern Philippines, allowing researchers to study semantic fields with regard to Muslims and the relationship between the texts and the source newspaper, their region of origin, and political administration, among other variables. All comments in the dataset were downloaded through Facebook's Graph API via Facepager (Jünger & Keyling, 2019).

One CSV file (MMB151719SOCMED_v2.csv) is provided, along with a codebook that contains descriptions of the variables and codes used in the CSV file, and a Readme document with a changelog.

Each social media comment is annotated with the following metadata:

object_id: identifier associated with the comment;

message: the textual string of the comment;

message_proc: the textual string of the comment after pre-processing;

lang_label: categorical value for the language of the comment (Tagalog (Filipino), Cebuano, English, Taglish, Bislog, Bislish, Trilingual or Other);

from_name: identifier of public pages (not profiles of individuals) leaving comments (NaN for profiles of individuals, 'NAME' for public pages besides the newspapers, otherwise, the page name of the newspaper);

created_time: Facebook Graph API's-generated string for the date and time the comment was posted;

month_year: categorical value in the form string+YY (e.g. Jun-15) of the month and year when the comment was posted;

year: numerical value in the form YY;

newspaper: categorical value for the newspaper Facebook page under which the comment was found;

corpus: categorical value for comments from the main corpus or the side (control) corpus;

administration: categorical value for political administration (pbsa = President Benigno Aquino III, prrd = President Rodrigo Roa Duterte);

count: numerical value referring to the number of string sequences without spaces;

The dataset may only be used for non-commercial purposes and is licensed under the CC BY-NC-SA 4.0 DEED.

V2 - 05/06/2024

Corrections

Corrections made to region to include Luzon, Visayas and Mindanao (as opposed to Mindanao, non-Mindanao);

Corrections made to administration coding.

This dataset is described by:

Cruz, F. A. (2024). A Multilingual Collection of Facebook Comments on the Moro Identity and Armed Conflict in the Southern Philippines. Journal of Open Humanities Data, 10(1), 41. DOI: https://doi.org/10.5334/johd.219

Bibiliography

Jünger, J., & Keyling, T. (2019). Facepager: An application for automated data retrieval on the web (4.5.3) [Computer software]. https://github.com/strohne/Facepager/

Insights include statistics on how many people posts reached, how many people engaged with each post and how many people talked about the post with their friends

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.

Analysis of ‘prediction of facebook comment’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kiranraje/prediction-facebook-comment on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Dataset is uploaded in ZIP format. The dataset contains 5 variants of the dataset, for the details about the variants and detailed analysis read and cite the research paper TITLE='Comment Volume Prediction

Content

28 columns content in this Dataset 1] Describing popularity or support for the source. 2] Describe how many prople so far visited this place 3]Defines the daily interest of individuals towards source of the document/ Post. 4]Defines the daily interest of individuals towards source of the document/ Post.

--- Original source retains full ownership of the source dataset ---

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.

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.

Purpose For the purpose of informing tobacco intervention programs, this dataset was created and used to explore how online social networks of smokers differed from those of nonsmokers. The study was a secondary analysis of data collected as part of a randomized control trial conducted within Facebook. (See "Other References" in "Metadata" for parent study information.) Basic description of 4 anonymized data files of study participants. fbr_friends: Anonymized Facebook friends networks, basic ego demographics, basic ego social media activity fbr_family: Anonymized Facebook family networks, basic ego demographics, basic ego social media activity fbr_photos: Anonymized Facebook photo networks, basic ego demographics, basic ego social media activity fbr_groups: Anonymized Facebook group networks, basic ego demographics, basic ego social media activity Each network comprises the ego, the ego's first degree connections, and the (second degree) connections between the ego's friends. Missing data and users who did not have friend, family, photo, or group networks were cleaned from the data beforehand. Each data file contains the following columns of data, taken with participant knowledge and consent participant_id: Nonidentifying ids assigned to different study participants. is_smoker: Binary value (0,1) that takes on the value 1 if participant was a smoker and 0 otherwise. gender: One of three categories: male, female, or blank, which signified Other (different from missing data). country: One of four categories: Canada (ca), US (us), Mexico (mx), or Other (xx). likes_count: Numeric data indicating number of Facebook likes the participant had made up to the date the data was collected. wall_count: Numeric data indicating number of Facebook wall posts the participant had made up to the date the data was collected. t_count_page_views: Numeric data indicating number of pages participant had visited in the UbiQUITous app up to the date the data was collected. yearsOld: Numeric data indicating age in years of the participant; right censored at 90 years for data anonymity. vertices: Number of people in the participant's network. edges: Number of connections between people in the network. density: The portion of potential connections in a network that are actual connections; a network-level metric; calculated after removing ego and isolates. mean_betweenness_centrality: An average of the relative importance of all individuals within their own network; a network-level metric; calculated after removing ego and isolates. transitivity: The extent to which the relationship between two nodes in a network that are connected by an edge is transitive (calculated as the number of triads divided by all possible connections); a network-level metric; calculated after removing ego and isolates. mean_closeness: Average of how closely associated members are to one another; a network-level metric; calculated after removing ego and isolates. isolates2: Number of individuals with no connections other than to the ego; a network-level metric. diameter3: Maximum degree of separation between any two individuals in the network; a network-level metric; calculated after removing ego and isolates. clusters3: Number of subnetworks; a network-level metric; calculated after removing ego and isolates. communities3: Number of groups, sorted to increase dense connections within the group and decrease sparse connections outside it (i.e., to maximize modularity); a network-level metric; calculated after removing ego and isolates. modularity3: The strength of division of a network into communities (calculated as the fraction of ties between community members in excess of the expected number of ties within communities if ties were random); a network-level metric. Detailed information on network metrics in the associated manuscript: "An exploration of the Facebook social networks of smokers and non-smokers" by Fu, L, Jacobs MA, Brookover J, Valente TW, Cobb NK, and Graham AL.

Context

The Dataset is uploaded in ZIP format. The dataset contains 5 variants of the dataset, for the details about the variants and detailed analysis read and cite the research paper TITLE='Comment Volume Prediction

Content

28 columns content in this Dataset 1] Describing popularity or support for the source. 2] Describe how many prople so far visited this place 3]Defines the daily interest of individuals towards source of the document/ Post. 4]Defines the daily interest of individuals towards source of the document/ Post.

A Malayalam Question Answering dataset of 5,000 training samples and 5,000 testing samples was generated by translating Facebook bAbI tasks. Facebook's bAbI tasks was originally created in English, some of the languages it has been translated are French, German, Hindi, Chinese, Russian. Twenty fictitious tasks that test a system's capacity for responding to a range of themes, including text comprehension and reasoning, are included in the dataset. Five task-oriented usability questions with comparable sentence patterns are also included in the collection. The questions here range in difficulty. Every job has 1000 test samples and 1000 training samples in the dataset. we created the dataset for the proposed work by utilizing the bAbI dataset to translate the English dataset into Malayalam for five tasks (tasks 1, 4, 11, 12, and 13), represented as tasks 1 through 5. Titles such as "Single Supporting Facts," "Two Argument Relations," "Basic Coreference," "Conjunction," and "Compound Coreference" relate to the tasks. Every sample in the dataset comprises a series of statements (sometimes called stories) about people's movements around things, a question, a suitable answer. Tasks: Task 1: Single supporting fact: This task tests whether a model can identify a single important fact from a story to answer a question. The story usually contains several sentences, but only one sentence is directly useful in answering the question. Task 2: Relationships with two arguments: This task involves understanding the relationship between two entities. The model must infer relationships between pairs of objects, people or places. Task 3: Core co-reference: Co-reference resolution is the task of linking pronouns or phrases to the correct entities. In this task, the model must resolve simple pronominal references. Task 4: Conjunctions: This task tests the model's ability to understand sentences in which several actions or facts are joined by conjunctions such as "and" or "or". The model must process these linked statements to answer the questions correctly. Task 5: Compound Reference: This task is more complex because it requires the model to solve the conjunctions in the sentence with composite entities or more complex structures.

About this Dataset

This dataset contains list of facebook users who are member of integromat facebook group. Integromat (now make.com) is a popular automation SaaS that allows users to design their own automation flow consisting of multiple marketing tools. Competitors of integromat are Zapier, Integrately, etc You can use this list to find propsects who are most likely interested in SaaS products

Category

Lead Generation

Keywords

integromat,automation,rpa,SaaS,make.com

Row Count

17200

Price

$20.00

This dataset contains individual details who committed suicide in Bangladesh during the Pandemic between February, 2020 to November, 2020. This dataset includes details of every individuals who committed suicide like personal details, family & social life, profession, financial condition, methods of committing suicide, location and weather info. The dataset is freely available. The major fields included in this dataset are: age group, age, gender, profession group, reason, method, suicide date & time, addiction status, mental status, economic condition, marital status, family details, academic qualification, weather. Apart from the above data this dataset also contains a CSV file of a Bengali wordcloud built on social media posts of the suicide victims.

The access to the dataset files is kept restricted. Fill up the below form (link in the References section) to request the data.

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.

NOTE: We plan to no longer update this dataset after May 22 2022.

These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.

Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/

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.

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

此一資料集為農業部生物多樣性研究所 Taiwan Biodiversity Research Institute, MOA ; TBRI (原 特有生物研究保育中心 Taiwan Endemic Species Research Institute, TESRI)應用社群網站-臉書(Facebook)成立社團-「慕光之城—蛾類世界(https://www.facebook.com/groups/EnjoyMoths2/)」，邀集對蛾類有興趣的網友加入，並鼓勵加入的網友分享蛾照及拍照地點和日期的資料，特生中心的蛾類科普推廣團隊則使用Access開發資料庫程式將蛾友分享的資料逐筆建檔。該社團成立於2010年11月，迄2025年5月已建檔資料達690,359筆。每筆資料除有日期、海拔高度、經度、緯度等時空分布資料及分類群鑑定結果外，均有一張蛾照作為引證照片，團隊並應用這些資料開發一網站「台灣飛蛾資訊分享站(http://twmoth.tesri.gov.tw/peo/aboutme.aspx)」，提供各界查詢及下載查詢結果。 This dataset is from the Taiwan Biodiversity Research Institute, MOA (Ministry of Agriculture); TBRI (formerly known as the Taiwan Endemic Species Research Institute, TESRI). They established a Facebook group called "City of Moths - Moth World (https://www.facebook.com/groups/EnjoyMoths2/)" to engage and invite internet users interested in moths to join. They encouraged the members to share moth photos along with the location and date of the sightings. The specialized team at the Endemic Species Research Institute used Access to develop a database program to archive the data shared by moth enthusiasts. This group was established in November 2010, and as of May 2025, they have archived 690,359 records. Each record includes information such as date, altitude, longitude, latitude, and taxonomic identification results, along with a moth photograph serving as reference. The team also utilized this data to create a website called the "Taiwan Moth Information Sharing Site (http://twmoth.tesri.gov.tw/peo/aboutme.aspx)" to provide various users with the ability to query and download search results.