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...

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).

Access our extensive Facebook datasets that provide detailed information on public posts, pages, and user engagement. Gain insights into post performance, audience interactions, page details, and content trends with our ethically sourced data. Free samples are available for evaluation. Over 940M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Post ID Post Content & URL Date Posted Hashtags Number of Comments Number of Shares Likes & Reaction Counts (by type) Video View Count Page Name & Category Page Followers & Likes Page Verification Status Page Website & Contact Info Is Sponsored Post Attachments (Images/Videos) External Link Data And much more

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

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16190527%2F53f929ff911508241fa2b6808c9d885f%2FFacebookAdsBidding-1200x630-1.jpg?generation=1696942877786519&alt=media" alt="">

Subject:

Facebook recently introduced a new bidding system called "average bidding"(test group) alongside the existing "maximum bidding"(control group) system. These bidding systems determine which ads get displayed to users based on how much advertisers are willing to pay.

With "maximum bidding," advertisers specify the maximum amount they are willing to pay for each impression. For example, an advertiser might say, "I'm willing to pay a maximum of $10 for each impression."

With "average bidding," advertisers specify an average amount they are willing to pay for impressions. For instance, they might say, "On average, I'm willing to pay $6 for each impression."

Here's the key point: In this dataset, we've gathered the results of these two bidding strategies over the last 40 days to see which one is more effective at getting their ads displayed to the target audience.

Business Challenge: Unleashing the Power of Facebook Advertising

As a forward-thinking company poised to make waves in the realm of Facebook advertising, we're on a mission to unearth the most advantageous approach for our brand. Our burning questions:

1. Maximize Clicks and Purchases: Which strategy will propel us toward our goal of skyrocketing clicks and driving purchases?
2. Battle of Titans: Is there a discernible and statistically significant difference between the two bidding options?

Enter the AB Test: Today, we embark on a journey of data-driven discovery, where the clash of titans—Average Bidding versus Maximum Bidding—will be meticulously dissected and evaluated. We're on a quest for insights that will define our Facebook advertising strategy's future, using data as our compass and innovation as our weapon.

The outcome of this AB Test will not just answer our questions but will be the harbinger of a glorious era in our Facebook advertising endeavors. Stay tuned for a transformation that will leave a mark on the digital advertising landscape.

Features:

• Impression: Number of impressions per ad.
• Click: Numbers of clicks per ad.
• Purchase: The number of products purchased after the click.
• Earnings: Earning after purchase.

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

The Multi-aspect Integrated Migration Indicators (MIMI) dataset is the result of the process of gathering, embedding and combining traditional migration datasets, mostly from sources like Eurostat and UNSD Demographic Statistics Database, and alternative types of data, which consists in multidisciplinary features and measures not typically employed in migration studies, such as the Facebook Social Connectedness Index (SCI). Its purpose is to exploit these novel types of data for: nowcasting migration flows and stocks, studying integration of multiple sources and knowledge, and investigating migration drivers.

The MIMI dataset is designed to have a unique pair of countries for each row. Each record contains country-to-country information about: migrations flows and stock their share, their strength of Facebook connectedness and other features, such as corresponding populations, GDP, coordinates, NET migration, and many others.

Methodology.

After having collected bilateral flows records about international human mobility by citizenship, residence and country of birth (available for both sexes and, in some cases, for different age groups), they have been merged together in order to obtain a unique dataset in which each ordered couple (country-of-origin, country-of-destination) appears once. To avoid duplicate couples, flow records have been selected by following this priority: first migration by citizenship, then migration by residence and lastly by country of birth.

The integration process started by choosing, collecting and meaningfully including many other indicators that could be helpful for the dataset final purpose mentioned above.

• International migration stocks (having a five-year range of measurement) for each couple of countries.
• Geographical features for each country: ISO3166 name and official name, ISO3166-1 alpha-2 and alpha-3 codes, continent code and name of belonging, latitude and longitude of the centroid, list of bordering countries, country area in square kilometres. Also, the following features have been included for each pair of countries: geodesic distance (in kilometres) computed between their respective centroids.

• Non-bidirectional migration measures for each country: total number of immigrants and emigrants for each year, NET migration and NET migration rate in a five-year range.

• Other multidisciplinary indicators (cultural, social, anthropological, demographical, historical features) related to each country: religion (single one or list), yearly GDP at PPP, spoken language (or list of languages), yearly population stocks (and population densities if available), number of Facebook users, percentage of Facebook users, cultural indicators (PDI, IDV, MAS, UAI, LTO). Also the following feature have been included for each pair of countries: Facebook Social Connectedness Index.

Once traditional and non-traditional knowledge is gathered and integrated, we move to the pre-processing phase where we manage the data cleaning, preparation and transformation. Here our dataset was subjected to various computational standard processes and additionally reshaped in the final structure established by our design choices.

The data quality assessment phase was one of the longest and most delicate, since many values were missing and this could have had a negative impact on the quality of the desired resulting knowledge. They have been integrated from additional sources such as The World Bank, World Population Review, Statista, DataHub, Wikipedia and in some cases extracted from Python libraries such as PyPopulation, CountryInfo and PyCountry.

The final dataset has the structure of a huge matrix having countries couples as index (uniquely identified by coupling their ISO 3166-1 alpha-2 codes): it comprises 28725 entries and 485 columns.

Dataset Card for MultiLingual LibriSpeech

  Dataset Summary

This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from OpenSLR to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It… See the full description on the dataset page: https://huggingface.co/datasets/facebook/multilingual_librispeech.

The study contains news articles which have been shared on Facebook and Twitter over time.

Purpose:

To study what news articles are shared on Facebook and Twitter over time.

This dataset contains 953,069 news articles (title, URL, social shares) from the 12 largest Swedish news sites during 20 months, from February 2014 to October 2015.

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?

The number of Facebook users in Belgium was forecast to increase between 2024 and 2028 by in total **** million users (+**** percent). This overall increase does not happen continuously, notably not in 2026. The Facebook user base is estimated to amount to **** million users in 2028. 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).

How does Facebook always seems to know what the next funny video should be to sustain your attention with the platform? Facebook has not asked you whether you like videos of cats doing something funny: They just seem to know. In fact, FaceBook learns through your behavior on the platform (e.g., how long have you engaged with similar movies, what posts have you previously liked or commented on, etc.). As a result, Facebook is able to sustain the attention of their user for a long time. On the other hand, the typical mHealth apps suffer from rapidly collapsing user engagement levels. To sustain engagement levels, mHealth apps nowadays employ all sorts of intervention strategies. Of course, it would be powerful to know—like Facebook knows—what strategy should be presented to what individual to sustain their engagement. To be able to do that, the first step could be to be able to cluster similar users (and then derive intervention strategies from there). This dataset was collected through a single mHealth app over 8 different mHealth campaigns (i.e., scientific studies). Using this dataset, one could derive clusters from app user event data. One approach could be to differentiate between two phases: a process mining phase and a clustering phase. In the process mining phase one may derive from the dataset the processes (i.e., sequences of app actions) that users undertake. In the clustering phase, based on the processes different users engaged in, one may cluster similar users (i.e., users that perform similar sequences of app actions).

List of files

0-list-of-variables.pdf includes an overview of different variables within the dataset. 1-description-of-endpoints.pdf includes a description of the unique endpoints that appear in the dataset. 2-requests.csv includes the dataset with actual app user event data. 2-requests-by-session.csv includes the dataset with actual app user event data with a session variable, to differentiate between user requests that were made in the same session.

Overview

This data set consists of links to social network items for 34 different forensic events that took place between August 14th, 2018 and January 06th, 2021. The majority of the text and images are from Twitter (a minor part is from Flickr, Facebook and Google+), and every video is from YouTube.

Data Collection

We used Social Tracker (https://github.com/MKLab-ITI/mmdemo-dockerized), along with the social medias' APIs, to gather most of the collections. For a minor part, we used Twint (https://github.com/twintproject/twint). In both cases, we provided keywords related to the event to receive the data.

It is important to mention that, in procedures like this one, usually only a small fraction of the collected data is in fact related to the event and useful for a further forensic analysis.

Content

We have data from 34 events, and for each of them we provide the files:

items_full.csv: It contains links to any social media post that was collected.

images.csv: Enlists the images collected. In some files there is a field called "ItemUrl", that refers to the social network post (e.g., a tweet) that mentions that media.

video.csv: Urls of YouTube videos that were gathered about the event.

video_tweet.csv: This file contains IDs of tweets and IDs of YouTube videos. A tweet whose ID is in this file has a video in its content. In turn, the link of a Youtube video whose ID is in this file was mentioned by at least one collected tweet. Only two collections have this file.

description.txt: Contains some standard information about the event, and possibly some comments about any specific issue related to it.

In fact, most of the collections do not have all the files above. Such an issue is due to changes in our collection procedure throughout the time of this work.

Events

We divided the events into six groups. They are,

1. Fire

• Devastating fire is the main issue of the event, therefore most of the informative pictures show flames or burned constructions

• 14 Events

2. Collapse

• Most of the relevant images depict collapsed buildings, bridges, etc. (not caused by fire).

• 5 Events

3. Shooting

• Likely images of guns and police officers. Few or no destruction of the environment.

• 5 Events

4. Demonstration

• Plethora of people on the streets. Possibly some problem took place on that, but in most cases the demonstration is the actual event.

• 7 Events

5. Collision

• Traffic collision. Pictures of damaged vehicles on an urban landscape. Possibly there are images with victims on the street.

• 1 Event

6. Flood

• Events that range from fierce rain to a tsunami. Many pictures depict water.

• 2 Events

We enlist the events in the file recod-ai-events-dataset-list.pdf

Media Content

Due to the terms of use from the social networks, we do not make publicly available the texts, images and videos that were collected. However, we can provide some extra piece of media content related to one (or more) events by contacting the authors.

Funding

DéjàVu thematic project, São Paulo Research Foundation (grants 2017/12646-3, 2018/18264-8 and 2020/02241-9)

CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of crowdsourced voice recordings.

Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .mp3 format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the .map() function as follows:

import torchaudio

def map_to_array(batch):
  speech_array, _ = torchaudio.load(batch["file"])
  batch["speech"] = speech_array.numpy()
  return batch

dataset = dataset.map(map_to_array, remove_columns=["file"])

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.

The number of Facebook users in Europe was forecast to continuously increase between 2024 and 2028 by in total 15.5 million users (+3.91 percent). According to this forecast, in 2028, the Facebook user base will have increased for the sixth consecutive year to 412.26 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 South America and North America.

*** Newly Emerged Rumors in Twitter ***

These 12 datasets are the results of an empirical study on the spreading process of newly emerged rumors in Twitter. Newly emerged rumors are those rumors whose rise and fall happen in a short period of time, in contrast to the long standing rumors. Particularly, we have focused on those newly emerged rumors which have given rise to an anti-rumor spreading simultaneously against them. The story of each rumor is as follow :

1- Dataset_R1 : The National Football League team in Washington D.C. changed its name to Redhawks.

2- Dataset_R2 : A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

3- Dataset_R3 : Facebook CEO Mark Zuckerberg bought a "super-yacht" for $150 million.

4- Dataset_R4 : Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

5- Dataset_R5 : Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

6- Dataset_R6 : Harley-Davidson's chief executive officer Matthew Levatich called President Trump "a moron."

7- Dataset_R7 : The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

8- Dataset_R8 : Michael Jordan resigned from the board at Nike and took his Air Jordan line of apparel with him.

9- Dataset_R9 : In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

10- Dataset_R10 : During confirmation hearings for Supreme Court nominee Brett Kavanaugh, congressional Democrats demanded that the nominee undergo DNA testing to prove he is not Adolf Hitler.

11- Dataset_R11 : Singer Michael Bublé's upcoming album will be his last, as he is retiring from making music.Singer Michael Bublé's upcoming album will be his last, as he is retiring from making music.

12- Dataset_R12 : A screenshot from MyLife.com confirms that mail bomb suspect Cesar Sayoc was registered as a Democrat.

The structure of excel files for each dataset is as follow :

- Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet :

- User ID (user who has posted the current tweet/retweet)

- The description sentence in the profile of the user who has published the tweet/retweet

- The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

- Date and time of creation of the the account by which the current tweet/retweet has been posted

- Language of the tweet/retweet

- Number of followers

- Number of followings (friends)

- Date and time of posting the current tweet/retweet

- Number of like (favorite) the current tweet had been acquired before crawling it

- Number of times the current tweet had been retweeted before crawling it

- Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

- The source (OS) of device by which the current tweet/retweet was posted

- Tweet/Retweet ID

- Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

- Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

- Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

- Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

- State of the tweet which can be one of the following forms (achieved by an agreement between the annotators) :

r : The tweet/retweet is a rumor post

a : The tweet/retweet is an anti-rumor post

q : The tweet/retweet is a question about the rumor, however neither confirm nor deny it

n : The tweet/retweet is not related to the rumor (even though it contains the queries related to the rumor, but does not refer to the rumor)

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.

The number of Facebook users in Central & Western Europe was forecast to decrease between 2024 and 2028 by in total 29.8 million users. This overall decrease does not happen continuously, notably not in 2026 and 2027. The Facebook user base is estimated to amount to 192.47 million users 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).Find more key insights for the number of Facebook users in countries like Eastern Europe and Russia.