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

Context Collection of Facebook spam-legit profile and content-based data. It can be used for classification tasks.

Content The dataset can be used for building machine learning models. To collect the dataset, Facebook API and Facebook Graph API are used and the data is collected from public profiles. There are 500 legit profiles and 100 spam profiles. The list of features is as follows with Label (0-legit, 1-spam). 1. Number of friends 2. Number of followings 3. Number of Community 4. The age of the user account (in days) 5. Total number of posts shared 6. Total number of URLs shared 7. Total number of photos/videos shared 8. Fraction of the posts containing URLs 9. Fraction of the posts containing photos/videos 10. Average number of comments per post 11. Average number of likes per post 12. Average number of tags in a post (Rate of tagging) 13. Average number of hashtags present in a post

Inspiration Dataset helps the community to understand how features can help to differ Facebook legit users from spam users.

Please cite the following paper when using this dataset: N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A.Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” arXiv [cs.CY], 2024. Available: http://arxiv.org/abs/2406.07693 Abstract This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

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.

Dataset Card for PLM-Video Human

PLM-Video-Human is a collection of human-annotated resources for training Vision Language Models, focused on detailed video understanding. Training tasks include: fine-grained open-ended question answering (FGQA), Region-based Video Captioning (RCap), Region-based Dense Video Captioning (RDCap) and Region-based Temporal Localization (RTLoc). [📃 Tech Report] [📂 Github]

  Dataset Structure






  Fine-Grained Question Answering… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PLM-Video-Human.

This dataset is designed to explore multistreaming social media video as a research method used to collect semi-structured interview data. The data are provided by Dr Karen E. Sutherland and Ms Krisztina Morris from the School of Business and Creative Industries at the University of the Sunshine Coast in Queensland, Australia. The dataset is drawn from the publicly available video recording of an interview undertaken as part of the research project called: ‘Like, Share, Follow’, a multistreaming show, featuring Dr Sutherland interviewing university graduates about their career journeys, that is broadcast across Facebook, LinkedIn, and Twitter and later uploaded to YouTube. This dataset examines how multistreaming video interview data can be used to answer research questions and the benefits and challenges this specific method of data collection can pose in the process of data analysis. The video example is accompanied by a teaching guide and a student guide.

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.

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)

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.

Dataset Summary

PLM-VideoBench is a collection of human-annotated resources for evaluating Vision Language models, focused on detailed video understanding. [📃 Tech Report] [📂 Github]

  Supported Tasks

PLM-VideoBench includes evaluation data for the following tasks:

  FGQA

In this task, a model must answer a multiple-choice question (MCQ) that probes fine-grained activity understanding. Given a question and multiple options that differ in a… See the full description on the dataset page: https://huggingface.co/datasets/facebook/PLM-VideoBench.

Anansi Masters - the story continuesThe Anansi Masters project is developed by Vista Far Reaching Visuals (Mr. Jean Hellwig) and partners. It is designed as a public digital platform at http://www.anansimasters.net and opened in 2007. At the website one can find information about the story character of Nanzi (or Anansi or Kweku Ananse), with English and Dutch subtitled video recordings of storytelling in several countries in different languages, educational modules about storytelling for use at schools and academies, and digital issues of the Anansi Masters Journal published since the beginning of the project. All storytelling videos and videos that were made for documentation or marketing purposes are published on Youtube. Since 2012 all films of Anansi Masters were uploaded to Youtube and linked to the Anansi Masters website. Their display is embedded in the website together with the respective metadata that are entered through a custom made content management system (CMS).In March 2012, public storytelling events were organized by Drs. Jean Hellwig (Hellwig Productions AV / Vista Far Reaching Visuals Foundation) on the islands of Curacao and Aruba. Any professional or non-professional storyteller was invited to tell a story in front of the Anansi Masters camera and the available audience. Storytellers were free to choose their story and language. Each storyteller had to agree that the video registration of their story could be made available for open access. Storytellers were asked in front of the camera to answer a few questions about who they are and how they selected the story that they told.The Anansi Masters project started in 2007 with the registration of Kweku Ananse stories in Ghana and The Netherlands. The storytelling events organized on Curacao and Aruba in 2012 were part of the second phase 'Anansi Masters - the story continues'. The project registers contemporary ways of storytelling from an old tradition and aims to stimulate and revitalize the Nanzi storytelling by making the storytelling videos available to a large international audience. In 2008 a dvd in Dutch was released with 22 stories from Ghana and The Netherlands. In 2013 a dvd in English is released with all 32 stories that were recorded on Curaçao and Aruba.The stories of the Anansi tradition originate in Africa and were exported to other parts of the world through slave trade and migration. In Anansi Masters, the similarities and differences between the stories and storytellers, who tell in their own language, can be found. Anansi Masters initiates different activities all over the world where stories from this oral tradition can be found. The founder has the ambition to film as many stories from this tradition as possible in as many countries as possible. Anansi Masters collaborates with writers, theatre makers, filmmakers, researchers, schools and of course with many many storytellers.This dataset contains the documentation, video files, documents and pictures that were made to document the second phase of the Anansi Masters project with the subtitle 'the story continues'. These files were produced to report the process and results to the sponsoring funds and to be used in marketing through Facebook.This dataset contains the following:- report in Dutch with separate appendices- videos with datasheets 0015 - 0022 reflecting some of performances in the media to market the storytelling events- short video impression with datasheet 0023 of a musical performance at the storytelling event in Curacao- a list with names and codes of the recorded stories and storytellersFor each storyteller and their stories a new dataset has been created. Links to these datasets can be found under 'Relations'.

Of the videos published in 2019 on the social media platform Facebook, the video titled "Illusionist Riana on Asia's Got Talent 2017" was ranked first with over 614 million views. The second most-viewed video released on Facebook in 2019 was "Get clever with your clutter...and these 7 organization hacks!" with 446 million views.

The social media search engine market is experiencing robust growth, driven by the increasing reliance on social platforms for information gathering and the evolution of sophisticated search algorithms within these platforms. The market, while difficult to precisely quantify due to the interwoven nature of search functionality within social media platforms (many don't offer dedicated search engines), is estimated to be valued at approximately $50 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors: the expanding user base of social media platforms, the increasing sophistication of social media search algorithms (better understanding of natural language queries and visual search capabilities), and the rise of social commerce, which intrinsically relies on effective search within social networks to discover products and services. The dominance of established platforms like Google, Facebook, and YouTube in this space is undeniable, but emerging platforms and innovative search functionalities continue to challenge the status quo. Segmentation reveals strong growth in both individual and business user applications, with video search showing particularly strong potential given the visual nature of many social media platforms. However, the market also faces restraints. Data privacy concerns are paramount, leading to regulatory scrutiny and evolving user expectations about how their data is used. Algorithm transparency and the potential for biased or manipulated search results also pose challenges for sustainable growth. Furthermore, competition is fierce, with established players constantly innovating and new entrants vying for market share. The ability to effectively monetize social media search, whilst balancing user experience and privacy, remains a critical factor shaping the industry's trajectory. Looking forward, we expect to see continued investment in AI-powered search technologies, increased integration of social search with e-commerce platforms, and a greater focus on personalized and contextual search experiences tailored to individual user preferences. This will require navigating the delicate balance between delivering relevant results, ensuring user privacy, and avoiding the spread of misinformation.

Does online fundraising increase charitable giving? Using the Facebook advertising tool, we implemented a natural field experiment across Germany, randomly assigning almost 8,000 postal codes to Save the Children fundraising videos or to a pure control. We studied changes in the donation revenue and frequency for Save the Children and other charities by postal code. Our georandomized design circumvented many difficulties inherent in studies based on click-through data, especially substitution and measurement issues. We found that (i) video fundraising increased donation revenue and frequency to Save the Children during the campaign and in the subsequent five weeks; (ii) the campaign was profitable for the fundraiser; and (iii) the effects were similar independent of video content and impression assignment strategy. However, we also found some crowding out of donations to other similar charities or projects. Finally, we demonstrated that click data may be an inappropriate proxy for donations and recommend that managers use careful experimental designs that can plausibly evaluate the effects of advertising on relevant outcomes. For further information on the design of the study see also: Adena, Maja, & Hager, Anselm (forthcoming). Does online fundraising increase charitable giving? A nationwide field experiment on Facebook. Management Science. Nicht-Wahrscheinlichkeitsauswahl - Respondenten-gesteuerte Auswahl Field/Intervention experimentExperiment.FieldIntervention Feld-/InterventionsexperimentExperiment.FieldIntervention

A free, publicly available Malaysia Covid-19 dataset.

Data Descriptions

28 variables. Include:

New case New case (7 day rolling average) Recovered Active case Local cases Imported case ICU Death Cumulative deaths

People tested Cumulative people tested Positivity rate Positivity rate (7 day rolling average)

Data Sources

Column 1 to 22 are Twitter data, which the Tweets are retrieved from Health DG @DGHisham timeline with Twitter API. A typical covid situation update Tweet is written in a relatively fixed format. Data wrangling are done in Python/Pandas, numerical values extracted with Regular Expression (RegEx). Missing data are added manually from Desk of DG (kpkesihatan).

Column 23 ['remark'] is my own written remark regarding the Tweet status/content.

Column 24 ['Cumulative people tested'] data is transcribed from an image on MOH COVID-19 website. Specifically, the first image under TABURAN KES section in each Situasi Terkini daily webpage of http://covid-19.moh.gov.my/terkini. If missing, the image from CPRC KKM Telegram or KKM Facebook Live video is used. Data in this column, dated from 1 March 2020 to 11 Feb 2021, are from Our World in Data, their data collection method as stated here.

Why does this dataset exist?

MOH does not publish any covid data in csv/excel format as of today, they provide the data as is, along with infographics that are hardly informative. In an undisclosed email, MOH doesn't seem to understand my request for them to release the covid public health data for anyone to download and do their analysis if they do wish.

To be updated periodically

A simple visualization dashboard is now published on Tableau Public. It's is updated daily. Do check it out! More charts to be added in the near future

Inspiration

Create better visualizations to help fellow Malaysians understand the Covid-19 situation. Empower the data science community.

Reward-based crowdfunding is a typical two-sided platform (fundraiser side and backer side) with high information asymmetry. While existing research indicates that signals from fundraisers and backers can impact crowdfunding performance, the interplay among these signals warrants further investigation. Drawing on signaling theory, this study adopts a configurational perspective and utilizes the fsQCA method and linear regression to investigate the combined effects of fundraiser engagement (update and fundraiser comment), third-party endorsement (backer comment and Facebook sharing), and project preparedness (video, image, and description) on crowdfunding performance. Drawing data from the reward-based crowdfunding platform Indiegogo, this research pointed out that these signals cannot generate better crowdfunding performance alone and examined substitution and complementary effects among different signals. Based on the linear regression and fsQCA results, configurations that lead to high crowdfunding performance are identified. We found that project preparedness must work with other signals to produce high crowdfunding performance. Besides, we summarized these configurations into two patterns that may lead to high crowdfunding performance: a fundraiser engagement-driven pattern and a third-party endorsement-driven pattern. This study contributes a configurational perspective and valuable insights into how signals can work together to mitigate information asymmetry in crowdfunding.

Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users, although there is rapid uptake among older age groups. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the last few years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform reported that it averaged revenue growth of over 450% between 2019 and 2022. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers have pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. Revenue is expected to grow by 14.3% in 2024-25, constrained by a slowdown in user growth for most major social media platforms. Over the five years through 2024-25, revenue is forecast to expand at a compound annual rate of 32.8% to reach £9.8 billion. Looking forward, regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The rising prominence of AI will require the introduction of adequate regulations. The Online Safety Bill sets out new guidelines for social media platforms to abide by, with hefty fines in store for those who do not. Operating costs will swell as platforms look to meet consumers’ expectations, weighing on profit. Over the five years through 2029-30, social media platforms' revenue is projected to climb at an estimated 9.4% to reach £15.4 billion.

Recently, a large number of research has been done on different language conversions from standard Bangla. However, only a limited number of effective works have been done in Bangla dialect conversion. We developed the “ChatgaiyyaAlap” dataset to convert the Chittagongian dialect into standard Bangla. The dataset has two Comma Separated Values (.CSV) files. The first file is for Chittagonian and Bangla sentences. This file contains two columns: one is for Standard Bangla sentences, and the other one is for Chittagonian sentences. For both columns, each row contains sentences in Standard Bangla and their translations in the Chittagonian dialect. The other file contains word mapping of the Chittagonian dialect and standard Bangla which is our state-of-the-art dictionary file. The Chittagonian sentences, in the first CSV file, were collected from diverse sources like Youtube and Facebook posts, comments, videos, short films, and dramas in the Chittagongian dialect. After data collection and preprocessing, we evaluated our collected data through five professional human evaluators who are native speakers of the Chittagong dialect and also know the standard Bangla language. Assembling sentences in the Chittagongian dialect was a slow process, where resource limitation was our major drawback. To speed up our process of data collection, we started to gather Bangla sentences from different social media sites and then translate those sentences into Chittagongian dialect with the assistance of five native speakers. As we verified and translated the data from five different speakers, there is a chance to use more than one synonym for a Bangla word. We tried to use more noticeable terms in our dataset rather than using alternative synonyms for the same phrase in order to avoid any misunderstandings. To keep the system simple and improve the translation process, we have maintained a dictionary file that helps us to select the proper Chittagonian word for a standard Bangla word. So the total dataset consists of two files one is Chittagong and Bangla sentences and the other one is a dictionary file.

As social platforms experience an influx of diverse content from users, the need to determine high-quality contributions becomes crucial, especially for educational purposes. This paper highlights the pivotal role of quality in assessing how educational-purposed user-generated content (UGC) shapes user experiences, fosters engagement, and establishes credibility. This study proposes a computational framework using a quasi-experimental evaluation through the sorting-based ELimination Et Choice TRanslating Reality, termed ELECTRE-SORT, with a dataset randomly generated from normally distributed user evaluations. Considering the diverse nature of contents, the method evaluates 16 educational-purposed UGC videos from different online media platforms (i.e. Facebook, YouTube, TikTok). These videos were categorized based on their concordance and discordance to three (3) main criteria: content quality, design quality, and technology quality. Employing the ELECTRE-SORT reveals that most UGC videos (i.e. 14 out of 16) fall into the “medium quality” category, possessing a considerable standard for the quality of educational purpose content. Their characteristics generally satisfy the quality attributes and can be used to guide the development of future relevant UGC videos. Finally, to demonstrate the robustness of the proposed approach, we presented a sensitivity analysis by designing different weight assignments to the quality attributes. Practical insights are outlined in this work.