This dataset is about books and is filtered where the book is Becoming Facebook : the 10 challenges that defined the company disrupting the world. It has 7 columns such as book, author, ISBN, BNB id, and language. The data is ordered by publication date (descending).

This dataset contains statistics (key metrics) related to the Unleashed Facebook page (https://www.facebook.com/UnleashedADL/). Unleashed is an open data competition, an initiative of the Office for Digital Government, Department of the Premier and Cabinet. This data is used to monitor the level of engagement activity with the audience, and make the communication effective in regards to the event.

This dataset is an OSN-transmitted (Online Social Network) version of the Columbia dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version.

The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in:

[Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

Dataset Card for Winoground

  Dataset Description

Winoground is a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly—but crucially, both captions contain a completely identical set of words/morphemes, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich… See the full description on the dataset page: https://huggingface.co/datasets/facebook/winoground.

This dataset is about book subjects and is filtered where the books is Becoming Facebook : the 10 challenges that defined the company disrupting the world. It has 2 columns: book subject, and publication dates. The data is ordered by earliest publication date (descending).

Social mobilization is a process that enlists a large number of people to achieve a goal within a limited time, especially through the use of social media. There is increasing interest in understanding the factors that affect the speed of social mobilization. Based on the Langley Knights competition data set, we analyzed the differences in mobilization speed between users of Facebook and e-mail. We include other factors that may influence mobilization speed (gender, age, timing, and homophily of information source) in our model as control variables in order to isolate the effect of such factors. We show that, in this experiment, although more people used e-mail to recruit, the mobilization speed of Facebook users was faster than that of those that used e-mail. We were also able to measure and show that the mobilization speed for Facebook users was on average seven times faster compared to e-mail before controlling for other factors. After controlling for other factors, we show that Facebook users were 1.84 times more likely to register compared to e-mail users in the next period if they have not done so at any point in time. This finding could provide useful insights for future social mobilization efforts.

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version.

The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in:

[Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the DSO dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version.

The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in:

[Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

The Hateful Memes Challenge README

The Hateful Memes Challenge is a dataset and benchmark created by Facebook AI to drive and measure progress on multimodal reasoning and understanding. The task focuses on detecting hate speech in multimodal memes. Please see the paper for further details: The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes D. Kiela, H. Firooz, A. Mohan, V. Goswami, A. Singh, P. Ringshia, D. Testuggine

  Dataset details

The files… See the full description on the dataset page: https://huggingface.co/datasets/emily49/hateful-memes.

UPDATED 08/12/2013: The editor of Indie Review Magazine, spoke with Facebook on 08/12/2013, and it was confirmed that the technical difficulities with the advertising account have been resolved.

They will be assisting in an investigation to handle the troll problem with the magazne's page, and the other difficulties the page has been experiencing as a direct result of the stalker's harassment.

Attached you will find screenshots of various ads that we've released on Facebook.

The conclusion: The software predicted a performance for all of the ads pictured of 0.01 - 0.03. Prior to having major issues with negative "SEO" tactics on our facebook page the software's predictions for advertising perforamnce was extremely accurate.

After we began experiencing the troll problem, the advertisements began to perform in a very unpredicatble, sporadic, and oftentimes schizophrenic fashion.

The same ad, with the swimsuit model, showed a sustained performance of 0.08 earlier in the day, and later in the evening, after a series of technial difficulties on Facebook's end (with our page) the perforamance of the ad shot down to 0.19.

We took video of the ads from the moment they began to run until we paused them after the money started to deplete at an alarming rate, and what we found was shocking.

In the first 30 minutes our page was growing at a rate of around 3 likes a second (which is what is expected for the predicted growth rate of the ad - 0.01 to 0.03). This rate of growth had been sustained for days in the past with a very similar ad.

Then suddenly the ad performance plunged to dismal numbers that do not accurately reflect the rate at which our likes were initially gained. Not on the specific day in question nor in the past (with the same or similar ad and page content).

At this time, we have paused all of our facebook ads and plan to thoroughly analyze this data.

As stated in a previous article, which is linked below, we cannot comment directly on the internal workings of Facebook's advertising platform as we do not have access to that system - but our data highly suggests that if someone is using bot accounts they can significantly impact page performance.

We have been relying heavily on our custom software pagacke to anaylze the sitaution, and with the help of my neural networks we were able to identify patterns which revealed problem areas and allowed us to adjust our advertising strategy so that our page would be less suspectible to negative SEO tactics.

UPDATE: 08/10/2013 Facebook will be speaking with the editor of Indie Review Magazine, they indicated that there were experiencing technical difficulties. These technical difficulties have been impacting our page for days, thus we've halted our experimentation and will update with another figshare article, once more information has been obtained.

Reference: 1. http://www.forbes.com/sites/davidthier/2012/08/01/facebook-investigating-claims-that-80-of-ad-clicks-come-from-bots/ 2. Previous figshare article: http://figshare.com/articles/Potential_Issues_with_FB_Advertising_Algorithms_/767331

Dataset

This dataset was created by alifarsi

Contents

More than 200 million businesses use Facebook globally. The goal of Meta’s quarterly Small Business Surveys is to learn about the unique perspectives, challenges and opportunities of small and medium-sized businesses (SMBs).

The Future of Business (FoB) Survey is conducted biannually in partnership with the World Bank and the Organisation for Economic Cooperation and Development (OECD) across nearly 100 countries. The target population consists of SMEs that have an active Facebook Business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. Meta also conducts the Global State of Small Business (GSoSB) Survey bi-annually in partnership with various academic partners across approximately 30 countries. Similarly to the FoB Survey, the target population is active Facebook Page Administrators, but also includes the general population of Facebook users.

Survey questions for all surveys cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, gender inequity, access to finance, digital technologies, reduction in revenues, business closures, international trade, inflation, reduction of employees and challenges/needs of the business.

Aggregated country level data for each survey wave is available to the public on HDX and controlled access microdata is available to Data for Good at Meta partners. Please visit https://dataforgood.facebook.com/dfg/tools/future-of-business-survey to apply for access to microdata or contact dataforgood@fb.com for any questions.

False positive detections, such as species misidentifications, occur in ecological data, although many models do not account for them. Consequently, these models are expected to generate biased inference. The main challenge in an analysis of data with false positives is to distinguish false positive and false negative processes while modeling realistic levels of heterogeneity in occupancy and detection probabilities without restrictive assumptions about parameter spaces. Building on previous attempts to account for false positive and false negative detections in occupancy models, we present hierarchical Bayesian models that utilize a subset of data with either confirmed detections of a species' presence (CP model) or both confirmed presences and confirmed absences (CACP model). We demonstrate that our models overcome the challenges associated with false positive data by evaluating model performance in Monte Carlo simulations of a variety of scenarios. Our models also have the ability to improve inference by incorporating previous knowledge through informative priors. We describe an example application of the CP model to quantify the relationship between songbird occupancy and residential development, plus we provide instructions for ecologists to use the CACP and CP models in their own research. Monte Carlo simulation results indicated that, when data contained false positive detections, the CACP and CP models generated more accurate and precise posterior probability distributions than a model that assumed data did not have false positive errors. For the scenarios we expect to be most generally applicable, those with heterogeneity in occupancy and detection, the CACP and CP models generated essentially unbiased posterior occupancy probabilities. The CACP model with vague priors generated unbiased posterior distributions for covariate coefficients. The CP model generated unbiased posterior distributions for covariate coefficients with vague or informative priors, depending on the function relating covariates to occupancy probabilities. We conclude that the CACP and CP models generate accurate inference in situations with false positive data for which previous models were not suitable.

The instant messaging (IM) market is experiencing robust growth, projected to reach a market size of $22,550 million in 2025, expanding at a compound annual growth rate (CAGR) of 8.9% from 2019 to 2033. This substantial growth is driven by several factors. The increasing penetration of smartphones and mobile internet access globally fuels the adoption of IM platforms, offering convenient and immediate communication. The rise of enterprise-grade IM solutions enhances collaboration and productivity within businesses, further stimulating demand. Furthermore, the integration of IM with other communication tools and platforms, such as video conferencing and file sharing, creates a seamless and versatile communication ecosystem. The evolving landscape of social media and online communities also plays a significant role, with many IM platforms acting as crucial hubs for social interaction and information dissemination. However, the market also faces challenges. Security concerns related to data privacy and the potential for misuse remain a persistent issue, influencing user trust and platform adoption. Competition amongst numerous established and emerging IM providers is intense, leading to price wars and impacting profitability for some players. Furthermore, regulatory changes concerning data protection and user consent in different regions could impose additional constraints on market growth. Nevertheless, continuous innovation in features, such as enhanced encryption and AI-powered functionalities, coupled with increasing demand for seamless cross-platform communication, is expected to mitigate these challenges and sustain the long-term growth of the instant messaging market. The market is segmented by device type (PC, Mobile) and application (Personal, Enterprise, Other), offering various opportunities for tailored product development and market penetration across geographical regions.

Abstract (en): Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook's scale as well as the relative representativeness of Facebook's user body, these data provide the first comprehensive measure of friendship networks at a national level.

Cardiac MRI is a crucial tool for assessing congenital heart disease (CHD). However, its application remains challenging in young children when performed at 3T. The aim of this retrospective single center study was to compare a non-contrast free-breathing 2D CINE T1-weighted TFE-sequence with compressed sensing (FB 2D CINE CS T1-TFE) with 3D imaging for diagnostic accuracy of CHD, image quality, and vessel diameter measurements in sedated young children. FB 2D CINE CS T1-TFE was compared with a 3D non-contrast whole-heart sequence (3D WH) and 3D contrast-enhanced MR angiography (3D CE-MRA) at 3T in 37 CHD patients (20♂, 1.5±1.4 years). Two radiologists independently assessed image quality, type of CHD, and diagnostic confidence. Diameters and measures of contrast and sharpness of the aorta and pulmonary vessels were determined. A non-parametric multi-factorial approach was used to estimate diagnostic accuracy for the diagnosis of CHD. Linear mixed models were calculated to compare contrast and vessel sharpness. Krippendorff’s alpha was determined to quantify vessel diameter agreement. FB 2D CINE CS T1-TFE was rated superior regarding image quality, diagnostic confidence, and diagnostic sensitivity for both intra- and extracardiac pathologies compared to 3D WH and 3D CE-MRA (all p