Founder and Hedgehog CEO John Matze joined'FOX & Friends First' to discuss his optimism surrounding the community notes program, staying competitive globally with AI and the possibility of Oracle buying TikTok. Reviewing your Facebook data allows you to see what personal information Facebook has collected about you, helping you make informed decisions about your privacy settings. You might also need a copy of your data, which serves as a backup of your photos, messages and memories in case you lose access to your account or decide to delete it. Additionally, understanding what data Facebook stores can help you better comprehend how the platform uses your information for advertising and content personalization. Here's how to do it.

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Method: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusions: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

Meta has been hit with a €265 million ($277 million) fine for failing to prevent millions of Facebook users' mobile phone numbers and other data from being scraped and dumped online, Independent.ie It's the second fine levied by the Irish Data Protection Commission (DPC) in just the past few months, following a €405 million ($402 million at the time) penalty issued in September. In just the last 18 months, Meta has tallied nearly €1 billion in fines. The penalty was issued in response to the leak of 533 million Facebook users' data reported in April last year. That included phone numbers, birth dates, email addresses and locations, information that could be exploited in phishing and other attacks.

In the process of numerically modeling natural languages, developing language embeddings is a vital step. However, it is challenging to develop functional embeddings for resource-poor languages such as Sinhala, for which sufficiently large corpora, effective language parsers, and any other required resources are difficult to find. In such conditions, the exploitation of existing models to come up with an efficacious embedding methodology to numerically represent text could be quite fruitful. This paper explores the effectivity of several one-tiered and two-tiered embedding architectures in representing Sinhala text in the sentiment analysis domain. With our findings, the two-tiered embedding architecture where the lower-tier consists of a word embedding and the upper-tier consists of a sentence embedding has been proven to perform better than one-tier word embeddings, by achieving a maximum F1 score of 88.04% in contrast to the 83.76% achieved by word embedding models. Furthermore, embeddings in the hyperbolic space are also developed and compared with Euclidean embeddings in terms of performance. A sentiment data set consisting of Facebook posts and associated reactions have been used for this research. To effectively compare the performance of different embedding systems, the same deep neural network structure has been trained on sentiment data with each of the embedding systems used to encode the text associated.

Paris – An analysis of 21 billion Facebook friendships shows that children from poorer homes are likely to earn more later in life if they grow up in areas where they can become friends with wealthier children. It has long been believed that having rich friends can help children rise up out of poverty, but previous research has had small sample sizes or limited data, according to two studies published in the journal Nature on Monday. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page. We humbly apologize for the inconvenience.

The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To achieve this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04%. Further, since all the deep learning models show F1 scores above 75% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.

The Financial Times reports the Irish Data Protection Commission has fined WhatsApp €225 million ($266.8 million) for not sharing enough details of how it shares European Union users' data with Facebook. The messaging service allegedly failed to live up to its General Data Protection Regulation (GDPR) transparency obligations. The Commission also said the data sharing itself violated GDPR. WhatsApp was merely storing "pseudonymous" phone number data, for instance, rather than truly anonymizing it. While the numbers were stored using lossy hashes, WhatsApp had the hash key needed to decrypt that info -- it could tie that number to a specific person if it wanted. The ruling asked WhatsApp to both improve its transparency and bring the data sharing in line with the GDPR.

The Massachusetts College of Art and Design is holding a virtual auction to help support student scholarships. The silent auction portion of the fundraising event ends at noon on April 11, while a live auction takes place online on the evening of April 10. The 32nd annual MassArt Auction is the second conducted online due to the pandemic. Artists who have had their works juried into the auction donate either 50% or 100% of the sale price to support MassArt scholarships. The two auctions will feature over 300 works from MassArt students, graduates, members of the faculty and others.

It's easy to do bad things with Facebook data. From targeting ads for bizarrely specific T-shirts to manipulating an electorate, the questionable purposes to which the social media behemoth can be put are numerous. But there are also some people out there trying to use Facebook for good--or, at least, to improve the diagnosis of mental illness. On December 3, a group of researchers reported that they had managed to predict psychiatric diagnoses with Facebook data--using messages sent up to 18 months before a user received an official diagnosis. The team worked with 223 volunteers, who all gave the researchers access to their personal Facebook messages.

A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.