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It relates known fraudsters to other individuals, using record linkage and social network methods. [15] [16] This type of detection is only able to detect frauds similar to those which have occurred previously and been classified by a human. To detect a novel type of fraud may require the use of an unsupervised machine learning algorithm.
In December Facebook and Twitter disabled a global network of 900 pages, groups and accounts sending pro-Trump messages. The fake news accounts managed to avoid detection as being inauthentic, and they used photos generated with the aid of artificial intelligence. The campaign was based in the U.S. and Vietnam.
Fake news website that has published claims about the pilot of Malaysia Airlines Flight 370 reappearing, a billionaire wanting to recruit 1,000 women to bear his children, and an Adam Sandler death hoax. [173] [174] [175] LiveMonitor livemonitor.co.za Fake news website in South Africa, per Africa Check, an IFCN signatory. [133] lockerdome.com
OpenML: [494] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [495] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
In an online essay, activist and historian Thum Ping Tjin denied that fake news was a problem in Singapore, and accused the People's Action Party government as the only major source of fake news, claiming that detentions made without trial during Operation Coldstore and Operation Spectrum were based on fake news for party political gain. [388]
Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. [ 48 ] Computational learning theory can assess learners by computational complexity , by sample complexity (how much data is required), or by other notions of optimization .
For example, in 2017, Google researchers used the term to describe the responses generated by neural machine translation (NMT) models when they are not related to the source text, [17] and in 2018, the term was used in computer vision to describe instances where non-existent objects are erroneously detected because of adversarial attacks.
Another 2019 study in Science found, "fake news accounted for nearly 6% of all news consumption [on Twitter], but it was heavily concentrated—only 1% of users were exposed to 80% of fake news, and 0.1% of users were responsible for sharing 80% of fake news. Interestingly, fake news was most concentrated among conservative voters." [280]