Automatic Authorship Attribution (AAA) is the result of applying tools and techniques from Digital Humanities to authorship attribution studies. Through a quantitative and statistical approach this discipline can draw further conclusions about renowned authorship issues which traditional critics have been dealing with for centuries, opening a new door to style comparison. The aim of this paper is to prove the potential of these tools and techniques by testing the authorship of five comedies traditionally attributed to Spanish playwright Tirso de Molina (1579-1648): La ninfa del cielo, El burlador de Sevilla, Tan largo me lo fiais, La mujer por fuerza and El condenado por desconfiado. To accomplish this purpose some experiments concerning clustering analysis by Stylo package from R and four distance measures are carried out on a corpus built with plays by Tirso, Andres de Claramonte (c. 1560-1626), Antonio Mira de Amescua (1577-1644) and Luis Velez de Guevara (1579-1644). The results obtained point to the denial of all the attributions to Tirso except for the case of La mujer por fuerza.

Social media users may trust artificial intelligence (AI) as much as human editors to flag hate speech and harmful content, according to researchers at Penn State. The researchers said that when users think about positive attributes of machines, like their accuracy and objectivity, they show more faith in AI. However, if users are reminded about the inability of machines to make subjective decisions, their trust is lower. The findings may help developers design better AI-powered content curation systems that can handle the large amounts of information currently being generated while avoiding the perception that the material has been censored, or inaccurately classified, said S. Shyam Sundar, James P. Jimirro Professor of Media Effects in the Donald P. Bellisario College of Communications and co-director of the Media Effects Research Laboratory. "There's this dire need for content moderation on social media and more generally, online media," said Sundar, who is also an affiliate of Penn State's Institute for Computational and Data Sciences.

Clickbait might not lure readers as before – and using artificial intelligence to detect fake news might be much more complex than previously thought, a team of researchers suggest. Clickbait headlines might not be as enticing to readers as once thought, according to a team of researchers. They added that artificial intelligence – AI – may also come up short when it comes to correctly determining whether a headline is clickbait. In a series of studies, the researchers found that clickbait – headlines that often rely on linguistic gimmicks to tempt readers to read further – often did not perform any better and, in some cases, performed worse than traditional headlines. Because fake news is a concern on social media, researchers have explored using AI to systematically identify and block clickbait.

In April 2018, the Nobel Prize Inspiration Initiative and 3M hosted the lecture, Climate Change: Science and Policy with Dr. Mario Molina. Molina won the Nobel Prize for Chemistry for his scientific discovery of the chemistry of the stratospheric ozone layer and its susceptibility to human-made activities. He co-authored research in 1974 in Nature magazine on the threat to the ozone layer from chlorofluorocarbon (CFC) gasses being used in spray cans. Molina has also served on the United States President's Council of Advisors on Science and Technology from 1994 to 2000 and again in 2010-2016. "Science doesn't tell you what to do. Science isn't either good or bad so you can not give Nobel prizes in science to good people, you do that in principle for the science," said Molina.