The new AI-powered Wikipedia competitor falsely claims that pornography worsened the AIDS epidemic and that social media may be fueling a rise in transgender people. On Monday, Elon Musk's xAI startup launched Grokipedia, which the billionaire is pitching as an AI-generated alternative to the crowdsourced encyclopedia Wikipedia. Musk first announced the project in late September on his social media platform X, saying it would be "a massive improvement over Wikipedia," and "a necessary step towards the xAI goal of understanding the Universe." Musk said last week that he had delayed the launch of Grokipedia because his team needed "to do more work to purge out the propaganda." When Grokipedia eventually dropped on Monday, WIRED was initially unable to access the website and received an automated message that it was blocked.

Millions of people have watched Mike Hughes die. It happened on February 22, 2020, not far from Highway 247 near the Mojave Desert city of Barstow, California. A homemade rocket ship with Hughes strapped in it took off from a launching pad mounted on a truck. A trail of steam billowed behind the rocket as it swerved and then shot upward, a detached parachute unfurling ominously in its wake. In a video recorded by the journalist Justin Chapman, Hughes disappears into the sky, a dark pinpoint in a vast, uncaring blueness.

Understanding how online media frame issues is crucial due to their impact on public opinion. Research on framing using natural language processing techniques mainly focuses on specific content features in messages and neglects their narrative elements. Also, the distinction between framing in different sources remains an understudied problem. We address those issues and investigate how the framing of health-related topics, such as COVID-19 and other diseases, differs between conspiracy and mainstream websites. We incorporate narrative information into the framing analysis by introducing a novel frame extraction approach based on semantic graphs. We find that health-related narratives in conspiracy media are predominantly framed in terms of beliefs, while mainstream media tend to present them in terms of science. We hope our work offers new ways for a more nuanced frame analysis.

Although you'd never guess it from mainstream media, the most significant antitrust case in more than 20 years is under way in Washington. In it, the US justice department, alongside the attorneys general of eight states, is suing Google for abusively monopolising digital advertising technologies, thereby subverting competition through "serial acquisitions" and anti-competitive auction manipulation. Or, to put it more prosaically, arguing that Google – which has between 90% and 95% of the search market – has maintained its monopoly not by making a better product, but by locking down almost every avenue through which consumers might find a different search engine and making sure they only see Google wherever they look. Basically, because the US government has been asleep at the wheel for almost a quarter of a century and has finally woken up to its democratic responsibilities. The last time it stirred itself to take on an aggressive monopolist was in 2001, when it sued Microsoft for illegally tying its Internet Explorer browser to Windows as part of a (successful) campaign to destroy Netscape, maker of the first distinctive commercial web browser, which Bill Gates and co perceived as a potentially lethal competitive threat.

The exponential growth in user acquisition and popularity of OpenAIs ChatGPT, an artificial intelligence(AI) powered chatbot, was accompanied by widespread mainstream media coverage. This article presents a quantitative data analysis of the early trends and sentiments revealed by conducting text mining and NLP methods onto a corpus of 10,902 mainstream news headlines related to the subject of ChatGPT and artificial intelligence, from the launch of ChatGPT in November 2022 to March 2023. The findings revealed in sentiment analysis, ChatGPT and artificial intelligence, were perceived more positively than negatively in the mainstream media. In regards to word frequency results, over sixty-five percent of the top frequency words were focused on Big Tech issues and actors while topics such as jobs, diversity, ethics, copyright, gender and women were poorly represented or completely absent and only accounted for six percent of the total corpus. This article is a critical analysis into the power structures and collusions between Big Tech and Big Media in their hegemonic exclusion of diversity and job challenges from mainstream media.

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' Billionaire Elon Musk took another swing at artificial intelligence service ChatGPT and the mainstream media on Thursday with a viral meme that accumulated over 254,000 likes on Twitter. Musk has emerged as a major critic of ChatGPT amid accusations that the artificial intelligence (AI) bot engages in liberal bias. The Tesla CEO and owner of Twitter shared a meme with the caption, "ChatGPT to the mainstream media." "Look at me," the meme read.

In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus. The challenge focused on the automatic detection of all cause-effect-signal spans present in the sentence from news-media. We detect cause-effect-signal spans in a sentence using T5 -- a pre-trained autoregressive language model. We iteratively identify all cause-effect-signal span triplets, always conditioning the prediction of the next triplet on the previously predicted ones. To predict the triplet itself, we consider different causal relationships such as cause$\rightarrow$effect$\rightarrow$signal. Each triplet component is generated via a language model conditioned on the sentence, the previous parts of the current triplet, and previously predicted triplets. Despite training on an extremely small dataset of 160 samples, our approach achieved competitive performance, being placed second in the competition. Furthermore, we show that assuming either cause$\rightarrow$effect or effect$\rightarrow$cause order achieves similar results.

Fox News contributor Joe Concha weighs in on the mainstream media's coverage of election celebrations vs. Trump rallies on'America's Newsroom.' Washington Post media columnist Margaret Sullivan suggested that her journalist peers "never fully learned how to cover" President Trump but "might have saved democracy" following his projected defeat against Joe Biden. "Over the past four or five years, I've been sharply critical of the media, including that subset I like to call the'reality-based press,'" Sullivan wrote on Sunday. "My continuing complaint has been that mainstream journalism never quite figured out how to cover President Trump, the master of distraction and insult who craved media attention and knew exactly how to get it, regardless of what it meant for the good of the nation." Sullivan indicated that the press was too obedient of the "deeply abnormal president," writing "When he said'jump,' journalists all too often said'how high?'" and that the media "constantly sought to normalize him, treating his deranged tweets like legitimate news and piously forecasting, every time he sounded the least bit calm, that he was becoming'presidential.'"

When ranking big data observations such as colleges in the United States, diverse consumers reveal heterogeneous preferences. The objective of this paper is to sort out a linear ordering for these observations and to recommend strategies to improve their relative positions in the ranking. A properly sorted solution could help consumers make the right choices, and governments make wise policy decisions. Previous researchers have applied exogenous weighting or multivariate regression approaches to sort big data objects, ignoring their variety and variability. By recognizing the diversity and heterogeneity among both the observations and the consumers, we instead apply endogenous weighting to these contradictory revealed preferences. The outcome is a consistent steady-state solution to the counterbalance equilibrium within these contradictions. The solution takes into consideration the spillover effects of multiple-step interactions among the observations. When information from data is efficiently revealed in preferences, the revealed preferences greatly reduce the volume of the required data in the sorting process. The employed approach can be applied in many other areas, such as sports team ranking, academic journal ranking, voting, and real effective exchange rates.

Fox Nation host Lara Logan and The Hill's Joe Concha break down the coverage. Fox Nation host Lara Logan said Thursday that the American media is largely failing to cover the swath of the Iranian population that is celebrating the death of terror general Qassem Soleimani in an American drone attack last week. Logan appeared on "Hannity" to discuss some media outlets' decision to highlight large public gatherings of mourning throughout Iran. Meanwhile, some hosts have characterized Soleimani as an icon of Iran. "I mean, he was a terrorist," Logan said.