House Oversight chair James Comer claims Bill Clinton and Hillary Clinton agreed to depositions in the Epstein investigation after facing bipartisan contempt votes that surprised them.

Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care about. Because these stations are trusted news sources, viewers are particularly susceptible to the information they report. The Sinclair Broadcast group is a broadcasting company that has acquired many local news stations in the last decade. We investigate the effects of local news stations being acquired by Sinclair: how does coverage change? We use computational methods to investigate changes in internet content put out by local news stations before and after being acquired by Sinclair and in comparison to national news outlets. We find that there is clear evidence that local news stations report more frequently on national news at the expense of local topics, and that their coverage of polarizing national topics increases.

--Large Language Models (LLMs) have recently emerged as powerful tools for natural language generation, with applications spanning from content creation to social simulations. Their ability to mimic human interactions raises both opportunities and concerns, particularly in the context of politically relevant online discussions. In this study, we evaluate the performance of LLMs in replicating user-generated content within a real-world, divisive scenario: Reddit conversations during the 2016 US Presidential election. In particular, we conduct three different experiments, asking GPT -4 to generate comments by impersonating either real or artificial partisan users. We analyze the generated comments in terms of political alignment, sentiment, and linguistic features, comparing them against real user contributions and benchmarking against a null model. We find that GPT -4 is able to produce realistic comments, both in favor of or against the candidate supported by the community, yet tending to create consensus more easily than dissent. In addition we show that real and artificial comments are well separated in a semantically embedded space, although they are indistinguishable by manual inspection. Our findings provide insights on the potential use of LLMs to sneak into online discussions, influence political debate and shape political narratives, bearing broader implications of AI-driven discourse manipulation. Artificial intelligence (AI) has been the cornerstone of scientific inquiry and technological advancement for several decades, driving innovation in multiple scientific fields [1]. Despite its long-standing presence, AI has captured unprecedented public and academic attention in recent years, largely due to breakthroughs in generative models [2]. Among these, Large Language Models (LLMs) [3] stand out as a transforma-tive innovation, redefining how we approach problems in natural language processing, decision-making, and simulations. In the past two years, the release of powerful models capable of generating coherent and contextually relevant responses (such as GPT -3.5 and GPT -4 [4], Llama [5], Mistral [6] and Gemini [7]) not only captivated the public imagination, but also opened new avenues for research in complex systems [8]-[10]. In particular, LLMs have sparked a lot of interest in complex networks studies and Agent-Based models (ABM). For example, a population of interacting LLMs agents was shown to exhibit preferential attachment [11] and thus creating scale-free networks [12], a characteristic found in many real-world systems [13].

Democratic strategist James Carville said on Wednesday he doesn't buy it when wealthy Jewish donors tell him they're ditching the Democratic Party because of antisemitism among its members. He says they're doing it for a "f------ tax cut." There are two truths about presidential candidates. One: There is no such thing as a perfect candidate. Two: It is very difficult to convince party elites that there are no perfect candidates.

Meta on Tuesday said it had struck an agreement to keep one nuclear reactor of a US utility company in Illinois operating for 20 years. Meta's deal with Constellation Energy is the social networking company's first with a nuclear power plant. Other large tech companies are looking to secure electricity as US power demand rises significantly in part due to the needs of artificial intelligence and datacenters. Google has reached agreements to supply its datacenters with nuclear power via a half-dozen small reactors built by a California utility company. Microsoft's similar contract will restart the Three Mile Island nuclear plant, the site of the most serious nuclear accident and radiation leak in US history.

Large language models have demonstrated remarkable capabilities in natural language processing, yet their application to political discourse analysis remains underexplored. This paper introduces a novel approach to evaluating presidential debate performances using LLMs, addressing the longstanding challenge of objectively assessing debate outcomes. We propose a framework that analyzes candidates' "Policies, Persona, and Perspective" (3P) and how they resonate with the "Interests, Ideologies, and Identity" (3I) of four key audience groups: voters, businesses, donors, and politicians. Our method employs large language models to generate the LLM-POTUS Score, a quantitative measure of debate performance based on the alignment between 3P and 3I. We apply this framework to analyze transcripts from recent U.S. presidential debates, demonstrating its ability to provide nuanced, multi-dimensional assessments of candidate performances. Our results reveal insights into the effectiveness of different debating strategies and their impact on various audience segments. This study not only offers a new tool for political analysis but also explores the potential and limitations of using LLMs as impartial judges in complex social contexts. In addition, this framework provides individual citizens with an independent tool to evaluate presidential debate performances, which enhances democratic engagement and reduces reliance on potentially biased media interpretations and institutional influence, thereby strengthening the foundation of informed civic participation.

Populism is a concept that is often used but notoriously difficult to measure. Common qualitative measurements like holistic grading or content analysis require great amounts of time and labour, making it difficult to quickly scope out which politicians should be classified as populist and which should not, while quantitative methods show mixed results when it comes to classifying populist rhetoric. In this paper, we develop a pipeline to train and validate an automated classification model to estimate the use of populist language. We train models based on sentences that were identified as populist and pluralist in 300 US governors' speeches from 2010 to 2018 and in 45 speeches of presidential candidates in 2016. We find that these models classify most speeches correctly, including 84% of governor speeches and 89% of presidential speeches. These results extend to different time periods (with 92% accuracy on more recent American governors), different amounts of data (with as few as 70 training sentences per category achieving similar results), and when classifying politicians instead of individual speeches. This pipeline is thus an effective tool that can optimise the systematic and swift classification of the use of populist language in politicians' speeches.

Andrew Meieran is about to reopen the doors of one of L.A.'s legendary restaurants in a bid to once again make it an offbeat dining and entertainment destination. Meieran is the proprietor of Clifton's Republic, the kitschy, forest-themed restaurant on Broadway in downtown's Historic Core that for nearly a century served up comfort food such as pot roast, mashed potatoes and Jell-O. The five-story restaurant and bar complex has been closed for the last year after a burst water pipe caused a flood that destroyed the kitchen and collapsed the ceilings on three floors. Clifton's is scheduled to reopen next month after extensive repairs and renovations. Among the changes patrons will find is a basement venue several years in the making that Meieran said is "dedicated to innovation and the magic of experiences" with "entertainment, cocktails and culinary offerings." Andrew Meieran has ambitious vision for Clifton's Cafeteria Meieran is keeping details under wraps for now, but he has demonstrated a knack for creating provocative entertainment and dining venues through an obsessive attention to offbeat details, as well as a willingness to spend more money than most real estate developers to realize his vision and preserve the historic integrity of his projects.

This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.

Former Secretary of State Hillary Clinton took a swipe at voters "upset" by the forthcoming rematch between President Biden and former President Trump during her appearance on "The Tonight Show." Hillary Clinton reacted to a recent ruling in Arizona, which bans abortion in nearly all circumstances, calling it "cruelty" during an interview with Kelly Clarkson and encouraging Americans to vote in a way that would "make life better" for the largest number of people. "I feared it would happen but I hoped it wouldn't happen. Now here we are in the middle of this very difficult period for women in about half the states in our country, who cannot get the care that they need. And the old law in Arizona is without exceptions and the danger to women's lives as well as to our right to make our own decisions about our bodies and ourselves is so profound," Clinton said during the interview with Clarkson on "The Kelly Clarkson Show."