The US economy is growing - so where are all the jobs? When 42-year-old Jacob Trigg lost his job as a project manager in the tech industry he didn't think it would take too long to find a new one - he always had before. But more than 2,000 job applications later he is still hunting, trying to make ends meet with jobs in package delivery and landscaping. It's a huge surprise because I've always been able to get a job very easily, said Trigg, who lives in Texas. It wasn't even on my radar to be prepared for more than six months of unemployment.

'She Has a Presence': The Superfans Who Turned Up for Opening Weekend WIRED attended two documentary screening parties--one on each coast--for the First Lady's film. For decades now, people have been wondering: Who is Melania Trump? The First Lady opens her 2024 memoir with a story about leaving her family in Slovenia to immigrate to America as a 26-year-old model. Ten years later, she became an American citizen. "It was not an easy process," she writes. "And my personal experience dealing with the trials of the immigration process opened my eyes to the difficulties faced by all who wish to become US citizens." OK, but what does that mean, exactly? Her husband, in both his terms as president, put harshly enforcing immigration policy at the center of his domestic agenda. This is all to say that I was authentically excited to see, the documentary that Amazon paid $40 million to acquire and $35 million to market. The director, Brett Ratner, previously accused of sexual misconduct by six different women, is currently in the news thanks to his appearance in a photo included in the most recent dump of Epstein files. What is Melania like behind closed doors?

Large language models (LLMs) are increasingly used to answer high-stakes study-abroad questions about admissions, visas, scholarships, and eligibility. Yet it remains unclear how reliably they advise students, and how often otherwise helpful answers drift into unsupported claims (``hallucinations''). This work provides a clear, domain-grounded overview of how current LLMs behave in this setting. Using realistic questions set drawn from ApplyBoard's advising workflows -- an EdTech platform that supports students from discovery to enrolment -- we evaluate two essentials side by side: accuracy (is the information correct and complete?) and hallucination (does the model add content not supported by the question or domain evidence). These questions are categorized by domain scope which can be a single-domain or multi-domain -- when it must integrate evidence across areas such as admissions, visas, and scholarships. To reflect real advising quality, we grade answers with a simple rubric which is correct, partial, or wrong. The rubric is domain-coverage-aware: an answer can be partial if it addresses only a subset of the required domains, and it can be over-scoped if it introduces extra, unnecessary domains; both patterns are captured in our scoring as under-coverage or reduced relevance/hallucination. We also report measures of faithfulness and answer relevance, alongside an aggregate hallucination score, to capture relevance and usefulness. All models are tested with the same questions for a fair, head-to-head comparison. Our goals are to: (1) give a clear picture of which models are most dependable for study-abroad advising, (2) surface common failure modes -- where answers are incomplete, off-topic, or unsupported, and (3) offer a practical, reusable protocol for auditing LLMs before deployment in education and advising contexts.

As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to align with certain human value systems. Therefore, studying the alignment of LLMs with human values has become a crucial field of inquiry. Prior work, however, mostly focuses on evaluating the alignment of fully trained models, overlooking the training dynamics by which models learn to express human values. In this work, we investigate how and at which stage value alignment arises during the course of a model's post-training. Our analysis disentangles the effects of post-training algorithms and datasets, measuring both the magnitude and time of value drifts during training. Experimenting with Llama-3 and Qwen-3 models of different sizes and popular supervised fine-tuning (SFT) and preference optimization datasets and algorithms, we find that the SFT phase generally establishes a model's values, and subsequent preference optimization rarely re-aligns these values. Furthermore, using a synthetic preference dataset that enables controlled manipulation of values, we find that different preference optimization algorithms lead to different value alignment outcomes, even when preference data is held constant. Our findings provide actionable insights into how values are learned during post-training and help to inform data curation, as well as the selection of models and algorithms for preference optimization to improve model alignment to human values.

The rise of right-wing populism in Europe has brought to the forefront the significance of analysing social media discourse to understand the dissemination of extremist ideologies and their impact on political outcomes. Twitter, as a platform for interaction and mobilisation, provides a unique window into the everyday communication of far-right supporters. In this paper, we propose a methodology that uses state-of-the-art natural language processing techniques with sociological insights to analyse the MIGR-TWIT corpus of far-right tweets in English and French. We aim to uncover patterns of discourse surrounding migration, hate speech, and persuasion techniques employed by right and far-right actors. By integrating linguistic, sociological, and computational approaches, we seek to offer cross-disciplinary insights into societal dynamics and contribute to a better understanding of contemporary challenges posed by right-wing extremism on social media platforms.

As the Netherlands gears up for a snap parliamentary election on October 29, less than halfway through parliament's usual four-year term following the collapse of the ruling coalition, the likelihood of another win for the country's far-right Party for Freedom (PVV) is mounting. An outright win is next to impossible. The Netherlands has always had a coalition government formed by a minimum of two parties due to its proportional representation electoral system, under which seats in parliament are awarded to parties in proportion to the number of votes they win. It then partnered with three other far-right parties - the Farmer-Citizen Movement (BBB), New Social Contract (NSC), and the People's Party for Freedom and Democracy (VVD) - to form a coalition government. But in June, PVV made a dramatic exit from the coalition government over a disagreement on immigration policy.

Abstract: This paper aims to explore two compet ing data science meth odologies to attempt answer ing th e question, " Which issues contributed most to voters' choice in the 2024 presidential election? " The methodologies involve novel empirical evidence driven by artificial intelligence (AI) techniques . By using two distinct methods based on natural language processing and clustering analysis to mine over eight thousand user comments on election - related YouTube videos from one right leaning journal, Wall Street Journal, and one left leaning journal, New York Times, during pre - election week, we quantify the frequency of selected issue areas among user comments to infer which issues were most salient to potential voters in the seven days preceding the November 5th election. Empirically, we primarily demonstrate that immigration and democracy were the most frequently and consistently invoked issues in user comments on the analyzed YouTube videos, followed by the issue of identity politics, while inflation was significantly less frequently referenced. These results corroborate certain findings of post - election surveys but also refute the supposed importance of inflation as an election issue. This indicate s that variations on opinion mining, with their analysis of raw user data online, ca n be more revealing than polling and surveys for analyzing election outcomes. Keywords: artificial intelligence; opinion mining; clustering; vot e choice; cleavages 1. Introduction The Democrats lost both houses of Congress and the Presidency to Republicans in the 2024 election, with former president Donald Trump winning all seven swing states and the national popular vote, despite most pre - election polls giving Vice President Kamala Harris and President Trump a roughly equal chance of winning . Most post - election punditry and analysis in the legacy press and alternative media has attributed the Democrats' large loss to two main issues - inflation [59] and immigration [30] However, a growing contingent of analysts has also attributed the election outcome to the Democratic party's association with cultural issues purportedly distant from the median voter's preferences, such as th ose alternatively aggregated under the concept of "identity" or " woke " politics [54, 56] . To this point, three post - election studies illustrate how voters associated Democrats with left - of - center ideas that were ostensibly distant from most voters' priorities. S urvey research from the think tank Third Way demonstrates that Democrats, and thus Kamala Harris, were largely perceived as "too liberal" [15], while a study from More In Common polling over 5, 000 Americans concluded that while inflation was the top concern for every major demographic group across both parties, Americans misperceived LGBT/transgender policies as the top policy priority for Democrats [37] .

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.

Welcome to the Fox News Politics newsletter, with the latest updates on the Trump administration, Capitol Hill and more Fox News politics content. FIRST ON FOX: The intelligence community did not have any direct information that Russian President Vladimir Putin wanted to help elect Donald Trump during the 2016 presidential election, but, at the "unusual" direction of then-President Barack Obama, published "potentially biased" or "implausible" intelligence suggesting otherwise, the House Intelligence Committee found. Director of National Intelligence Tulsi Gabbard declassified a report prepared by the House Permanent Select Committee on Intelligence back in 2020… READ MORE. Former President Barack Obama nominates John Brennan, to be CIA director during an event in the East Room at the White House on Jan. 7, 2013, in Washington, D.C. (Mark Wilson/Getty Images) 'INSTRUMENTAL': Coast Guard overhaul takes off amid Trump administration's immigration, narcotics crackdown POWER PLAY POLITICS: How China'weaponized' the battery supply chain to control over 80% of the materials needed for batteries in defense tech An airstrike hits a building in the Al-Nasr neighborhood in Gaza City, Gaza on July 21, 2025. REAL'WONDER WOMAN': 'Wonder Woman' actress Gal Gadot praises'strength' of freed Hamas hostages during emotional visit'QUIET PART OUT LOUD': Immigrants needed for'redistricting purposes,' House Dem admits in viral clip: 'Quiet part out loud' CASHFLOW: WATCH: Lawmakers break down how billions in the'big, beautiful bill' boost Trump's immigration crackdown Emil Bove, President Donald Trump's nominee to be U.S. Circuit Judge for the Third Circuit, is sworn in before testifying during his Senate Judiciary Committee nomination hearing in the Hart Senate Office Building on June 25, 2025 in Washington, DC.

Opinion Dynamics (OD) models are a particular case of Agent-Based Models in which the evolution of opinions within a population is studied. In most OD models, opinions evolve as a consequence of interactions between agents, and the opinion fusion rule defines how those opinions are updated. In consequence, despite being simplistic, OD models provide an explainable and interpretable mechanism for understanding the underlying dynamics of opinion evolution. Unfortunately, existing OD models mainly focus on explaining the evolution of (usually synthetic) opinions towards consensus, fragmentation, or polarization, but they usually fail to analyze scenarios of (real-world) highly oscillating opinions. This work overcomes this limitation by studying the ability of several OD models to reproduce highly oscillating dynamics. To this end, we formulate an optimization problem which is further solved using Evolutionary Algorithms, providing both quantitative results on the performance of the optimization and qualitative interpretations on the obtained results. Our experiments on a real-world opinion dataset about immigration from the monthly barometer of the Spanish Sociological Research Center show that the ATBCR, based on both rational and emotional mechanisms of opinion update, is the most accurate OD model for capturing highly oscillating opinions.