The people of Venezuela have spent years learning resilience in the face of censorship, disinformation, and repression. They now rely on those tools more than ever. In the early morning hours of Saturday, January 3, the roar of bombs dropping from the sky announced the US military attack on Venezuela, waking the sleeping residents of La Carlota, in Caracas, a neighborhood adjacent to the air base that was a target of Operation Absolute Resolve. Marina G.'s first thought, as the floors, walls, and windows of her second-story apartment shook, was that it was an earthquake. Her cat scrambled and hid for hours, while the neighbors' dogs began to bark incessantly.

In an age characterized by the proliferation of mis- and disinformation online, it is critical to empower readers to understand the content they are reading. Important efforts in this direction rely on manual or automatic fact-checking, which can be challenging for emerging claims with limited information. Such scenarios can be handled by assessing the reliability and the political bias of the source of the claim, i.e., characterizing entire news outlets rather than individual claims or articles. This is an important but understudied research direction. While prior work has looked into linguistic and social contexts, we do not analyze individual articles or information in social media. Instead, we propose a novel methodology that emulates the criteria that professional fact-checkers use to assess the factuality and political bias of an entire outlet. Specifically, we design a variety of prompts based on these criteria and elicit responses from large language models (LLMs), which we aggregate to make predictions. In addition to demonstrating sizable improvements over strong baselines via extensive experiments with multiple LLMs, we provide an in-depth error analysis of the effect of media popularity and region on model performance. Further, we conduct an ablation study to highlight the key components of our dataset that contribute to these improvements. To facilitate future research, we released our dataset and code at https://github.com/mbzuai-nlp/llm-media-profiling.

--This study analyzes Chinese and English media reports on the Paris Olympics using topic modeling, Large Language Model (LLM) prompt engineering, and corpus phraseology methods to explore similarities and differences in discourse construction and attitudinal meanings. Common topics include the opening ceremony, athlete performance, and sponsorship brands. Chinese media focus on specific sports, sports spirit, doping controversies, and new technologies, while English media focus on female athletes, medal wins, and eligibility controversies. Chinese reports show more frequent prepositional co-occurrences and positive semantic prosody in describing the opening ceremony and sports spirit. The Paris Olympics, held from July 26 to August 11, 2024, marked France's return to hosting the Summer Games after a 100-year gap. As a global sporting event and ceremonial medium, the Olympics have significant cultural, political, and economic impact, attracting intense media attention worldwide. Media reports not only document the events but also reflect the cultural perspectives and values of the reporting countries, shaping global perceptions of the Olympic spirit and the host nation.

Affective polarization, or, inter-party hostility, is increasingly recognized as a pervasive issue in democracies worldwide, posing a threat to social cohesion. The digital media ecosystem, now widely accessible and ever-present, has often been implicated in accelerating this phenomenon. However, the precise causal mechanisms responsible for driving affective polarization have been a subject of extensive debate. While the concept of echo chambers, characterized by individuals ensconced within like-minded groups, bereft of counter-attitudinal content, has long been the prevailing hypothesis, accumulating empirical evidence suggests a more nuanced picture. This study aims to contribute to the ongoing debate by employing an agent-based model to illustrate how affective polarization is either fostered or hindered by individual news consumption and dissemination patterns based on ideological alignment. To achieve this, we parameterize three key aspects: (1) The affective asymmetry of individuals' engagement with in-party versus out-party content, (2) The proportion of in-party members within one's social neighborhood, and (3) The degree of partisan bias among the elites within the population. Subsequently, we observe macro-level changes in affective polarization within the population under various conditions stipulated by these parameters. This approach allows us to explore the intricate dynamics of affective polarization within digital environments, shedding light on the interplay between individual behaviors, social networks, and information exposure.

Illinois Attorney General Kwame Raoul spoke to CNN's Jim Acosta on Monday about daytime tv talk show host Dr. Phil joining an ICE deportation operation. Talk show host Dr. Phil called out multiple media outlets for expressing outrage over his ride-along with Immigration and Customs Enforcement (ICE) as it apprehended illegal immigrants. Phil McGraw, known as "Dr. Phil," joined border czar Tom Homan and a team of agents as they took various illegal immigrants into custody in Chicago. As part of his show on Merit TV, Dr. Phil filmed a variety of arrests and even interviewed a convicted sex offender and internet predator from Thailand who was being taken into custody.

Apple Intelligence was launched in the UK last week. Reporters Without Borders, also known as RSF, said it was was "very concerned by the risks posed to media outlets" by AI tools. The group said the BBC incident proves "generative AI services are still too immature to produce reliable information for the public". Vincent Berthier, the head of RSF's technology and journalism desk, added: "AIs are probability machines, and facts can't be decided by a roll of the dice. "RSF calls on Apple to act responsibly by removing this feature.

In this paper, we introduce the MediaSpin dataset aiming to help in the development of models that can detect different forms of media bias present in news headlines, developed through human-supervised and -validated Large Language Model (LLM) labeling of media bias. This corpus comprises 78,910 pairs of news headlines and annotations with explanations of the 13 distinct types of media bias categories assigned. We demonstrate the usefulness of our dataset for automated bias detection in news edits.

Today in the newsletter: X's final form, learnings from a packed week of earnings, and niche online Halloween costumes. Thank you for joining me. With the US election, X's transformation into Elon Musk's weapon reaches its peak. He has succeeded in bending his social network to his will. Last week, Musk deputized his followers to report any "potential instances of voter fraud and irregularities", tweeting about and linking to a forum within X called the "election integrity community".

The group said it targeted the camp in northern Israel using a "swarm of drones". Israeli censorship rules prevent media outlets saying exactly where or what was targeted, but some media outlets say the location was hit by a low-level drone launched from Lebanon - a relatively unsophisticated weapon that appears not to have activated early warning alarms. Footage carried by Israeli media showed those wounded being helped into emergency vehicles, including helicopters. Israeli media reported that 67 people have been injured - with four in a critical condition and five others seriously wounded. Many of the wounded have been evacuated to Hillel Yaffe Medical Centre in nearby Hadera - with others being taken to hospitals in Tel Hashomer, Haifa, Afula and Netanya.

In societies valuing freedom of expression, individuals now frequently express and share their opinions, integrating this practice as a natural part of their routines. Unfortunately, this new social and informational landscape has favored an unprecedented amplification of cyber threats such as hate speech and disinformation, posing significant risks to democratic systems Office of Science and Technology of the Congress of Deputies (Office C) (2023). This situation has intensified and drawn substantial attention from the research community, governmental bodies, and the general public, particularly following extensive disinformation campaigns associated with recent events, including the COVID-19 pandemic Kim and Kesari (2021), the Russia-Ukraine war Pierri et al. (2022), and the Israel-Palestine conflict Aljazeera (2024). Consequently, a structured model encapsulating the key actors, dynamics, and resulting societal impacts is proposed to understand and contextualize the environment being worked on. Figure 1 illustrates our threat model with three main components. In blue, the media and audience as actors in the model, providing the information environment with online news and social network posts that people can read, react to, and comment on. In orange, the content is considered potentially harmful due to intrinsic hateful narratives of today's ecosystem (particularly, public reactions that will be the focus of this research work). In red, the online situation leads to polarization, extremism, and heightened tension, creating a vulnerable environment for society OSMUNDSEN et al. (2021); Cinelli et al. (2021); Pastor-Galindo et al. (2021). In fact, this agitated context serves as a vector for disinformation to become more effective Kim and Kesari (2021).