Clockwise from top left: Shrimp Jesus, Nayib Bukele, Justin Bieber and Super Cat League. Clockwise from top left: Shrimp Jesus, Nayib Bukele, Justin Bieber and Super Cat League. In the algorithm-driven economy of 2025, one man's shrimp Jesus is another man's side hustle. AI slop - the low-quality, surreal content flooding social media platforms, designed to farm views - is a phenomenon, some would say the phenomenon of the 2024 and 2025 internet. Merriam-Webster's word of the year this year is "slop", referring exclusively to the internet variety.

Ramzan Kadyrov vows retaliation against Ukraine following drone attack on Grozny building, while Ukrainian officials say Zelenskyy remains unafraid of threats.

A driver takes his hands off the wheel of an Isuzu self-driving truck during a test run in Mukawa, Hokkaido, on Nov. 18. In the face of a serious shortage of drivers in the logistics industry, Japan's government and commercial vehicle-makers are accelerating experiments aimed at putting self-driving trucks into practical use. They are aiming to attain Level 4 autonomous driving, or driving without human intervention under certain conditions. In addition to carrying out the trials, they will also make efforts to gain the public's understanding for self-driving trucks to ease concerns. Autonomous driving for large vehicles carries a risk of being rejected by the public with a single accident, a senior official of a commercial vehicle-maker said. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (r/Palestine, r/Judaism, r/IsraelPalestine, and r/worldnews). A lexicon-based, unsupervised methodology was developed to measure "extreme opinions" by considering factors such as anger, polarity, and subjectivity. The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events, such as the IDF's bombings of Al Quds Hospital and the Jabalia Refugee Camp, and the end of a ceasefire following a terrorist attack. Additionally, this study explores the distribution and correlation of these scores across different subreddits and over time, providing insights into the propagation of polarised sentiments in response to conflict events. By examining the quantitative effects of each score on extremism and analysing word cloud similarities through Jaccard indices, the research offers a nuanced understanding of the factors driving extreme online opinions. This approach underscores the potential of social media analytics in capturing the complex interplay between real-world events and online discourse, while also highlighting the limitations and challenges of measuring extremism in social media contexts.

Residents and officials from multiple US states are demanding answers about mysterious drone sightings that have been blamed on everything from foreign governments to alien UFOs. Numerous'SUV-sized' craft first appeared in New Jersey in mid-November, and have since spread to New York, Pennsylvania and Connecticut. Drone sightings have also been reported in states such as Texas, Oklahoma and California as well as foreign countries such as Germany. But it's unclear whether these reports are related to the activity plaguing the Northeast. In New Jersey, the drones sometimes appear in groups and often remain in the same place for hours at a time, according to eyewitnesses.

Reports of mysterious drone sightings in New Jersey have now spread to multiple states, as residents and local officials demand answers from the US Government. Numerous'car-sized' drones have been seen hovering throughout the state since mid-November, sometimes appearing in groups and often remaining in the same place for hours at a time. The first drone sightings appeared over the US Army's Picatinny Arsenal and over President-elect Donald Trump's golf course in Bedminster on November 18. But reports of varying levels of credibility have now spread to at least 12 counties throughout the Garden State, as well as eastern Pennsylvania and Orange County, New York. The FBI and other agencies are investigating, but the Department of Homeland Security said Wednesday: 'We have no more information as to where these drones are coming from, where they're launching from, where they're landing.'

Intelligence analysts have revealed why they believe Russia is behind the mysterious drones invading the skies over New Jersey. US Army general Darryl Williams described a situation that mirrors what has unfolded at American/NATO bases across Europe that are known to supply arms to Ukraine. And retired police lieutenant and intelligence analyst Tim McMillan told DailyMail.com Lt McMillan and other experts have noted that the New Jersey sightings circled around Picatinny Arsenal, home of the US Army's CCDC Armaments Center, which is responsible for manufacturing and supplying Ukraine with artillery ammunition. These experts suggest that Russia could be carrying out an intelligence-gathering mission known as'ferreting', meant to intentionally trigger and test their foreign rival's airspace defense procedures and response time.

Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large areas is time-consuming and requires domain expertise. Thanks to their accuracy, generalization capabilities, and highly parallelizable workload, Deep Neural Networks (DNNs) provide an excellent way to automate this task. Nevertheless, there is a scarcity of VHR data pertaining to conflict situations, and consequently, of studies on the effectiveness of DNNs in those scenarios. Motivated by this, our work extensively studies the applicability of a collection of state-of-the-art Convolutional Neural Networks (CNNs) originally developed for natural disasters damage assessment in a war scenario. To this end, we build an annotated dataset with pre- and post-conflict images of the Ukrainian city of Mariupol. We then explore the transferability of the CNN models in both zero-shot and learning scenarios, demonstrating their potential and limitations. To the best of our knowledge, this is the first study to use sub-meter resolution imagery to assess building damage in combat zones.

Defined by Entmann (1993) as a process of selecting and making more salient the specific aspects of social reality, framing is among the most extensively used concepts in the field of communication science (Olsson & Ihlen, 2018). The abundant body of research utilising the concept of framing highlights the versatility of the concept: it has been used for examining the representation of armed conflict (Tschirky & Makhortykh, 2024), climate change (Vu et al., 2021), politics (Ogan et al., 2018), and racial injustice (Lane et al., 2020). The diversity of areas in which the concept of framing is applied and the vagueness of its operationalisation are, however, occasionally viewed as the concept's weakness: Cacciatore et al. (2016) note that it results in the unnecessarily broad understanding of framing that overlaps with other concepts, such as agenda-setting, and diminishes its explanatory potential. Despite the above-mentioned criticism, we suggest that framing remains an essential tool for understanding how certain interpretations of important societal issues become more visible and in which ways individuals are exposed to these interpretations. The importance of such an understanding increases under the conditions of the high-choice media environment (van Aelst et al., 2017) in which we are consuming information. With more available information sources and, consequently, more possibilities for being exposed to them -- both selectively (Messing & Westwood, 2014) and incidentally (Lee & Kim, 2014) -- it is crucial to be able to distinguish between frames coming from these sources, especially regarding the salience of epistemically contested issues which can easily amplify polarisation in the society. The ability to detect the presence or absence of specific frames in this context also becomes paramount for detecting attempts to manipulate public opinion. Another reason why frame detection is highly relevant is the growing reliance on artificial intelligence (AI)-powered systems for organising and generating information regarding societally relevant issues. The adoption of systems such as search engines and recommendations systems and, recently, generative AI-powered chatbots has profound implications for how individuals are exposed to information as these systems decide what information sources and interpretations are prioritised in response to the user input (e.g.

The electric vehicle (EV) battery supply chain's vulnerability to disruptions necessitates advanced predictive analytics. We present SHIELD (Schema-based Hierarchical Induction for EV supply chain Disruption), a system integrating Large Language Models (LLMs) with domain expertise for EV battery supply chain risk assessment. SHIELD combines: (1) LLM-driven schema learning to construct a comprehensive knowledge library, (2) a disruption analysis system utilizing fine-tuned language models for event extraction, multi-dimensional similarity matching for schema matching, and Graph Convolutional Networks (GCNs) with logical constraints for prediction, and (3) an interactive interface for visualizing results and incorporating expert feedback to enhance decision-making. Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods (e.g., GPT-4o) in disruption prediction. These results demonstrate SHIELD's effectiveness in combining LLM capabilities with domain expertise for enhanced supply chain risk assessment.