Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Is the fall of Pokrovsk, Ukraine's key eastern stronghold, inevitable? Pokrovsk, a key fortress and logistical hub for Ukrainian forces in the eastern region of Donbas, has been under siege for almost two years. But in recent weeks, tens of thousands of Russian soldiers have been storming the town around the clock, taking over the streets where buildings are mostly reduced to bombed-out, deserted ruins. They use reconnaissance drones and satellite images to identify gaps in Ukrainian defences and use tiny groups of soldiers who are attacked and killed in droves by Ukrainian drones .

Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns about their potential to reflect and amplify social biases. We investigate social identity framing in Chinese LLMs using Mandarin-specific prompts across ten representative Chinese LLMs, evaluating responses to ingroup ("We") and outgroup ("They") framings, and extending the setting to 240 social groups salient in the Chinese context. To complement controlled experiments, we further analyze Chinese-language conversations from a corpus of real interactions between users and chatbots. Across models, we observe systematic ingroup-positive and outgroup-negative tendencies, which are not confined to synthetic prompts but also appear in naturalistic dialogue, indicating that bias dynamics might strengthen in real interactions. Our study provides a language-aware evaluation framework for Chinese LLMs, demonstrating that social identity biases documented in English generalize cross-linguistically and intensify in user-facing contexts.

Hostile attribution bias is the tendency to interpret social interactions as intentionally hostile. The Ambiguous Intentions Hostility Questionnaire (AIHQ) is commonly used to measure hostile attribution bias, and includes open-ended questions where participants describe the perceived intentions behind a negative social situation and how they would respond. While these questions provide insights into the contents of hostile attributions, they require time-intensive scoring by human raters. In this study, we assessed whether large language models can automate the scoring of AIHQ open-ended responses. We used a previously collected dataset in which individuals with traumatic brain injury (TBI) and healthy controls (HC) completed the AIHQ and had their open-ended responses rated by trained human raters. We used half of these responses to fine-tune the two models on human-generated ratings, and tested the fine-tuned models on the remaining half of AIHQ responses. Results showed that model-generated ratings aligned with human ratings for both attributions of hostility and aggression responses, with fine-tuned models showing higher alignment. This alignment was consistent across ambiguous, intentional, and accidental scenario types, and replicated previous findings on group differences in attributions of hostility and aggression responses between TBI and HC groups. The fine-tuned models also generalized well to an independent nonclinical dataset. To support broader adoption, we provide an accessible scoring interface that includes both local and cloud-based options. Together, our findings suggest that large language models can streamline AIHQ scoring in both research and clinical contexts, revealing their potential to facilitate psychological assessments across different populations.

Tensions continue to mount as India and Pakistan traded accusations and attacks across their frontier in Kashmir overnight. New Delhi and Islamabad accused one another on Friday of launching drone attacks as well as "numerous ceasefire violations" over the Line of Control (LoC) in the disputed territory. The ongoing hostilities have provoked further calls for restraint as the risk of an escalation between the two nuclear powers grows. Pakistan launched "multiple attacks" using drones and other munitions along India's western border on Thursday night and early Friday, the Indian army said, claiming it had repelled the attacks and responded forcefully, although it did not provide details. Islamabad has denied any cross-border attacks and instead accused Indian forces of sending drones into Pakistani territory, killing at least two civilians.

Telegram has become a major space for political discourse and alternative media. However, its lack of moderation allows misinformation, extremism, and toxicity to spread. While prior research focused on these particular phenomena or topics, these have mostly been examined separately, and a broader understanding of the Telegram ecosystem is still missing. In this work, we fill this gap by conducting a large-scale analysis of the Italian Telegram sphere, leveraging a dataset of 186 million messages from 13,151 chats collected in 2023. Using network analysis, Large Language Models, and toxicity detection tools, we examine how different thematic communities form, align ideologically, and engage in harmful discourse within the Italian cultural context. Results show strong thematic and ideological homophily. We also identify mixed ideological communities where far-left and far-right rhetoric coexist on particular geopolitical issues. Beyond political analysis, we find that toxicity, rather than being isolated in a few extreme chats, appears widely normalized within highly toxic communities. Moreover, we find that Italian discourse primarily targets Black people, Jews, and gay individuals independently of the topic. Finally, we uncover common trend of intra-national hostility, where Italians often attack other Italians, reflecting regional and intra-regional cultural conflicts that can be traced back to old historical divisions. This study provides the first large-scale mapping of the Italian Telegram ecosystem, offering insights into ideological interactions, toxicity, and identity-targets of hate and contributing to research on online toxicity across different cultural and linguistic contexts on Telegram.

Warmongers and authoritarians are "suffocating" human rights across the world, the chief of the United Nations has warned. Speaking at the UN Human Rights Council in Geneva on Monday, Secretary-General Antonio Guterres depicted a world where human rights were "on the ropes and being pummelled hard". Highlighting the devastating effects of conflicts, including in the Middle East, Ukraine and Congo, Guterres noted abuses linked to economics, technology, climate change, migration, and gender. Guterres called out a "morally bankrupt global financial system" that favours profits over planet protections. He also spoke of those who might exploit artificial intelligence to harm people, and leaders who seek to demonise migrants or restrict women's rights.

Abortion has been one of the most divisive issues in the United States. Yet, missing is comprehensive longitudinal evidence on how political divides on abortion are reflected in public discourse over time, on a national scale, and in response to key events before and after the overturn of Roe v Wade. We analyze a corpus of over 3.5M tweets related to abortion over the span of one year (January 2022 to January 2023) from over 1.1M users. We estimate users' ideology and rely on state-of-the-art transformer-based classifiers to identify expressions of hostility and extract five prominent frames surrounding abortion. We use those data to examine (a) how prevalent were expressions of hostility (i.e., anger, toxic speech, insults, obscenities, and hate speech), (b) what frames liberals and conservatives used to articulate their positions on abortion, and (c) the prevalence of hostile expressions in liberals and conservative discussions of these frames. We show that liberals and conservatives largely mirrored each other's use of hostile expressions: as liberals used more hostile rhetoric, so did conservatives, especially in response to key events. In addition, the two groups used distinct frames and discussed them in vastly distinct contexts, suggesting that liberals and conservatives have differing perspectives on abortion. Lastly, frames favored by one side provoked hostile reactions from the other: liberals use more hostile expressions when addressing religion, fetal personhood, and exceptions to abortion bans, whereas conservatives use more hostile language when addressing bodily autonomy and women's health. This signals disrespect and derogation, which may further preclude understanding and exacerbate polarization.

Numerous politicians use social media platforms, particularly X, to engage with their constituents. This interaction allows constituents to pose questions and offer feedback but also exposes politicians to a barrage of hostile responses, especially given the anonymity afforded by social media. They are typically targeted in relation to their governmental role, but the comments also tend to attack their personal identity. This can discredit politicians and reduce public trust in the government. It can also incite anger and disrespect, leading to offline harm and violence. While numerous models exist for detecting hostility in general, they lack the specificity required for political contexts. Furthermore, addressing hostility towards politicians demands tailored approaches due to the distinct language and issues inherent to each country (e.g., Brexit for the UK). To bridge this gap, we construct a dataset of 3,320 English tweets spanning a two-year period manually annotated for hostility towards UK MPs. Our dataset also captures the targeted identity characteristics (race, gender, religion, none) in hostile tweets. We perform linguistic and topical analyses to delve into the unique content of the UK political data. Finally, we evaluate the performance of pre-trained language models and large language models on binary hostility detection and multi-class targeted identity type classification tasks. Our study offers valuable data and insights for future research on the prevalence and nature of politics-related hostility specific to the UK.

An Israeli drone strike has killed one person in southern Lebanon, according to Lebanese health authorities, as Prime Minister Benjamin Netanyahu promised to enforce the ceasefire with Hezbollah "with an iron fist". Lebanon's Ministry of Public Health and state media said Israeli forces carried out several new drone and artillery strikes in southern Lebanon on Tuesday, putting further strain on a tenuous 6-day-old ceasefire with Hezbollah. "An Israeli enemy drone strike on the town of Shebaa killed one person," a Health Ministry statement said. The state-run National News Agency (NNA) described the man who was killed as a "shepherd". The new attacks come as Israeli officials threatened to expand attacks on Lebanon if the ceasefire with the Lebanese armed group Hezbollah collapses.

The Lebanese armed group Hezbollah has broadcast a drone video that it said showed air defence facilities, planes and fuel storage units at Israel's Ramat David airbase, nearly 50km (30 miles) into Israeli territory. The footage included labels pointing out apparent military infrastructure, including the short-range Iron Dome air defence system which is designed to destroy rockets. The video, more than eight minutes long, was mostly filmed on Tuesday, the Iran-aligned group said. The video also included nighttime shots that Hezbollah said were captured "earlier" and other images the group said were taken earlier in July. The caption said it was only "some" of what the drone had captured.