Media profiling offers valuable insights to enhance the objectivity and reliability of news coverage by providing comprehensive analysis, but the diversity in languages posed significant challenges to our identification of factuality and political bias of non-English sources. The limitation of existing media analysis research is its concentration on a singular high-resource language, and it hardly extends to languages beyond English. To address this, we introduce xMP, a dataset for zero-shot cross-lingual media profiling tasks. xMP’s cross-lingual test set encompasses 34 non-English languages and 18 language families, extending media profiling beyond English resources and allowing us to assess cross-lingual media profiling model performance. Additionally, we propose a method, named R-KAT, to enhance the model’s zero-shot cross-lingual transfer learning capability by building virtual multilingual embedding. Our experiments illustrate that our method improves the transferability of models in cross-lingual media profiling tasks. Additionally, we further discuss the performance of our method for different target languages. Our dataset and code are publicly available.

Implicit hate speech is difficult to detect because hostile intent is often conveyed through metaphor, irony, coded expressions, stereotypes, or culturally situated allusions rather than direct insults. We first construct a fine-grained Chinese implicit hate speech dataset containing 19,939 samples from Zhihu and Baidu Tieba, covering nine target categories and three expression labels. Based on this dataset, we propose FICHS, a fine-grained and implicit Chinese hate speech detection framework with three synergistic modules built on a RoBERTa-based base detector: an Explicit–Implicit Supervised Contrastive Module that learns a discriminative semantic space between explicit and implicit hate speech, a Semantic Opacity-Aware Routing and Adjudication module that selectively routes low-confidence but high-implicitness samples to an LLM for secondary judgment, and a Pattern-Guided Retrieval-Augmented Recovery Module that retrieves socio-cultural risk patterns to support second-round review and false-negative recovery. Experimental results demonstrate that FICHS effectively improves Chinese implicit hate speech detection, achieving a weighted F1 score of 0.8391, with precision of 0.8428 and recall of 0.8396, outperforming both the SOTA detector and standalone LLM baselines.

Large Language Model (LLM)-driven social simulation offers a controllable approach for analyzing crisis responses, but existing work on online crises often emphasizes either user engagement prediction or opinion and eWOM evolution. This separation is insufficient for online firestorms, where crisis impact emerges from the coupling between what users express and how participation expands over time. In such events, responsibility attribution, KOL influence, electronic Word of Mouth (eWOM), and user engagement jointly shape collective reactions. To address this gap, we introduce the Crisis Response Interaction Simulation Pipeline (CRISP), a training-free LLM-driven multi-agent framework for online firestorm simulation. CRISP integrates an eWOM Perception module for responsibility attribution and attitude formation with an Engagement Mechanism for predicting participation under evolving KOL influence. Experiments on four heterogeneous Weibo online firestorms across beverage, automobile manufacturing, food service, and education domains show that CRISP reproduces major eWOM and engagement trajectories across different activity scales and interaction structures. Counterfactual interventions on emotional composition and responsibility attribution further produce directionally consistent responses, suggesting mechanism-level validity beyond trajectory fitting. These findings indicate that CRISP provides a framework for analyzing online firestorm evolution and evaluating crisis communication strategies in controllable simulation environments.