Stance detection aims to identify the attitude expressed in text towards a given target, with applications in public opinion analysis and misinformation mitigation.Despite recent advances in large language models (LLMs), two key challenges remain: (1) spurious correlations between superficial features and stance labels, and ( 2) lack of cognitive modeling that simulates the transition from intuitive perception to deliberate reasoning.To address these issues, we propose Cognitive-Driven Stance Detection (CDSD), inspired by Kahneman's Dual-Process Theory.CDSD integrates fast intuitive judgment (System 1) and analytical reasoning (System 2), enhanced by three key modules: attention-based cognitive alignment to compare system focus, uncertainty-aware belief update using Bayesian inference, and self-doubt-triggered counterfactual reasoning for re-evaluation under low consistency or high uncertainty.Experimental results on SEM16, P-Stance, and VAST show that CDSD outperforms state-of-the-art methods across multiple LLMs.Notably, CDSD exhibits strong robustness against textual perturbations such as emotional word removal and rhetorical restructuring.By integrating cognitive theory with NLP, our work provides a promising path toward more reliable and interpretable stance detection systems.

Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive reports. In contrast to existing studies that mainly focus on unstructured web content, a more challenging DR task should additionally utilize structured knowledge to provide a solid data foundation, facilitate quantitative computation, and lead to in-depth analyses. In this paper, we refer to this novel task as Knowledgeable Deep Research (KDR), which requires DR agents to generate reports with both structured and unstructured knowledge. Furthermore, we propose the Hybrid Knowledge Analysis framework (HKA), a multi-agent architecture that reasons over both kinds of knowledge and integrates the texts, figures, and tables into coherent multimodal reports. The key design is the Structured Knowledge Analyzer, which utilizes both coding and vision-language models to produce figures, tables, and corresponding insights. To support systematic evaluation, we construct KDR-Bench, which covers 9 domains, includes 41 expert-level questions, and incorporates a large number of structured knowledge resources (e.g., 1,252 tables). We further annotate the main conclusions and key points for each question and propose three categories of evaluation metrics including general-purpose, knowledge-centric, and vision-enhanced ones. Experimental results demonstrate that HKA consistently outperforms most existing DR agents on general-purpose and knowledge-centric metrics, and even surpasses the Gemini DR agent on vision-enhanced metrics, highlighting its effectiveness in deep, structure-aware knowledge analysis. Finally, we hope this work can serve as a new foundation for structured knowledge analysis in DR agents and facilitate future multimodal DR studies.

Zero-shot stance detection (ZSSD) aims to classify stances towards previously unseen targets without direct supervision on those topics during training. While recent approaches have explored various strategies, they often suffer from limited linguistic diversity or unstable semantic representations, restricting generalization to new domains. To address these challenges, we propose DPSD, a dynamic framework that integrates LLM-assisted data augmentation, multi-granularity feature fusion with contrastive learning, and adaptive prototype updating. Our method enriches the training data with both diverse targets and stylistically varied texts. A gate-controlled fusion mechanism combines deep contextualized features from BERT with shallow lexical patterns via TF-IDF, while contrastive learning refines the feature space by pulling similar instances closer and pushing dissimilar ones apart, thereby improving representation discriminability. Furthermore, we introduce a sliding-window prototype pool that dynamically maintains class-specific prototypes while preserving historical knowledge, ensuring stable and interpretable inference over time. We also incorporate LLM-calibrated semantic similarity as an auxiliary scorer for controlled reasoning. Experimental results on three benchmark datasets -SEM16, P-Stance, and VAST - show that DPSD achieves strong performance in various zero-shot settings, especially in cross-dataset and unseen target scenarios.

Stance detection is a pivotal task in Natural Language Processing (NLP), identifying textual attitudes toward various targets.Despite advances in using Large Language Models (LLMs), challenges persist due to hallucinationmodels generating plausible yet inaccurate content.Addressing these challenges, we introduce MPVStance, a framework that incorporates Multi-Perspective Verification (MPV) with Retrieval-Augmented Generation (RAG) across a structured five-step verification process.Our method enhances stance detection by rigorously validating each response from factual accuracy, logical consistency, contextual relevance, and other perspectives.Extensive testing on the SemEval-2016 and VAST datasets, including scenarios that challenge existing methods and comprehensive ablation studies, demonstrates that MPVStance significantly outperforms current models.It effectively mitigates hallucination issues and sets new benchmarks for reliability and accuracy in stance detection, particularly in zero-shot, few-shot, and challenging scenarios.

Multimodal Stance Detection (MSD) aims to determine a user's stance -support, oppose, or neutral -toward a target by analyzing multimodal content such as texts and images from social media.Existing MSD methods struggle with generalizing to unseen targets and handling modality inconsistencies.To address these challenges, we propose the Targetdriven Multi-modal Alignment and Dynamic Weighting Model (T-MAD), which combines target-driven multi-modal alignment and dynamic weighting mechanisms to capture targetspecific relationships and balance modality contributions.The model incorporates iterative reasoning to iteratively refine predictions, achieving robust performance in both in-target and zero-shot settings.Experiments on the MMSD and MultiClimate datasets show that T-MAD outperforms state-of-the-art models, with optimal results achieved using RoBERTa, ViT, and an iterative depth of 5. Ablation studies further confirm the importance of multi-modal alignment and dynamic weighting in enhancing model effectiveness.

Stance detection, a critical task in Natural Language Processing (NLP), aims to identify the attitude expressed in text toward specific targets.Despite advancements in Large Language Models (LLMs), challenges such as limited interpretability and handling nuanced content persist.To address these issues, we propose the Multi-Path Reasoning Framework (MPRF), a novel framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection.Unlike prior work that relies on single-path reasoning or static explanations, MPRF introduces a structured end-to-end pipeline: it first generates diverse reasoning paths through predefined perspectives, then dynamically evaluates and optimizes each path using LLM-based scoring, and finally fuses the results via weighted aggregation to produce interpretable and reliable predictions.Extensive experiments on the SEM16, VAST, and PStance datasets demonstrate that MPRF outperforms existing models.Ablation studies further validate the critical role of MPRF's components, highlighting its effectiveness in enhancing interpretability and handling complex stance detection tasks.

Social media, e.g. Weblog and Internet forum, generate rich historical textual datasets which record lots of valuable events. Automatic event detection tries to discover important and interesting events and their related documents. Existing solutions to event detection, however, are mostly proposed for high quality news stories and may not work well when they are applied to noisy social media datasets, where content quality varies drastically from informative to trivial or even spamming. In this paper, an event detection framework, which directly utilizes burst property of events to filter out noise, is proposed. Experimental results on real dataset from Tencent Internet forum, a popular forum in China, demonstrate the effectiveness of the proposed framework.

Update summarization aims to create a summary over a topic-related multi-document dataset based on the assumption that the user has already read a set of earlier documents of the same topic. Beyond the problems (i.e., topic relevance, salience, and diversity in extracted information) tackled by topic-focused multi-document summarization, the update summarization must address the novelty problem as well. In this paper, we propose a novel extractive approach based on manifold ranking with sink points for update summarization. Specifically, our approach leverages a manifold ranking process over the sentence manifold to find topic relevant and salient sentences. More important, by introducing the sink points into sentence manifold, the ranking process can further capture the novelty and diversity based on the intrinsic sentence manifold. Therefore, we are able to address the four challenging problems above for update summarization in a unified way. Experiments on benchmarks of TAC are performed and the evaluation results show that our approach can achieve comparative performance to the existing best performing systems in TAC tasks.

This paper addresses the problem of query classification (QC), which aims to classify Web search queries into one or more predefined categories. The state-of-the-art solution for QC is to employ a bridging classifier via an intermediate taxonomy. In this paper, we advanced the bridging method by leveraging probabilistic topic models. The topic model, referred as RCTM (Regularized Correlated Topic Model), is an extension of the conventional CTM (Correlated Topic Model). RCTM learns a topic model by leveraging weak supervision from existing annotated data rather than in an unsupervised fashion, and thus it can effectively address the problem in topic modeling while the topics are predefined. The experimental evaluations show that our QC approach outperforms other baseline methods.

Topic representation mismatch is a key problem in topic-oriented summarization for the specified topic is usually too short to understand/interpret. This paper proposes a novel adaptive model for summarization, AdaSum, under the assumption that the summary and the topic representation can be mutually boosted. AdaSum aims to simultaneously optimize the topic representation and extract effective summaries. This model employs a mutual boosting process to minimize the topic representation mismatch for base summarizers. Furthermore, a linear combination of base summarizers is proposed to further reduce the topic representation mismatch from the diversity of base summarizers with a general learning framework. We prove that the training process of AdaSum can enhance the performance measure used. Experimental results on DUC 2007 dataset show that AdaSum significantly outperforms the baseline methods for summarization (e.g. MRP, LexRank, and GSPS).