Papers Stance Detection
“Stance Detection” 태그가 달린 논문 387편 · 필터 해제
Mawqif-XT: An Arabic Benchmark Dataset for Cross-Target Stance Detection
Publicly available Arabic datasets for target-specific stance detection remain limited, particularly for evaluating cross-target generalization. This paper presents the Mawqif-XT, consisting of 996 manually annotated Ara…
Stance DetectionTikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
Political discourse has increasingly moved to short-video platforms, yet computational analysis of such content remains constrained by the scarcity of datasets that jointly preserve audiovisual information and hierarchic…
Stance DetectionPAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection
We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning. We redefine stance as cloze-style masked langua…
Contrastive LearningStance DetectionZero-shot Tweet-Level Stance Detection Enhanced by External Knowledge and Reflective Chain-of-Thought Reasoning
Zero-shot tweet-level stance detection confronts two primary challenges: (1) mitigating the context sparsity inherent in short texts, and (2) establishing the relevance between implicit targets and textual content. While…
Contrastive LearningData AugmentationKnowledge GraphsStance DetectionLLM-Assisted Stance Detection in Scientific Discourse: A Test Case in Bayesian Cognitive Science
Qualitative coding is central to social science, but expert annotation is difficult to scale. LLMs offer a possible extension, yet require careful validation when the target construct is interpretive, theoretically loade…
Stance DetectionA Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We present BioStance, a context-aware data…
Stance DetectionArgument MiningSICI: A Semantic-Pragmatic Complexity Index Reveals Regime Shifts in LLM Stance Detection
Prompt-based LLMs are increasingly used for stance detection, but harder examples are not always repaired by clearer instructions, reasoning prompts, retrieval, or debate. We introduce SICI (Stance Inference Complexity I…
Stance DetectionMulti-Agent Reasoning with Adaptive Worker Allocation for Stance Detection
Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large language models (LLMs) achieve strong per…
Stance DetectionStanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse
We present StanceNakba 2026, a shared task on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict, organized as part of Nakba-NLP 2026 at LREC-COLING 2026. The task introduces…
Stance DetectionContextualized Prompting For Stance Detection On Social Media
Stance detection on social media is challenging due to short, noisy, and context-dependent language. While large language models (LLMs) show zero-shot generalization, they are typically prompted without contextual inform…
Zero-shot GeneralizationStance DetectionStance Detection in Prediction Markets: Addressing Imbalanced Trader Commentary via Counterfactual Augmentation and Market Context
Prediction markets such as Polymarket aggregate crowd beliefs into real-time probability estimates, and the comments traders post beneath each market contain rich directional stance signals that prices alone cannot captu…
Stance DetectionMM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection
Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties w…
Stance DetectionA Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection
Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task, from zero-shot prompting to multi-agent debate. However, existing …
Stance DetectionModeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding
Modeling human cognitive states is essential for advanced artificial intelligence. Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection, and fail to capture int…
Stance DetectionSemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
We present the SemEval-2026 shared task on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which improves traditional ABSA by modeling sentiment along valence-arousal (VA) dimensions rather than using categorical …
Aspect Sentiment Triplet ExtractionSentiment AnalysisStance DetectionContent Fuzzing for Escaping Information Cocoons on Digital Social Media
Information cocoons on social media limit users' exposure to posts with diverse viewpoints. Modern platforms use stance detection as an important signal in recommendation and ranking pipelines, which can route posts prim…
Stance DetectionKUET at StanceNakba Shared Task: StanceMoE: Mixture-of-Experts Architecture for Stance Detection
Actor-level stance detection aims to determine an author expressed position toward specific geopolitical actors mentioned or implicated in a text. Although transformer-based models have achieved relatively good performan…
Stance DetectionWhen Annotators Agree but Labels Disagree: The Projection Problem in Stance Detection
Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral. This convention was inherited from debate analysis and has been applied without modification to social media since SemEval…
Stance DetectionA conceptual framework for ideology beyond the left and right
NLP+CSS work has operationalized ideology almost exclusively on a left/right partisan axis. This approach obscures the fact that people hold interpretations of many different complex and more specific ideologies on issue…
Natural Language InferenceStance DetectionMind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models
Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move global financial markets. A central task is therefore to measure the ha…
Stance Detection