StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting
2026-07-27 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors created StanceFlip, a new dataset to better understand how people change their opinions during conversations using multiple types of information like text, emotions, and tone. They focus on capturing both the feelings and logical reasons behind opinion changes and include tasks to identify detailed parts of a person's stance and why they flipped their opinion. They also developed ConStaFF, a computer model that mimics thinking by breaking down the reasoning process and checking itself to accurately predict stance changes. Their method works better than previous models at identifying stance details and reasons for opinion flips in conversations.
conversational stance detectionmultimodal modelingstance flippingstance extractionstance attributionlarge language modelmultimodal cuessarcasm detectionopinion dynamicsself-reflective verification
Authors
Heyan Chai, Xin Li, Wenjie Wang, Jianyang Qin, Chaoyang Li, Lu Wang, Hao Chen, Qing Liao
Abstract
Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling. However, existing benchmarks exhibit three key limitations: failure to capture the dynamic evolution of beliefs, particularly during stance reversals; difficulty in disentangling affective states from logical reasoning; and neglect of the critical role of multimodal cues in resolving pragmatic ambiguities such as sarcasm. To address these limitations, we propose StanceFlip, a benchmark designed for multimodal conversational stance flipping forecasting over multi-turn dialogues across five modalities and multi-scenarios, which includes two novel subtasks: 1) Multimodal Stance Sextuple Extraction, extracting holder, target, emotion, sentiment, stance, and rationale as static state snapshots of dialogue to capture fine-grained cognitive structures. 2) Dynamic Stance Flip Attribution, tracking stance reversals across the conversation and identifying their underlying triggers. Alongside the dataset, we propose a dedicated framework, named ConStaFF, for Multimodal Conversational Stance Flipping Forecasting (MCSFF). Built upon a large language model, ConStaFF performs end-to-end stance reasoning, with a Thought-of-Stance (ToS) reasoning framework and a self-reflective verification mechanism integrated for structured stance modeling and faithful flip attribution. Specifically, ToS decomposes the reasoning process into specialized cognitive personas to formulate target propositions, resolve cross-modal conflicts, and infer historical stance trajectories. Extensive experiments show that our approach achieves state-of-the-art performance on both sextuple extraction and flip-trigger attribution, outperforming strong multimodal large language model baselines by substantial margins.