Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse multimodal social media content including text, and images multimodal stance detection (MSD) has become a crucial research area. However, existing MSD studies have focused on modeling stance within individual text-image pairs, overlooking the multi-party conversational contexts that naturally occur on social media. This limitation stems from a lack of datasets that authentically capture such conversational scenarios, hindering progress in conversational MSD. To address this, we introduce a new multimodal multi-turn conversational stance detection dataset (called MmMtCSD). To derive stances from this challenging dataset, we propose a novel multimodal large language model stance detection framework (MLLM-SD), that learns joint stance representations from textual and visual modalities. Experiments on MmMtCSD show state-of-the-art performance of our proposed MLLM-SD approach for multimodal stance detection. We believe that MmMtCSD will contribute to advancing real-world applications of stance detection research.

本研究解决了现有多模态立场检测研究忽视社交媒体多方对话上下文的问题。我们提出了一种新的多模态多轮对话立场检测数据集（MmMtCSD）及其相应的模型（MLLM-SD），该模型能够从文本和视觉模态中学习联合立场表示。实验结果表明，所提出的方法在多模态立场检测上达到了最先进的性能，预示着该研究对于现实应用的潜在影响。

多模态多轮对话立场检测：挑战数据集和有效模型