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.
@article{arxiv.2409.00597,
title = {Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model},
author = {Fuqiang Niu and Zebang Cheng and Xianghua Fu and Xiaojiang Peng and Genan Dai and Yin Chen and Hu Huang and Bowen Zhang},
journal= {arXiv preprint arXiv:2409.00597},
year = {2024}
}