Stance Detection
Stance detection is the task of automatically identifying the attitude (e.g., favor, against, neutral) expressed in text towards a specific target. Current research emphasizes improving the accuracy and generalizability of stance detection models, particularly focusing on multimodal approaches (combining text and images), leveraging large language models (LLMs) for various tasks including zero-shot and few-shot learning, and mitigating biases inherent in these models. This field is significant for understanding public opinion on diverse topics, informing decision-making in areas like marketing and politics, and advancing automated content moderation and misinformation detection.
Papers
STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet B. Pierrehumbert
EDDA: A Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection
Daijun Ding, Li Dong, Zhichao Huang, Guangning Xu, Xu Huang, Bo Liu, Liwen Jing, Bowen Zhang
Multi-modal Stance Detection: New Datasets and Model
Bin Liang, Ang Li, Jingqian Zhao, Lin Gui, Min Yang, Yue Yu, Kam-Fai Wong, Ruifeng Xu
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration
Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu