17 citations · 19 across the 9 of their papers we have counts for
9 papers
A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
Hu Huang, Genan Dai, Fuqiang Niu +3
Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We…
Rethinking Fusion: Disentangled Learning of Shared and Modality-Specific Information for Stance Detection
Zhiyu Xie, Fuqiang Niu, Genan Dai +4
Multi-modal stance detection (MSD) aims to determine an author's stance toward a given target using both textual and visual content. While recent methods leverage multi-modal fusio…
Geometry-Aware Spiking Graph Neural Network
Bowen Zhang, Genan Dai, Hu Huang +1
Graph Neural Networks (GNNs) have demonstrated impressive capabilities in modeling graph-structured data, while Spiking Neural Networks (SNNs) offer high energy efficiency through…
MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection
Fuqiang Niu, Genan Dai, Yisha Lu +4
In the realm of contemporary social media, automatic stance detection is pivotal for opinion mining, as it synthesizes and examines user perspectives on contentious topics to uncov…
A Survey of Stance Detection on Social Media: New Directions and Perspectives
Bowen Zhang, Genan Dai, Fuqiang Niu +5
In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these…
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model
Fuqiang Niu, Zebang Cheng, Xianghua Fu +5
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 mu…
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning
Bowen Zhang, Zhichao Huang, Genan Dai +3
Graph classification is a pivotal challenge in machine learning, especially within the realm of graph-based data, given its importance in numerous real-world applications such as s…
More is Better: Deep Domain Adaptation with Multiple Sources
Sicheng Zhao, Hui Chen, Hu Huang +2
In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the l…
Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media
Bowen Zhang, Xianghua Fu, Daijun Ding +5
Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, e…