7 accepted papers
Zero-shot stance detection (ZSSD) seeks to determine the stance of text toward previously unseen targets, a task critical for analyzing dynamic and polarized online discourse with limited labeled data. While large language models (LLMs) offer zero-shot capabilities, prompting-based approaches often
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 fusion and prompt-based learning, most fail to distinguish between modality-specific signals and cross-modal evidence, leadin…
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 social network analysis, recommendation systems, and bioinformatics. Despite its significance, graph classification faces…
Topic evolution and stance dynamics are deeply intertwined in online social media, shaping the fragmentation and polarization of public discourse. Yet existing dynamic topic models and stance analysis approaches usually consider these processes in isolation, relying on abstractions that lack interpr
Human mobility nowcasting is crucial for public safety, especially during disasters when human mobility significantly differs from normal patterns, posing unique challenges. Recent studies have shown a correlation between disaster-related social media information and abnormal patterns in human mobil…
Zero-shot stance detection (ZSSD) is a challenging task that requires classifying stances towards unseen targets without large, well-curated training datasets. Existing data augmentation methods for ZSSD often suffer from semantic inconsistencies, hindering their effectiveness. To address these limi…
Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have at…