###### Abstract  
Stance detection, the task of identifying the stance expressed in a text toward a specific target, is essential for analyzing public opinion across diverse domains. The existing approaches primarily focus on modeling the semantic relationship between the text and target, but they often struggle when the target is implicit or indirectly referenced. In real-world scenarios, stance is frequently conveyed through references to related entities, events, or contextual implications, making stance detection particularly challenging. To tackle this challenge, we propose a novel framework that leverages large language models to construct a heterogeneous stance network from textual data. Based on this network, we develop two complementary methodologies tailored for distinct application scenarios: (1) In a supervised setting, we employ a graph neural network approach to learn stance representations from the heterogeneous stance network, enhancing stance prediction performance. (2) For zero-shot stance detection, we introduce an LLM-based method that leverages the heterogeneous stance network to infer stance without task-specific supervision. The experimental results on benchmark datasets demonstrate that our methods outperform the existing approaches, highlighting their effectiveness in both supervised and zero-shot scenarios. s +
Footnote †: journal: applied sciences  
article  
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[color=gray!The current stance detection methods can be broadly classified into two categories. The first category consists of supervised learning approaches [4; 5], which train models on labeled data for stance prediction. These methods often leverage deep learning models like BERT [6], which effectively capture the semantic relationships between the text and target. The second category includes zero-shot methods [7; 8], which infer stance on unseen targets without requiring specific labeled training data. Earlier zero-shot approaches, such as TOAD [9] and JointCL [10], relied on contrastive learning, transfer learning, or task-adaptive fine-tuning to generalize to new targets. Recently, large language models (LLMs) have been applied to zero-shot stance detection [11; 12; 13], leveraging in-context learning and prompt engineering to infer stance without task-specific supervision. While these methods have shown promise, most existing approaches assume a direct textual connection between the stance and target [8; 14; 15], which is often not the case in real-world scenarios. Stance is frequently conveyed indirectly through references to intermediate entities, events, or contextual implications. For instance, as illustrated in Figure 1, a user may express a stance toward _[PERSON]_ not by explicitly naming him but through hashtags (e.g., #_MAGA_) or by discussing related entities such as _CNN_. Similarly, social interactions between different users can imply support or opposition toward a target, even if their direct stance is not explicitly stated. This limitation constrains the effectiveness of both supervised and zero-shot approaches. To address this limitation, we propose leveraging the observation that indirect stance expression often involves intermediate entities or contextual references linking the text and target. By identifying these entities and analyzing their stance relationships with both text and target, we construct a heterogeneous stance network that captures implicit stance connections. This network captures indirect stance relationships, providing a structured representation that enhances stance detection. Our objective is to utilize this network to improve stance inference in both supervised and zero-shot settings. In this paper, we propose a novel framework consisting of two key components. First, we construct a heterogeneous stance network using LLMs to extract entities explicitly mentioned in the text and analyze stance relationships among the text, entities, and target. This network encodes implicit stance cues, providing a structured representation that enhances stance inference.