## 5 Conclusions  
In this work, we propose MVSD, a multi-view stance detection framework that effectively integrates textual and structural features for both supervised and zero-shot settings. By leveraging heterogeneous stance networks with MVSD(GCN) and MVSD(LLM), our approach consistently outperforms the existing baselines across various datasets. The experiments on SEM16 and P-Stance demonstrate that MVSD(GCN) excels when labeled data are available, while MVSD(LLM) showcases strong generalization capabilities in zero-shot scenarios. Additionally, an ablation study further emphasizes the critical roles of relational graph modeling, multi-view representations, and attention mechanisms in improving performance.  
Despite its strengths, our framework has several limitations. First, MVSD(GCN) relies on graph structure quality, which may degrade if noisy or incomplete stance relations are present. Second, while MVSD(LLM) reduces dependency on labeled data, its zero-shot performance is still constrained by the LLM's pre-trained knowledge and prompt design. Third, our current approach focuses on static stance detection, leaving dynamic stance shifts (e.g., temporal evolution in debates) for future exploration.  
Our work highlights the value of combining structural (HSN) and semantic (text) views for stance detection, offering a more robust solution than single-view methods. The success of MVSD(LLM) in zero-shot settings suggests that LLMs, when guided by relational graphs, can reduce annotation costs while maintaining competitive accuracy, a promising direction for low-resource scenarios. Moreover, the framework's modular design allows extensions to other graph-aware NLP tasks, such as rumor detection or opinion mining.  
For future work, we aim to enhance our approach by (1) generating higher-quality heterogeneous stance networks to capture richer relational information, (2) incorporating robust feature fusion methods, and (3) refining task-specific prompts for LLMs to improve zero-shot performance. Furthermore, we plan to explore how this architecture can support deeper interpretive analysis of ideological discourse online, such as uncovering latent bias patterns or tracing the evolution of narratives across communities. These directions will help to push the boundaries of stance detection and increase its real-world applicability while bridging the gap between predictive modeling and sociolinguistic insights.  
Writing--original draft preparation, X.C.; supervision, B.L. and H.H.; writing--review and editing, Y.C. and M.G.; resources, X.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available at \"[[https://github.com/chuchun8/PStance](https://github.com/chuchun8/PStance)]([https://github.com/chuchun8/PStance](https://github.com/chuchun8/PStance))\" (P-Stance) (accessed on 1 May 2024) and \"[[https://alt.qcri.org/semeval2016/task6/](https://alt.qcri.org/semeval2016/task6/)]([https://alt.qcri.org/semeval2016/task6/](https://alt.qcri.org/semeval2016/task6/))\" (SEM16) (accessed on 1 May 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NLP & Natural Language Processing \\ BERT & Bi-directional Encoder Representations from Transformers \\ LLM & Large Language Model \\ \end{tabular}