## 4 Experiments
### The Overall Comparison  
Tables 2 and 3 report the Macro F1 scores of various models on the SEM16 and P-Stance datasets across both supervised and zero-shot settings. The results demonstrate that our proposed models, MVSD(GCN) for the supervised setting and MVSD(LLM) for the zero-shot setting, achieve the highest overall performance across both datasets, underscoring the effectiveness of our multi-view stance detection approach. In the supervised setting, MVSD(GCN) significantly outperforms other baselines, achieving average F1 scores of 75.4% on SEM16 and 81.4% on P-Stance. MVSD(GCN)'s success is driven by two key factors. First, it utilizes the LLM-generated HSN, which enriches the graph with additional contextual and relational information, significantly enhancing its ability to capture nuanced stance dependencies. Second, MVSD(GCN) integrates textual features with graph-based representations, effectively linking linguistic cues to structural relationships for a deeper understanding of stance dynamics. In the zero-shot setting, MVSD(LLM) achieves state-of-the-art performance, with average F1 scores of 75.0% on SEM16 and 84.1% on P-Stance, significantly outperforming existing zero-shot models. LLM-based approaches generally outperform traditional \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**SEM16 (\%)**} \\  & & **DT** & **HC** & **FM** & **LA** & **AT** & **CC** & **avg** \\ \hline \multirow{6}{*}{supervised} & BiCond & - & 56.1 & 52.9 & 61.2 & 55.3 & 35.6 & 52.2 \\  & BERT & - & 61.3 & 59.0 & 63.1 & 60.7 & 38.8 & 56.6 \\  & CrossNet & - & 60.2 & 55.7 & 61.3 & 56.4 & 40.1 & 54.7 \\  & ASGCN & - & 61.0 & 58.7 & 63.2 & 59.5 & 40.6 & 56.6 \\  & TPDG & - & 73.4 & 67.3 & 74.7 & 64.7 & 42.3 & 64.5 \\  & MVSD(GCN) & - & **84.7** & **70.3** & **77.1** & **76.7** & **68.1** & **75.4** \\ \hline \multirow{6}{*}{zero-shot} & TOAD & 49.5 & 51.2 & 54.1 & 46.2 & 46.1 & 30.9 & 46.3 \\  & JointCL & 50.5 & 54.8 & 53.8 & 49.5 & 54.5 & 39.7 & 50.5 \\ \cline{1-1}  & GPT3.5-direct & 62.3 & 66.2 & 60.5 & 60.3 & 20.6 & 56.9 & 54.5 \\ \cline{1-1}  & COLA & 68.5 & 81.7 & 63.4 & 71.0 & **70.8** & 65.5 & 70.2 \\ \cline{1-1}  & MB-Cal & 72.8 & 80.3 & 75.8 & 68.8 & 66.5 & 71.0 & 72.5 \\ \cline{1-1}  & MVSD(LLM) & **76.1** & **82.0** & **79.9** & **77.7** & 56.9 & **77.3** & **75.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different models on the SEM16 dataset. The results are reported as Macro F1 scores (%). The best-performing model in each setting is highlighted in bold. Notably, the DT target lacks training data. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**P-Stance (\%)**} \\  & & **[PERSON]** & **[PERSON]** & **[PERSON]** & **avg** \\ \hline \multirow{6}{*}{supervised} & BiCond & 73.0 & 69.4 & 64.6 & 69.0 \\  & BERT & 67.7 & 73.1 & 68.2 & 69.7 \\  & CrossNet & 58.0 & 65.0 & 53.0 & 58.7 \\  & ASGCN & 77.0 & 78.4 & 70.8 & 75.4 \\  & TPDG & 76.8 & 78.1 & 71 & 75.3 \\  & MVSD(GCN) & **83.4** & **86.7** & **74.0** & **81.4** \\ \hline \multirow{6}{*}{zero-shot} & TOAD & 53.0 & 68.4 & 62.9 & 61.4 \\  & JointCL & 62.0 & 59.0 & 73.0 & 64.7 \\ \cline{1-1}  & GPT3.5-direct & 82.1 & 82.0 & 79.0 & 81.0 \\ \cline{1-1}  & COLA & **86.6** & 84.0 & 79.7 & 83.4 \\ \cline{1-1}  & MB-Cal & 85.1 & **85.1** & 81.1 & 83.8 \\ \cline{1-1}  & MVSD(LLM) & 85.8 & 84.6 & **82.0** & **84.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different models on the P-Stance dataset. The best-performing model in each setting is highlighted in bold. learning-based methods due to their superior adaptability to unseen targets. Notably, directly using GPT-3.5 without optimization leads to suboptimal performance, underscoring the critical role of prompt engineering in enhancing model reasoning.