\section{Related Work}
Online polarization denotes the division of individuals or communities into opposing groups with distinct ideological positions, values, or identities in online discourse. Prior studies have characterized such polarization through differences at both group and corpus levels. Polarized concepts have been modeled via salience and framing in Reddit communities \citep{hofmann2022salience}, while topic-level polarization has been quantified by associating opposing poles in aligned distributional representations \citep{bianchi2021sweat}. More recently, large language model (LLM)-based simulations have been leveraged to study echo chambers and opinion polarization in social networks \citep{wang2025echochambers}.

In polarized discourse, ideological groups often exhibit divergent evaluations of the same issue or entity. Aspect-based sentiment analysis (ABSA) has been employed to capture stance-dependent affective differences \citep{vorakitphan2020regrexit} and to track sentiment toward multiple geopolitical or ideological entities in large-scale online comments \citep{miehling2025israelhamas}. Beyond this, online polarization has been formulated as a supervised text classification task, where models predict whether a text is polarized and, in more fine-grained settings, classify its associated social dimensions or manifestations. Polarization may also emerge at the annotation level, particularly in toxic language detection. For example, polarized annotations have been modeled using normalized distance from unimodality, enabling their treatment as an additional class and yielding improved classification performance \citep{pavlopoulos2024polarized}.

Despite these advances, multilingual online polarization detection remains challenging, as polarized expressions are shaped by language, culture, and sociopolitical context. The importance of language-specific resources for analyzing political polarization, propaganda techniques, and media bias has been emphasized \citep{szwoch2022polish}. Evidence further indicates that low- and mid-resource languages remain underexplored, and that modeling choices, such as monolingual, multilingual, cross-lingual, or zero-shot LLM settings, can substantially affect performance \citep{davoudi2026persian}. These challenges motivate the development of robust multilingual approaches that mitigate model-specific weaknesses across languages. 

\begin{figure*}[t]
  \centering
\includegraphics[width=\textwidth]{Figure/Fig1-NYCU-NLP-System.png}
  \caption{Our NYCU-NLP system architecture for the SemEval-2026 Task 9.}
  \label{fig:NYCU-NLP_system_architecture}
\end{figure*}


