\section{Introduction}
Online polarization, defined as the sharp division and hostility among social, political, or identity-based groups \cite{waller2021quantifying}, has attracted increasing attention due to its potential to escalate into hate speech, offensive language, harassment, real-world violence, and broader social fragmentation \cite{piazza2023political,martinez2024methodology}. Consequently, the identification of polarized opinions has become a critical task for determining whether a given text exhibits explicit attitudinal polarization, thereby contributing to the promotion of a healthier online environment.

SemEval-2026 Task 9 \cite{naseem-etal-2026-polar} focuses on analyzing how polarization is manifested in textual discourse across different languages, cultures, and socially significant contexts. The shared task comprises three subtasks: 1) \textbf{Subtask 1: Polarization Detection}, which determines whether a given text expresses polarization; 2) \textbf{Subtask 2: Polarization Type Classification}, which identifies the underlying social dimension(s) of polarization, including political, racial/ethnic, religious, gender/sexual, and other categories; and 3) \textbf{Subtask 3: Polarization Manifestation Identification}, which detects rhetorical manifestation(s) of polarization, such as stereotyping, vilification, dehumanization, extreme language, lack of empathy, and invalidation. While Subtask 1 is formulated as a binary classification problem, Subtasks 2 and 3 are designed as multi-label classification tasks, reflecting the possibility that multiple forms of polarization may co-occur within a single text. The datasets cover 22 languages and are provided by the task organizers. Participating teams may choose to compete in one or more languages and subtasks according to their preference.

This paper presents the \textbf{NYCU-NLP} (\textbf{N}ational \textbf{Y}ang Ming \textbf{C}hiao Tung \textbf{U}niversity, \textbf{N}atural \textbf{L}anguage \textbf{P}rocessing Lab) system for the SemEval-2026 Task 9. Our approach leverages instruction-tuned Small Language Models (SLMs), including Phi-4 (14B) \cite{abdin2024-Phi4}, Mistral-small-3.2 (24B), and Gemma-3 (27B) \cite{Kamath-Gemma3}, to identify and classify polarized content. We further integrate these three fine-tuned models using a stacking ensemble \cite{stacking-ensemble} as our primary system architecture, aiming to achieve stable and robust performance across languages and subtasks. Experimental results on the official test set, averaged across all languages, demonstrate that our stacking-based SLM ensemble achieves macro-averaged F1 scores of 0.8071 for Subtask 1, 0.6108 for Subtask 2, and 0.5111 for Subtask 3. Our system ranked first in 15, second in 12, and third in 10 out of 62 language-specific leaderboards, highlighting the robustness and competitiveness of the stacking-based SLM ensemble in multilingual polarization analysis.

