View Proposal

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Proposer

Simona Frenda

Title

Attitude Polarization Detection in Multilingual, Multicultural and Multievent Contexts

Goal

Create, fine-tune, or prompt models to detect polarized texts posted online (YouTube, X, Bluesky, Reddit), identify the details of this polarization and classify how it is expressed in the message.

Description

Polarization refers to the division of opinions into two sharply contrasting groups, often accompanied by hostility, intolerance, or exclusion. Polarization tends to intensify across platforms and geographies, influencing public discourse, exacerbating conflicts, and contributing to societal fragmentation [1]. In this project, we can explore all the following tasks, only the first one, or two of them (1 and 2, or 1 and 3) on a multilingual dataset [2]: Subtask 1: Polarization Detection – Binary classification to determine whether a post contains polarized content (Polarized or Not Polarized). Subtask 2: Polarization Type Classification – Identify the target of polarization, including political groups, religious groups, racial/ethnic communities, gender identities, sexual orientations, or other domain-specific targets. Subtask 3: Manifestation Identification – Classify how polarization is expressed; multiple labels possible, such as stereotyping, vilification, dehumanization, deindividuation, extreme language, lack of empathy, invalidation. We will evaluate the investigated models with the metrics suggested by the organizers of the shared task: Polar@SemEval-2026.

Resources

[1] Isaac Waller and Ashton Anderson. 2021. Quantifying social organization and political polarization in online platforms. Nature, 600(7887):264–268. [2] NASEEM, Usman, et al. POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization. https://arxiv.org/pdf/2505.20624

Background

https://polar-semeval.github.io/index.html

Url

Difficulty Level

Easy

Ethical Approval

None

Number Of Students

0

Supervisor

Simona Frenda

Keywords

bias detection, calibration of models, multiple annotated corpora

Degrees

Bachelor of Science in Computer Science
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Data Science
Bachelor of Science in Computing Science
BSc Data Sciences