@inproceedings{davoudi-goharian-2026-online,
    title = "Online Polarization Detection in {P}ersian ({F}arsi) Social Media",
    author = "Davoudi, Saeedeh  and
      Goharian, Nazli",
    editor = "Merchant, Rayyan  and
      Megerdoomian, Karine",
    booktitle = "The Proceedings of the First Workshop on {NLP} and {LLM}s for the {I}ranian Language Family",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.silkroadnlp-1.6/",
    pages = "50--59",
    ISBN = "979-8-89176-371-5",
    abstract = "Polarization detection in low-resource and mid-resource languages remains a significant challenge for social understanding. This paper presents the first comprehensive benchmark to evaluate transformer-based models for detection of polarized language in Persian (also called Farsi) social media. The aim is to evaluate 1) how and if finetuning the pre-trained models have substantial impact; 2) how Persian specific monolingual models compare to multilingual for this task; 3) how and if transfer learning from models trained on other languages such as culturally-distant English, and culturally-close[er] Turkish, and Arabic can be of interest for this task; and 4) how competitive Large Language Models (LLMs) are in a zero-shot setting. Our evaluation of ten transformer-based models and two LLMs on a publicly available Farsi polarization dataset shows promising findings,highlighting both the strengths and limitations of each approach."
}