@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Online Polarization Detection in Persian (Farsi) Social Media
%A Davoudi, Saeedeh
%A Goharian, Nazli
%Y Merchant, Rayyan
%Y Megerdoomian, Karine
%S The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-371-5
%F davoudi-goharian-2026-online
%X 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.
%U https://aclanthology.org/2026.silkroadnlp-1.6/
%P 50-59
Markdown (Informal)
[Online Polarization Detection in Persian (Farsi) Social Media](https://aclanthology.org/2026.silkroadnlp-1.6/) (Davoudi & Goharian, SilkRoadNLP 2026)
ACL