# PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification

arXiv: https://arxiv.org/abs/2602.19333 · HTML: https://mlsanity.com/paper/2602.19333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 22 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification](https://mlsanity.com/paper/2406.08660/) (2024)
- [Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs](https://mlsanity.com/paper/1812.04718/) (2018)
- [Language Models for Text Classification: Is In-Context Learning Enough?](https://mlsanity.com/paper/2403.17661/) (2024)
- [Pushing The Limit of LLM Capacity for Text Classification](https://mlsanity.com/paper/2402.07470/) (2024)
- [FaBERT: Pre-training BERT on Persian Blogs](https://mlsanity.com/paper/2402.06617/) (2024)
- [FaMTEB: Massive Text Embedding Benchmark in Persian Language](https://mlsanity.com/paper/2502.11571/) (2025)
- [TookaBERT: A Step Forward for Persian NLU](https://mlsanity.com/paper/2407.16382/) (2024)
- [LSCP: Enhanced Large Scale Colloquial Persian Language Understanding](https://mlsanity.com/paper/2003.06499/) (2020)
- [Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer](https://mlsanity.com/paper/2501.13467/) (2025)

## Abstract (arXiv)

This research introduces the first large-scale, well-balanced Persian social media text classification dataset, specifically designed to address the lack of comprehensive resources in this domain. The dataset comprises 36,000 posts across nine categories (Economic, Artistic, Sports, Political, Social, Health, Psychological, Historical, and Science & Technology), each containing 4,000 samples to ensure balanced class distribution. Data collection involved 60,000 raw posts from various Persian social media platforms, followed by rigorous preprocessing and hybrid annotation combining ChatGPT-based few-shot prompting with human verification. To mitigate class imbalance, we employed undersampling with semantic redundancy removal and advanced data augmentation strategies integrating lexical replacement and generative prompting. We benchmarked several models, including BiLSTM, XLM-RoBERTa (with LoRA and AdaLoRA adaptations), FaBERT, SBERT-based architectures, and the Persian-specific TookaBERT (Base and Large). Experimental results show that transformer-based models consistently outperform traditional neural networks, with TookaBERT-Large achieving the best performance (Precision: 0.9622, Recall: 0.9621, F1- score: 0.9621). Class-wise evaluation further confirms robust performance across all categories, though social and political texts exhibited slightly lower scores due to inherent ambiguity. This research presents a new high-quality dataset and provides comprehensive evaluations of cutting-edge models, establishing a solid foundation for further developments in Persian NLP, including trend analysis, social behavior modeling, and user classification. The dataset is publicly available to support future research endeavors.