Abstract
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.
Abstract (translated)
这项研究引入了第一个大规模、平衡良好的波斯语社交媒体文本分类数据集，旨在解决该领域内综合性资源不足的问题。该数据集包含九个类别（经济、艺术、体育、政治、社会、健康、心理、历史和科技）的共计36,000篇帖子，每个类别包括4,000份样本，确保了类别的分布均衡。数据收集阶段从各种波斯语社交媒体平台中获取了60,000条原始帖子，并随后进行了严格的预处理以及结合ChatGPT少样本提示和人工验证的混合标注过程。 为缓解类别不平衡的问题，我们采用了欠采样并移除语义冗余的方法，同时采用包括词汇替换和生成性提示在内的高级数据增强策略。我们在多个模型（包括BiLSTM、XLM-RoBERTa及其LoRA和AdaLoRA适应版本、FaBERT、基于SBERT的架构以及专为波斯语设计的TookaBERT基础版和大型版）上进行了基准测试。 实验结果显示，基于转换器的模型始终优于传统的神经网络模型，其中TookaBERT-Large表现最佳（精确率：0.9622，召回率：0.9621，F1分数：0.9621）。类别级别的评估进一步证实了所有类别的稳健性能，尽管社会和政治文本由于固有的模糊性而得分略低。 这项研究不仅提供了一个新的高质量数据集，还全面评测了前沿模型的性能，为波斯语自然语言处理（NLP）领域的未来发展奠定了坚实的基础，包括趋势分析、社交行为建模及用户分类等。该数据集对未来的科研工作开放，可供下载使用。
URL
https://arxiv.org/abs/2602.19333
