用BERT预测微博内容是否被中国网络审查，效果优于传统方法。
A Classification System Approach in Predicting Chinese Censorship
- 构建带标注的中文短语数据集，采用逻辑回归和预训练模型分类
- 微调BERT在宏平均F1和ROC-AUC上均最优，准确率超其他方法
- 适合研究网络监管、信息过滤或中文NLP的学者参考
本文旨在利用分类器预测微博内容在中国互联网环境下是否会被审查。通过从Fu2021的研究中随机采样，并结合中文分词策略，我们构建了一个带有二值化审查标记的清洗后中文短语数据集。在该数据集上，运用多种基于概率的信息检索方法，推导出4个逻辑回归模型进行分类。此外，我们还实验了预训练变压器模型执行相同分类任务。在宏平均F1与ROC-AUC指标评估下，微调后的BERT模型性能超越其他策略。
原文摘要 · Abstract (English)
This paper is dedicated to using a classifier to predict whether a Weibo post would be censored under the Chinese internet. Through randomized sampling from \citeauthor{Fu2021} and Chinese tokenizing strategies, we constructed a cleaned Chinese phrase dataset with binary censorship markings. Utilizing various probability-based information retrieval methods on the data, we were able to derive 4 logistic regression models for classification. Furthermore, we experimented with pre-trained transformers to perform similar classification tasks. After evaluating both the macro-F1 and ROC-AUC metrics, we concluded that the Fined-Tuned BERT model exceeds other strategies in performance.
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