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.
@article{arxiv.2502.04234,
title = {A Classification System Approach in Predicting Chinese Censorship},
author = {Matt Prodani and Tianchu Ze and Yushen Hu},
journal= {arXiv preprint arXiv:2502.04234},
year = {2025}
}