# Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics

## TL;DR

This study analyzes how Sina Weibo censors posts by examining both text and images across various categories, revealing patterns in censorship timing, consistency, and indicators like sentiment, using deep learning techniques.

## Contribution

It introduces a multi-modal analysis approach combining text and image data to understand censorship mechanisms on Weibo, extending beyond previous text-only studies.

## Key findings

- Sentiment is a consistent indicator of censorship across topics.
- Categories related to protests and politicians are frequently censored.
- Most censored posts are deleted within three hours on average.

## Abstract

Widespread Chinese social media applications such as Weibo are widely known for monitoring and deleting posts to conform to Chinese government requirements. In this paper, we focus on analyzing a dataset of censored and uncensored posts in Weibo. Despite previous work that only considers text content of posts, we take a multi-modal approach that takes into account both text and image content. We categorize this dataset into 14 categories that have the potential to be censored on Weibo, and seek to quantify censorship by topic. Specifically, we investigate how different factors interact to affect censorship. We also investigate how consistently and how quickly different topics are censored. To this end, we have assembled an image dataset with 18,966 images, as well as a text dataset with 994 posts from 14 categories. We then utilized deep learning, CNN localization, and NLP techniques to analyze the target dataset and extract categories, for further analysis to better understand censorship mechanisms in Weibo. We found that sentiment is the only indicator of censorship that is consistent across the variety of topics we identified. Our finding matches with recently leaked logs from Sina Weibo. We also discovered that most categories like those related to anti-government actions (e.g. protest) or categories related to politicians (e.g. Xi Jinping) are often censored, whereas some categories such as crisis-related categories (e.g. rainstorm) are less frequently censored. We also found that censored posts across all categories are deleted in three hours on average.