  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.
