The emergence of social media has largely eased the way people receive
information and participate in public discussions. However, in countries with
strict regulations on discussions in the public space, social media is no
exception. To limit the degree of dissent or inhibit the spread of "harmful"
information, a common approach is to impose information operations such as
censorship/suspension on social media. In this paper, we focus on a study of
censorship on Weibo, the counterpart of Twitter in China. Specifically, we 1)
create a web-scraping pipeline and collect a large dataset solely focus on the
reposts from Weibo; 2) discover the characteristics of users whose reposts
contain censored information, in terms of gender, device, and account type; and
3) conduct a thematic analysis by extracting and analyzing topic information.
Note that although the original posts are no longer visible, we can use
comments users wrote when reposting the original post to infer the topic of the
original content. We find that such efforts can recover the discussions around
social events that triggered massive discussions but were later muted. Further,
we show the variations of inferred topics across different user groups and time
frames.Comment: Accepted for publication in Proceedings of the International Workshop
on Social Sensing (SocialSens 2022): Special Edition on Belief Dynamics, 202