Skype names of nude chats
This paper examines the social and political contexts behind the contents of these lists, and analyzes those times when the list has been updated, including correlations with current events. China’s complex regulatory system delegates much of the implementation of censorship and surveillance online to the private sector.
In many cases, measuring what is censored entails guessing terms that might be censorship keywords and then testing those terms, leading to results that are limited to the intersection between what censors are targeting and what those measuring the censorship expect to be censored.
Correlation between current events and keyword list updates 8. Key questions for the study of state–sponsored Internet censorship include: how do censors determine which content to control? Do censors care mostly about content that might cause organized movements such as protests?
Introduction While the existence of Internet censorship and surveillance in China is well known, questions remain concerning how such information controls are implemented in practice domestically. Do certain priorities apply when deciding what content should be subject to censorship and surveillance?
We conclude this paper with discussion of these questions and outline areas for future research.
Comparing the keyword lists of the two clients reveals very little overlap.
We obtained the keyword list URLs and encryption keys by reverse engineering the software binaries of the clients.
In the TOM–Skype client, keyword lists are used to trigger censorship and/or surveillance of user chats, while in Sina UC the keyword lists trigger only censorship.
Significant changes to keyword lists in both clients affected the implementation of censorship and surveillance functions.
Previous studies have similarly found little consistency in the implementation of censorship in Chinese blog services (Mac Kinnon, 2009) and search engines localized for the Chinese market (Villeneuve, 2008a).
These inconsistencies suggest that companies may be given general guidelines from authorities on what types of content to target, but have some degree of flexibility on how to implement these directives.
This data affords a rare opportunity to analyze the contents of, and updates to, complete and unbiased keyword lists used for both censorship and surveillance.
In this paper we discuss our efforts to translate and categorize these lists, and develop visualizations to help understand the relationships between keywords, categories, and current events.
Similarly, on 17 September 2012, four of the five Sina UC lists were reduced to a single keyword.