Social media is an area where users often experience censorship through a variety of means such as the restriction of search terms or active and retroactive deletion of messages. In this paper we examine the feasibility of automatically detecting censorship of microblogs. We use a network growing model to simulate discussion over a microblog follow network and compare two censorship strategies to simulate varying levels of message deletion. Using topological features extracted from the resulting graphs, a classifier is trained to detect whether or not a given communication graph has been censored. The results show that censorship detection is feasible under empirically measured levels of message deletion. The proposed framework can enable automated censorship measurement and tracking, which, when combined with aggregated citizen reports of censorship, can allow users to make informed decisions about online communication habits.
Despite these important works, no research to date has explored the effects of censorship on the underlying structure of the network and furthermore no research exists that attempts to automatically detect and classify censorship in these networks. Given that online social networks have certain universal properties @cite_8 , it is likely that common strategies of censorship such as limiting or deleting content or users from the network would have measurable effects on these properties. This research constitutes a first step to fill this gap by studying these effects.
@cite_8
2085761620
The Iranian government operates one of the largest and most sophisticated Internet censorship regimes in the world, but the mechanisms it employs have received little research attention, primarily due to lack of access to network connections within the country and personal risks to Iranian citizens who take part. In this paper, we examine the status of Internet censorship in Iran based on network measurements conducted from a major Iranian ISP during the lead up to the June 2013 presidential election. We measure the scope of the censorship by probing Alexa’s top 500 websites in 18 different categories. We investigate the technical mechanisms used for HTTP Host‐based blocking, keyword filtering, DNS hijacking, and protocol-based throttling. Finally, we map the network topology of the censorship infrastructure and find evidence that it relies heavily on centralized equipment, a property that might be fruitfully exploited by next generation approaches to censorship circumvention. In this study, we take another look at 5 years of web censorship data gathered by the OpenNet Initiative in 77 countries using user-based testing with locally relevant content. Prior to our work, this data had been analyzed with little automation, focusing on what content had been blocked, rather than how blocking was carried out. In this study, we use more rigorous automation to obtain a longitudinal, global view of the technical means used for web censorship. We also identify blocking that had been missed in prior analyses. Our results point to considerable variability in the technologies used for web censorship, across countries, time, and types of content, and even across ISPs in the same country. In addition to characterizing web censorship in countries that, thus far, have eluded technical analysis, we also discuss the implications of our observations on the design of future network measurement platforms and circumvention technologies. We present measurements and analysis of censorship on Weibo, a popular microblogging site in China. Since we were limited in the rate at which we could download posts, we identified users likely to participate in sensitive topics and recursively followed their social contacts. We also leveraged new natural language processing techniques to pick out trending topics despite the use of neologisms, named entities, and informal language usage in Chinese social media. We found that Weibo dynamically adapts to the changing interests of its users through multiple layers of filtering. The filtering includes both retroactively searching posts by keyword or repost links to delete them, and rejecting posts as they are posted. The trend of sensitive topics is short-lived, suggesting that the censorship is effective in stopping the "viral" spread of sensitive issues. We also give evidence that sensitive topics in Weibo only scarcely propagate beyond a core of sensitive posters. In addition to their common use for private online communication, anonymous communication networks can also be used to circumvent censorship. However, it is difficult to determine the extent to which they are actually used for this purpose without violating the privacy of the networks' users. Knowing this extent can be useful to designers and researchers who would like to improve the performance and privacy properties of the network. To address this issue, we propose a statistical data collection system, PrivEx, for collecting egress traffic statistics from anonymous communication networks in a secure and privacy-preserving manner. Our solution is based on distributed differential privacy and secure multiparty computation; it preserves the security and privacy properties of anonymous communication networks, even in the face of adversaries that can compromise data collection nodes or coerce operators to reveal cryptographic secrets and keys.