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
1830741683 2053816477 2186594794 2402831641
Over the years, the Internet has democratized the flow of information. Unfortunately, in parallel, authoritarian regimes and other entities (such as ISPs) for their vested interests have curtailed this flow by partially or fully censoring the web. The policy, mechanism, and extent of this censorship varies from country to country. We present the first study of the cause, effect, and mechanism of web censorship in Pakistan. Specifically, we use a publicly available list of blocked websites and check their accessibility from multiple networks within the country. Our results indicate that the censorship mechanism varies across websites: some are blocked at the DNS level while others at the HTTP level. Interestingly, the government shifted to a centralized, Internet exchange level censorship system during the course of our study, enabling our findings to compare two generations of blocking systems. Furthermore, we report the outcome of a controlled survey to ascertain the mechanisms that are being actively employed by people to circumvent censorship. Finally, we discuss some simple but surprisingly unexplored methods of bypassing restrictions. 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. 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. The Internet has traditionally been the most free medium for publishing and accessing information. It is also quickly becoming the dominant medium for quick and easy access to news. It is therefore not surprising that there are significant efforts to censor certain news articles or even entire web sites. For this reason, it is paramount to try to detect what is censored and by whom. In this paper we present the design and implemention of a web censorship monitor, called CensMon. CensMon is distributed in nature, operates automatically and does not rely on Internet users to report censored web sites, can differentiate access network failures from possible censorship, and uses multiple input streams to determine what kind of censored data to look for. Our evaluation shows that CensMon can successfully detect censored content and spot the filtering technique used by the censor.