A Taxonomy of Censors and Anti-Censors Part II

Leberknight, Christopher S.; Chiang, Mung; Wong, Felix Ming Fai

doi:10.4018/jep.2012100102

Cited by 16 publications

(13 citation statements)

References 20 publications

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“…There is a growing literature that focuses on what is censored [Deibert et al 2008[Deibert et al , 2010, how it is censored [Zittrain and Edelman 2003;Xu et al 2011;Anonymous 2012;Dornseif 2004;Clayton 2006; ICANN Security and Stability Advisory Committee (SSAC) 2012; Weaver et al 2009;Wolfgarten 2006;Fallows 2008;Sfakianakis et al 2011;Verkamp and Gupta 2012], and how censorship can be circumvented [Leberknight et al 2012a[Leberknight et al , 2012bElahi and Goldberg 2012].…”

Section: Related Workmentioning

confidence: 99%

Characterizing Web Censorship Worldwide

et al. 2015

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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.

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Section: Related Workmentioning

confidence: 99%

Characterizing Web Censorship Worldwide

et al. 2015

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“…There have been significant efforts to develop strategies to detect and evade censorship. Most work, however, focuses on exploiting technological limitations with existing routing protocols (Leberknight et al, 2012;Katti et al, 2005;Levin et al, 2015;McPherson et al, 2016;Weinberg et al, 2012). Research that pays more attention to linguistic properties of online censorship in the context of censorship evasion include, for example, Safaka et al (2016) who apply linguistic steganography to circumvent censorship.…”

Section: Previous Workmentioning

confidence: 99%

Neural Network Prediction of Censorable Language

Feldman

Peng

et al. 2019

Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

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Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House's annual Freedom on the Net report, more than half the world's Internet users now live in a place where the Internet is censored or restricted. China has built the world's most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention 'sensitive' topics or authored by 'sensitive' users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50% accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.

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“…Differently from veiling single terms, an entire domain might not be accessible, thus requiring different approaches to circumvent the block [29]. Work in [30,31,32] propose a survey on different topics related to censorship, either on detection or possible countermeasures. Also, monitoring tools exist, to trace the diffusion of the phenomenon [33,34].…”

Section: Related Workmentioning

confidence: 99%

Who framed Roger Reindeer? De-censorship of Facebook posts by snippet classification

Vigna

Petrocchi

Tommasi³

et al. 2018

Online Social Networks and Media

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This paper considers online news censorship and it concentrates on censorship of identities. Obfuscating identities may occur for disparate reasons, from military to judiciary ones. In the majority of cases, this happens to protect individuals from being identified and persecuted by hostile people. However, being the collaborative web characterised by a redundancy of information, it is not unusual that the same fact is reported by multiple sources, which may not apply the same restriction policies in terms of censorship. Also, the proven aptitude of social network users to disclose personal information leads to the phenomenon that comments to news can reveal the data withheld in the news itself. This gives us a mean to figure out who the subject of the censored news is. We propose an adaptation of a text analysis approach to unveil censored identities. The approach is tested on a synthesised scenario, which however resembles a real use case. Leveraging a text analysis based on a context classifier trained over snippets from posts and comments of Facebook pages, we achieve promising results. Despite the quite constrained settings in which we operate -such as considering only snippets of very short length -our system successfully detects the censored name, choosing among 10 different candidate names, in more than 50% of the investigated cases. This outperforms the results of two reference baselines. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the insidious issues of censorship on the web.

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A Taxonomy of Censors and Anti-Censors Part II

Cited by 16 publications

References 20 publications

Characterizing Web Censorship Worldwide

Characterizing Web Censorship Worldwide

Neural Network Prediction of Censorable Language

Who framed Roger Reindeer? De-censorship of Facebook posts by snippet classification

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