Access Denied The Practice and Policy of Global Internet Filtering

Deibert, Ronald J.; Palfrey, John; Rohozinski, Rafal; Zittrain, Jonathan

doi:10.21428/cb6ab371.9034e9f7

Cited by 80 publications

(29 citation statements)

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“…For each model, we use identical training labels, from the TNEWS dataset. 11 This yields a total of nine models, with three for each pre-trained word embeddings. Each trained model is then used to predict sentiment labels on the test set.…”

Section: Methodsmentioning

confidence: 99%

“…10 41 duplicated news headlines are dropped, resulting in 12,628 headlines in total. 11 Because headlines with neutral labels are more noisy and given the difficulty of training a three-class classifier with limited training data, we report results in the main text based on models that are trained with only positive and negative headlines. We report results with neutral headlines included in the Appendix.…”

Section: Methodsmentioning

confidence: 99%

“…While we use the example of online encyclopedias and word embeddings to make our point, its implications are much more general. Government censorship of social media, news, and websites directly affects large corpuses of text by blocking users' access, deleting individual messages, adding content through propaganda, or inducing self-censorship through intimidation and laws [11,19,20,22,25,32,34]. While Wikipedia's global reach makes it an attractive corpus for training models in many different languages, Wikipedia has also been periodically censored by many governments, including Iran, China, Uzbekistan, and Turkey [10].…”

Section: Censorship Of Chinese Language Wikipedia and Implications Fo...mentioning

confidence: 99%

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Censorship of Online Encyclopedias

Yang

Roberts

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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While artificial intelligence provides the backbone for many tools people use around the world, recent work has brought to attention that the algorithms powering AI are not free of politics, stereotypes, and bias. While most work in this area has focused on the ways in which AI can exacerbate existing inequalities and discrimination, very little work has studied how governments actively shape training data. We describe how censorship has affected the development of Wikipedia corpuses, text data which are regularly used for pre-trained inputs into NLP algorithms. We show that word embeddings trained on Baidu Baike, an online Chinese encyclopedia, have very different associations between adjectives and a range of concepts about democracy, freedom, collective action, equality, and people and historical events in China than its regularly blocked but uncensored counterpart -Chinese language Wikipedia. We examine the implications of these discrepancies by studying their use in downstream AI applications. Our paper shows how government repression, censorship, and self-censorship may impact training data and the applications that draw from them. CCS CONCEPTS• Computing methodologies → Supervised learning by classification; • Information systems → Content analysis and feature selection; • Social and professional topics → Political speech.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Censorship Of Chinese Language Wikipedia and Implications Fo...mentioning

confidence: 99%

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Censorship of Online Encyclopedias

Yang

Roberts

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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“…Government censorship of the Internet was systematically studied by Zittrain and Edelman [9], in their seminal analysis of filtering by the People's Republic of China. Important early studies were then contributed by Deibert et al [10], Wolfgarten [11], and Dornseif [12], who describe not only censorship policy but also mechanism of filtering as well as anti-censorship measures. Work in the area has since focused on either determining exactly which content is blocked in a given country (i.e., policy) or how such blocking is performed (mechanism).…”

Section: A Internet Censorshipmentioning

confidence: 99%

Few Throats to Choke: On the Current Structure of the Internet

Acharya

Chakravarty

Gosain

2017

2017 IEEE 42nd Conference on Local Computer Networks (LCN)

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in † All authors have equal intellectual contributions towards the paper. The author names are therefore arranged in alphabetical order of their last names.Abstract-The original design of the Internet was a resilient, distributed system, that maybe able to route around (and therefore recover from) massive disruption -up to and including nuclear war. However, network routing effects and business decisions cause traffic to often be routed through a relatively small set of Autonomous Systems (ASes). This is not merely an academic issue; it has practical implications -some of these frequently appearing ASes are hosted in censorious nations. Other than censoring their own citizens' network access, such ASes may inadvertently filter traffic for other foreign customer ASes.In this paper, we examine the extent of routing centralization in the Internet; identify the major players who control the "Internet backbone"; and point out how many of these are, in fact, under the jurisdiction of censorious countries (specifically, Russia, China, and India). Further, we show that China and India are not only the two largest nations by number of Internet users, but that many users in free and democratic countries are affected by collateral damage caused due to censorship by such countries.

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“…Many of the authors highlight that the foundations of these threats are most often motivated for ideological reasons [16], thus, in countries in which free of speech or political freedom are limited, privacy and anonymity are considered an enemy of the state. In addition, national defense and social "morality" are also some of the main arguments utilized when justifying actions against privacy and anonymity [8].…”

Section: A Social and Political Motivationsmentioning

confidence: 99%

Privacy and Anonymity

Yanes

2014

Preprint

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Since the beginning of the digital area, privacy and anonymity have been impacted drastically (both, positively and negatively), by the different technologies developed for communications purposes. The broad possibilities that the Internet offers since its conception, makes it a mandatory target for those entities that are aiming to know and control the different channels of communication and the information that flows through. In this paper, we address the current threats against privacy and anonymity on the Internet, together with the methods applied against them. In addition, we enumerate the publicly known entities behind those threats and their motivations. Finally, we analyze the state of the art concerning the protection of the privacy and anonymity on the Internet; introducing future lines of research.

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Access Denied The Practice and Policy of Global Internet Filtering

Cited by 80 publications

References 0 publications

Censorship of Online Encyclopedias

Censorship of Online Encyclopedias

Few Throats to Choke: On the Current Structure of the Internet

Privacy and Anonymity

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