Measuring QQMail's automated email censorship in China

Knockel, Jeffrey; Ruan, Lotus

doi:10.1145/3473604.3474560

Cited by 3 publications

(3 citation statements)

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“…The CAC has occasionally fined Tencent, Baidu and Weibo for failing to censor online content: from pornography to fake news, or any content that ‘incites ethic tension’ and ‘threatens social order’. In this connection, the Citizen Lab of the University of Toronto found that the most popular social app in China, WeChat, is constantly assisting the government in the surveillance of images and files shared on its platform through machine learning, using data collected from its users without any notification (Knockel et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Countering violent extremism through internet intermediaries: A typology for cross-country comparison

Wong

2024

Global Media and Communication

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This paper examines the counter-violent extremism and anti-terrorism measures in Australia, China, France, the United Kingdom and the United States by investigating how governments leveraged internet intermediaries as their surrogate censors. Particular attention is paid to how political rhetoric led to legislation passed or proposed in each of the countries studied, and their respective restrictive measures are compared against the recommendations specified by the United Nations Special Rapporteur on the Promotion and Protection of the Right to Freedom of Opinion and Expression. A typology for international comparison is proposed, which provides further insights into a country’s policy focus.

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Section: Resultsmentioning

confidence: 99%

Countering violent extremism through internet intermediaries: A typology for cross-country comparison

Wong

2024

Global Media and Communication

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“…Relevant research from previous academic studies has shown that censorship exists in many countries such as China (Chen et al, 2013;Clayton et al, 2006;Dunna et al, 2018;Ensafi et al, 2015;Holowczak & Houmansadr, 2015;Hounsel et al, 2018;King et al, 2013King et al, , 2014Knockel et al, 2015Knockel et al, , 2017Knockel et al, , 2018Lowe et al, 2007;Marczak et al, 2015;Ng et al, 2018;Park & Crandall, 2010;Robinson et al, 2013;Winter & Lindskog, 2012;Wright, 2012;Xu et al, 2011), Thailand (Gebhart et al, 2017), Bangladesh (Morshed et al, 2017), Pakistan (Aceto et al, 2016;Nabi, 2013), India (Gosain et al, 2017;Yadav et al, 2018), Iran (Anderson, 2012(Anderson, , 2013Aryan et al, 2013), Syria (Al-Saqaf, 2016Chaabane et al, 2014), Turkey (Tanash et al, 2015(Tanash et al, , 2017, Russia (Ramesh et al, 2020), and Mexico (Iszaevich, 2019). A few studies have looked at network interference and Internet blocking in the EU context (Busch et al, 2018;Savola, 2015;Schmidt-Kessen et al, 2019;Ververis et al, 2015Ververis et al, , 2017Ververis et al, , 2021.…”

Section: Structurementioning

confidence: 99%

Website blocking in the European Union: Network interference from the perspective of Open Internet

Ververis,

Lasota,

Ermakova

et al. 2023

Policy & Internet

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By establishing an infrastructure for monitoring and blocking networks in accordance with European Union (EU) law on preventive measures against the spread of information, EU member states have also made it easier to block websites and services and monitor information. While relevant studies have documented Internet censorship in non‐European countries, as well as the use of such infrastructures for political reasons, this study examines network interference practices such as website blocking against the backdrop of an almost complete lack of EU‐related research. Specifically, it performs and demonstrates an analysis for the total of 27 EU countries based on three different sources. They include first, tens of millions of historical network measurements collected in 2020 by Open Observatory of Network Interference volunteers from around the world; second, the publicly available blocking lists used by EU member states; and third, the reports issued by network regulators in each country from May 2020 to April 2021. Our results show that authorities issue multiple types of blocklists. Internet Service Providers limit access to different types and categories of websites and services. Such resources are sometimes blocked for unknown reasons and not included in any of the publicly available blocklists. The study concludes with the hurdles related to network measurements and the nontransparency from regulators regarding specifying website addresses in blocking activities.

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“…There has also been research that uses linguistic and content clues to detect censorship. Knockel et al (2015) and Zhu et al (2013) propose detection mechanisms to categorize censored content and automatically learn keywords that get censored. King et al (2013) in turn study the relationship between political criticism and chance of censorship.…”

Section: Previous Workmentioning

confidence: 99%

Linguistic Fingerprints of Internet Censorship: The Case of Sina Weibo

Feldman

Peng

2020

AAAI

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This paper studies how the linguistic components of blogposts collected from Sina Weibo, a Chinese microblogging platform, might affect the blogposts' likelihood of being censored. Our results go along with King et al. (2013)'s Collective Action Potential (CAP) theory, which states that a blogpost's potential of causing riot or assembly in real life is the key determinant of it getting censored. Although there is not a definitive measure of this construct, the linguistic features that we identify as discriminatory go along with the CAP theory. We build a classifier that significantly outperforms non-expert humans in predicting whether a blogpost will be censored. The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to ‘reading the mind’ of the censors in deciding whether a blogpost should be censored. We do not claim that censorship is only determined by the linguistic features. There are many other factors contributing to censorship decisions. The focus of the present paper is on the linguistic form of blogposts. Our work suggests that it is possible to use linguistic properties of social media posts to automatically predict if they are going to be censored.

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Measuring QQMail's automated email censorship in China

Cited by 3 publications

References 4 publications

Countering violent extremism through internet intermediaries: A typology for cross-country comparison

Countering violent extremism through internet intermediaries: A typology for cross-country comparison

Website blocking in the European Union: Network interference from the perspective of Open Internet

Linguistic Fingerprints of Internet Censorship: The Case of Sina Weibo

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