Censorship is f̶u̶t̶i̶l̶e̶ possible but difficult: A study in algorithmic ethnography

Authors

  • Paul Watters Massey University

DOI:

https://doi.org/10.5210/fm.v20i1.5612

Keywords:

Censorship, Behavioural Advertising, Malware, Risk

Abstract

Discourse around censorship tends to be sensationalised in many quarters. Nabi (2014), for example, recently sought to “prove ... the futility” of governments engaged in censorship programmes through the Streisand Effect (Greenberg, 2007). While most countries have an imperfect censorship regime, the sovereign rights of nations to make their own laws must be recognised, including (but not limited to) the protection of children, and the victims of child exploitation, gambling addicts, and Internet banking users, whose systems may be infected by malicious software, resulting in financial losses. The broader question to be posed seems to be, under what circumstances is censorship justified, and how can it best be achieved? In this paper, we present the results of a study that illustrates the overwhelming harms to users that emerge from an unregulated Internet regime: 89 percent of ads delivered to Canadian users on 5,000 rogue sites for the most complained-about movies and TV shows were classified as “high risk”. We conclude that more granular policies on what should be censored and better tools to enforce those policies are needed, rather than accepting that censorship is impossible.

Author Biography

Paul Watters, Massey University

Dr. Paul A. Watters is Professor of Information Technology at Massey University. He was previously the Director of the Internet Commerce Security Laboratory, which is a joint venture between the Australian Federal Police (AFP), Westpac Banking Corporation, IBM, the State Government of Victoria and the University of Ballarat. He is a Fellow of the British Computer Society, a Senior Member of the IEEE, and a Chartered IT Professional.

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Published

2015-01-03

How to Cite

Watters, P. (2015). Censorship is f̶u̶t̶i̶l̶e̶ possible but difficult: A study in algorithmic ethnography. First Monday, 20(1). https://doi.org/10.5210/fm.v20i1.5612