Darwin's Theory of Censorship: Analysing the Evolution of Censored Topics with Dynamic Topic Models

Asim Waheed, Sara Qunaibi, Diogo Barradas, Zachary Weinberg
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Abstract

We present a statistical analysis of changes in the Internet censorship policy of the government of India from 2016 to 2020. Using longitudinal observations of censorship collected by the ICLab censorship measurement project, together with historical records of web page contents collected by the Internet Archive, we find that machine classification techniques can detect censors' reactions to events without prior knowledge of what those events are. However, gaps in ICLab's observations can cause the classifier to fail to detect censored topics, and gaps in the Internet Archive's records can cause it to misidentify them.
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达尔文的审查理论:用动态话题模型分析审查话题的演变
我们对2016年至2020年印度政府互联网审查政策的变化进行了统计分析。利用ICLab审查测量项目收集的审查制度的纵向观察,以及互联网档案馆收集的网页内容的历史记录,我们发现机器分类技术可以检测审查员对事件的反应,而无需事先知道这些事件是什么。然而,ICLab观察中的空白可能导致分类器无法检测到审查主题,而Internet Archive记录中的空白可能导致分类器错误识别它们。
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