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

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.