Abstract:Publicly accessible censorship datasets, such as OONI and Censored Planet, provide valuable resources for understanding global censorship events. However, censorship event detection in these datasets is challenging due to the overwhelming amount of data, the dynamic nature of censorship, and potentially heterogeneous blocking policies across networks in the same country. This paper presents CenDTect, an unsupervised learning system based on decision trees that overcomes the scalability issue of manual analysis… Show more
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