Elisa Tsai (University of Michigan), Ram Sundara Raman (University of Michigan), Atul Prakash (University of Michigan), Roya Ensafi (University of Michigan)

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 and the interpretability issues of previous time-series methods. CenDTect employs iterative parallel DBSCAN to identify domains with similar blocking patterns, using an adapted cross-classification accuracy as the distance metric. The system analyzes more than 70 billion data points from Censored Planet between January 2019 and December 2022, discovering 15,360 HTTP(S) event clusters in 192 countries and 1,166 DNS event clusters in 77 countries. By evaluating CenDTect's findings with a curated list of 38 potential censorship events from news media and reports, we show how all events confirmed by the manual inspection are easy to characterize with CenDTect's output. We report more than 100 ASes in 32 countries with persistent ISP blocking. Additionally, we identify 11 temporary blocking events in clusters discovered in 2022, observed during periods of election, political unrest, protest, and war. Our approach provides informative and interpretable outputs, making censorship data more accessible to data consumers including researchers, journalists, and NGOs.

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