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In response to the growing sophistication of censor- ship methods deployed by governments worldwide, the existence of open-source censorship measurement platforms has increased. Analyzing censorship data is challenging due to the data’s large size, diversity, and variability, requiring a comprehensive under- standing of the data collection process and applying established data analysis techniques for thorough information extraction. In this work, we develop a framework that is applicable across all major censorship datasets to continually identify changes in cen- sorship data trends and reveal potentially unreported censorship. Our framework consists of control charts and the Mann-Kendall trend detection test, originating from statistical process control theory, and we implement it on Censored Planet, GFWatch, the Open Observatory of Network Interference (OONI), and Tor data from Russia, Myanmar, China, Iran, T ¨ urkiye, and Pakistan from January 2021 through March 2023. Our study confirms results from prior studies and also identifies new events that we validate through media reports. Our correlation analysis reveals minimal similarities between censorship datasets. However, because our framework is applicable across all major censorship datasets, it significantly reduces the manual effort required to employ multiple datasets, which we further demonstrate by applying it to four additional Internet outage-related datasets. Our work thus provides a tool for continuously monitoring censorship activity and acts as a basis for developing more systematic, holistic, and in-depth analysis techniques for censorship data.
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Detecting Media Self-Censorship without Explicit Training Data
The motives and means of explicit state censorship have been well studied, both quantitatively and qualitatively. Self-censorship by media outlets, however, has not received nearly as much attention, mostly because it is difficult to systematically detect. We develop a novel approach to identify news media self-censorship by using social media as a sensor. We develop a hypothesis testing framework to identify and evaluate censored clusters of keywords and a near-linear-time algorithm (called GraphDPD) to identify the highest-scoring clusters as indicators of censorship. We evaluate the accuracy of our framework, versus other state-of-the-art algorithms, using both semi-synthetic and real-world data from Mexico and Venezuela during Year 2014. These tests demonstrate the capacity of our framework to identify self-censorship and provide an indicator of broader media freedom. The results of this study lay the foundation for detection, study, and policy-response to self-censorship.
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- PAR ID:
- 10223465
- Editor(s):
- Demeniconi; Carlotta; Nitesh V. Chawla
- Date Published:
- Journal Name:
- Proceedings of the 2020 SIAM International Conference on Data Mining
- Page Range / eLocation ID:
- 550-558
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1137/1.9781611976236.62
