Toward automatic censorship detection in microblogs

Cascade-based censorship (ICM model) and uniform random deletion produce measurably different topological signatures: cascade removal causes greater increases in network diameter and radius as the censorship fraction γ increases and a substantial increase in assortativity at mid-removal levels (γ=0.2–0.5), whereas uniform deletion shows slower, more gradual changes across these same metrics.

§4.1 evaluation ml-classifiermeasurement-platform cn

An SVM classifier using a 60-dimensional feature vector — 10 topological network metrics (assortativity, clustering coefficient, diameter, radius, betweenness centrality, degree distribution exponents) plus 50 Laplacian eigenvalues — can detect network-level censorship without any content analysis. The classifier successfully distinguishes censored from uncensored reply-graphs even at the lowest tested censorship level of γ=0.1 (10% edge removal), using 10-fold cross-validation repeated 10 times.

§3–4 detection ml-classifiermeasurement-platform cn

Censors on Sina Weibo were documented retroactively removing entire repost cascades started from a single sensitive post. Extrapolating from sampled data, prior work estimated that up to 4,200 workers working eight-hour shifts would be required to match the censorship demand on Sina Weibo alone, with documented peak hours for deletion activity.

§2 Related work evaluation keyword-filtering cn

A random sample of Sina Weibo messages found that 16.25% were deleted overall, with geographic distribution having a strong impact: up to 53% of messages from some Chinese provinces were deleted. Nearly 30% of all deletions occurred within the first 5–30 minutes of posting, and up to 90% within 24 hours of the posting.

§2 Related work evaluation keyword-filtering cn