2014/02/21 by Donn Morrison, Morrison, Donn
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Internet Traffic Analysis and Secure E-voting #Physics and Society (physics.soc-ph) #Social Media and Politics #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1402.5310
13 pages. Updated with example cascades figure and typo fixes. To appear at the International Workshop on Data Mining in Social Networks (PAKDD-SocNet) 2014
openalex publication_date 2014/02/21 · arxiv created 2014/02/27 · arxiv updated 2014/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Social media is an area where users often experience censorship through a variety of means such as the restriction of search terms or active and retroactive deletion of messages. In this paper we examine the feasibility of automatically detecting censorship of microblogs. We use a network growing model to simulate discussion over a microblog follow network and compare two censorship strategies to simulate varying levels of message deletion. Using topological features extracted from the resulting graphs, a classifier is trained to detect whether or not a given communication graph has been censored. The results show that censorship detection is feasible under empirically measured levels of message deletion. The proposed framework can enable automated censorship measurement and tracking, which, when combined with aggregated citizen reports of censorship, can allow users to make informed decisions about online communication habits.