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.
Detection of deleted posts has been used in previous work to quantify censorship. The most pervasive methodology involves sampling microblog posts over a period of time to capture sensitive political events while querying the service at regular intervals to determine if any of the posts have been deleted @cite_17 @cite_6 @cite_18 .
@cite_18 @cite_6 @cite_17
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We offer the first large scale, multiple source analysis of the outcome of what may be the most extensive effort to selectively censor human expression ever implemented. To do this, we have devised a system to locate, download, and analyze the content of millions of social media posts originating from nearly 1,400 different social media services all over China before the Chinese government is able to find, evaluate, and censor (i.e., remove from the Internet) the subset they deem objectionable. Using modern computer-assisted text analytic methods that we adapt to and validate in the Chinese language, we compare the substantive content of posts censored to those not censored over time in each of 85 topic areas. Contrary to previous understandings, posts with negative, even vitriolic, criticism of the state, its leaders, and its policies are not more likely to be censored. Instead, we show that the censorship program is aimed at curtailing collective action by silencing comments that represent, reinforce, or spur social mobilization, regardless of content. Censorship is oriented toward attempting to forestall collective activities that are occurring now or may occur in the future—and, as such, seem to clearly expose government intent. We present measurements and analysis of censorship on Weibo, a popular microblogging site in China. Since we were limited in the rate at which we could download posts, we identified users likely to participate in sensitive topics and recursively followed their social contacts. We also leveraged new natural language processing techniques to pick out trending topics despite the use of neologisms, named entities, and informal language usage in Chinese social media. We found that Weibo dynamically adapts to the changing interests of its users through multiple layers of filtering. The filtering includes both retroactively searching posts by keyword or repost links to delete them, and rejecting posts as they are posted. The trend of sensitive topics is short-lived, suggesting that the censorship is effective in stopping the "viral" spread of sensitive issues. We also give evidence that sensitive topics in Weibo only scarcely propagate beyond a core of sensitive posters. Information extraction from microblog posts is an important task, as today microblogs capture an unprecedented amount of information and provide a view into the pulse of the world. As the core component of information extraction, we consider the task of Twitter entity linking in this paper. In the current entity linking literature, mention detection and entity disambiguation are frequently cast as equally important but distinct problems. However, in our task, we find that mention detection is often the performance bottleneck. The reason is that messages on micro-blogs are short, noisy and informal texts with little context, and often contain phrases with ambiguous meanings. To rigorously address the Twitter entity linking problem, we propose a structural SVM algorithm for entity linking that jointly optimizes mention detection and entity disambiguation as a single end-to-end task. By combining structural learning and a variety of firstorder, second-order, and context-sensitive features, our system is able to outperform existing state-of-the art entity linking systems by 15 F1. A number of existing works have focused on the problem of malicious following activity detection in microblog services. However, most of them make the assumption that the spamming following relationships are either from fraudulent accounts or compromised legitimate users. They therefore developed detection methodologies based on the features derived from this assumption. Recently, a new type of malicious crowdturfing following relationship is provided by the follower market, called voluntary following. Followers who provide voluntary following services (or named volowers) are normal users who are willing to trade their following activities for profit. Since most of their behaviors follow normal patterns, it is difficult for existing methods to detect volowers and their corresponding customers. In this work, we try to solve the voluntary following problem through a newly proposed detection method named DetectVC. This method incorporates both structure information in user following behavior graphs and prior knowledge collected from follower markets. Experimental results on large scale practical microblog data set show that DetectVC is able to detect volowers and their customers simultaneously and it also significantly outperforms existing solutions.