We propose and defend a distinction between two types of self-censorship: public and private. In public self-censorship, individuals restrain their expressive attitudes in response to public censors. In private self-censorship, individuals do so in the absence of public censorship. We argue for this distinction by introducing a general model which allows us to identify, describe, and compare a ...

censorship has a long history in iran that has interfered with text production, i.e., original writing as well as translation. this phenomenon seems to have marked the borderline between the government and the ‘enlightened’ intellectuals throughout history in iran. different governments have delineated ‘redlines’ for authors and translators and dealt with these constructors of culture based on ...

In this work, we examine the effect of the 2016 Turkish coup on social media censorship, both by the government ordering Twitter to conduct censorship and as well by people removing their own tweets. We compared 5.5M tweets collected from Turkey pre-coup to 8.5M tweets collected post-coup. Although self-censorship of the press is not a novel practice following past military coups in Turkey, in ...

Within a simple model of non-localized, Hotelling-type competition among arbitrary numbers of media outlets we characterize quality and content of media under different ownership structures. Assuming advertising-sponsored, profit-maximizing outlets, we show that (i) topics sensitive to advertisers can be underreported (self-censored) by all outlets in the market, (ii) self-censorship increases ...

Censorship in social media has been well studied and provides insight into how governments stifle freedom of expression online. Comparatively less (or no) attention has been paid to detecting (self) censorship in traditional media (e.g., news) using social media as a bellweather. We present a novel unsupervised approach that views social media as a sensor to detect censorship in news media wher...

We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last...