This paper presents a new version of the democratic argument for the freedom of expression that has the resources to give a plausible reply to the perennial objection—ordinarily considered fatal—that such accounts fail to deliver protections for abstract art, instrumental music, and lots of other deserving nonpolitical speech. The argument begins with the observation that there are different things that a free speech theory might aim to accomplish. It will hope to justify a right to free speech, of course, with (...) a particular scope and shape, but that right might be a moral right, an ordinary legal right, or a specifically constitutional right. The new version of the argument—the refined argument from democracy—aims to justify a specifically constitutional right to political speech, administered by strong-form judicial review. The democracy theorist can accommodate the intuition that abstract art deserves protection by agreeing that we have a moral right, and probably ought to have an ordinary legal right, to produce and view abstract art. She must only insist that abstract art should not be protected by a specifically constitutional right administered by strong-form judicial review. Not because abstract art does not deserve constitutional protection, but because, on this view, whether a particular right ought to be enshrined in the constitution and administered by judicial review is not solely a matter of the importance of the right or the value of the interests it protects. (shrink)

Harmful and dangerous language is frequent in social media, in particular in spaces which are considered anonymous and/or allow free participation. In this paper, we analyse the language in a Telegram channel populated by followers of Donald Trump, in order to identify the ways in which harmful language is used to create a specific narrative in a group of mostly like-minded discussants. Our research has several aims. First, we create an extended taxonomy of potentially harmful language that includes not only (...) hate speech and direct insults, but also more indirect ways of poisoning online discourse, such as divisive speech and the glorification of violence. We apply this taxonomy to a large portion of the corpus. Our data gives empirical evidence for harmful speech such as in/out-group divisive language and the use of codes within certain communities which have not often been investigated before. Second, we compare our manual annotations to several automatic methods of classifying hate speech and offensive language, namely list based and machine learning based approaches. We find that the Telegram data set still poses particular challenges for these automatic methods. Finally, we argue for the value of studying such naturally occurring, coherent data sets for research on online harm and how to address it in linguistics and philosophy. (shrink)