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Considerations for Successful Counterspeech

October 14, 2016

It may sometimes seem that the Internet is sullied by a relentless tide of hatred, vitriol, and extremist content, and that not much can be done to respond effectively. Such content cannot all be deleted, after all, since even if a statement, image, or user is deleted from one platform, there is always somewhere else to go.We have been pleasantly surprised, however, that our study of Twitter turned up numerous cases of effective counterspeech, which we define as a direct response to hateful or dangerous speech. Based on this first, qualitative study of counterspeech as it is practiced spontaneously on Twitter, we offer some preliminary suggestions on which strategies may help to make counterspeech successful.

Counterspeech on Twitter: A Field Study

October 14, 2016

As hateful and extremist content proliferates online, 'counterspeech' is gaining currency as a means of diminishing it. No wonder: counterspeech doesn't impinge on freedom of expression and can be practiced by almost anyone, requiring neither law nor institutions. The idea that 'more speech' is a remedy for harmful speech has been familiar in liberal democratic thought at least since U.S. Supreme Court Justice Louis Brandeis declared it in 1927. We are still without evidence, however, that counterspeech actually diminishes harmful speech or its effects. This would be very hard to measure offline but is a bit easier online, where speech and responses to it are recorded. In this paper we make a modest start. Specifically we ask: in what forms and circumstances does counterspeech - which we define as a direct response to hateful or dangerous speech - favorably influence discourse and perhaps even behavior?To our knowledge, this is the first study of Internet users (not a government or organization) counterspeaking spontaneously on a public platform like Twitter. Our findings are qualitative and anecdotal, since reliable quantitative detection of hateful speech or counterspeech is a problem yet to be fully solved due to the wide variations in language employed, although we made progress, as reported in an earlier paper that was part of this project (Saleem, Dillon, Benesch, & Ruths, 2016).We have identified four categories or "vectors" in each of which counterspeech functions quite differently, as hateful speech also does: one-to-one exchanges, many-to-one, one-to-many, and many-to-many. We also present a set of counterspeech strategies extrapolated from our data, with examples of tweets that illustrate those strategies at work, and suggestions for which ones may be successful.