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Abstract
In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship.
Highlights
In recent years, the automatic detection of online Hate Speech (HS), and offensive language more generally, has become an active research topic in machine learning (Davidson et al 2017; Schmidt and Wiegand 2017a, b; Fortuna and Nunes 2018)
The post9/11 preoccupation with anti-terrorism initiatives brought a new urgency to such considerations, and recent authoritative monographs such as Ishani Maitra and Mary Kate McGowan’s Speech and Harm: Controversies over Free Speech (Maitra and McGowan 2012), Jeremy Waldron’s The Harm in Hate Speech (Waldron 2012), Alex Brown’s Hate Speech Law: A Philosophical Examination (Brown 2015), and Eric Heinze’s Hate Speech and Democratic Citizenship (Heinze 2016) have explored a wide range of practical and theoretical
State-of-the-art methods for the automatic detection and classification of HS were summarised, before the main emphasis shifted to the way in which these technologies might eventually be used when their performance has improved
Summary
The automatic detection of online Hate Speech (HS), and offensive language more generally, has become an active research topic in machine learning (Davidson et al 2017; Schmidt and Wiegand 2017a, b; Fortuna and Nunes 2018). Recognising the non-trivial problems this creates, social media providers and video-sharing platforms such as YouTube, Facebook, and Twitter have developed internal policies for HS regulation, and they signed a Code of Conduct agreement with the European Commission (2019) At present, such decisions are taken at the corporate level, rather than the state level, which means that the companies concerned essentially regulate themselves. While previous research in this area has focused primarily on the core task of developing automated methods for detecting HS, this article probes instead the way in which such technologies
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