Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making

Abstract

In recent years, a new wave of hyperlocal community news websites has developed in the United Kingdom (UK), with many taking advantage of new opportunities provided by free open-source publishing platforms. Given the trend in the UK newspaper industry towards closure and retrenchment of their local and regional press titles, it is perhaps understandable that policy-makers have shifted their gaze to these sites. This article examines the viability of hyperlocal news services with a particular focus on those that are independently owned and managed. Such operations often have a longevity that sits in contrast to a number of failed attempts by major media organisations to operate in the hyperlocal space. Yet many of the business models that underpin these sites seem precarious, often benefiting from a degree of self-exploitation. Drawing on 35 interviews with hyperlocal news publishers from across the UK, this article argues that publishers draw upon a civic discourse in order to make sense of their practice. This framing may limit the potential to develop economic sustainability and risks alienating policy-makers keen to work with an idealised "fictive" hyperlocal entrepreneur.

Inducing a Lexicon of Abusive Words – a Feature-Based Approach

“…Most approaches consider it as one feature among many. Very often existing word lists from the web are employed (Xiang et al, 2012;Burnap and Williams, 2015;Nobata et al, 2016). Their limited effectiveness may be due to the fact that they were not built for the task of abusive language detection.…”

Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

“…A barrier for the use of text mining techniques for abusive content detection is the lack of labelled datasets in the field. At present, researchers collect data, and annotate by one of two approaches -their own labelling effort [30,9,1,19] which is timeconsuming or through the use of crowdsourcing services [25,2] such as Amazon's Mechanical Turk which can be costly.…”

Classification of tweets with a mixed method based on pragmatic content and meta-information

“…This type of classification is dependent on the language the tweet is written in and to some extent it is mostly for the expert to judge the ambiguities, turns of speech and context of language. This inevitable point of subjectivity, dependence on language, disappears as soon as the classification of the tweet is based only on the environmental issues [13] and not on its semantic and pragmatic content [14].…”