Us and them: identifying cyber hate on Twitter across multiple protected characteristics

Hateful and antagonistic content published and propagated via the World Wide Web has the potential to cause harm and suffering on an individual basis, and lead to social tension and disorder beyond cyber space. Despite new legislation aimed at prosecuting those who misuse new forms of communication to post threatening, harassing, or grossly offensive language - or cyber hate - and the fact large social media companies have committed to protecting their users from harm, it goes largely unpunished due to difficulties in policing online public spaces. To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability and sexual orientation. We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture ‘othering’ language - consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. Furthermore, we build a data-driven blended model of cyber hate to improve classification where more than one protected characteristic may be attacked ( e.g. race and sexual orientation), contributing to the nascent study of intersectionality in hate crime

Perceptions of the eCrime controllers: modelling the influence of cooperation and data source factors

eCrime is now the typical volume property crime in the United Kingdom impacting more of the public than traditional acquisitive crimes such as burglary and car theft (Anderson et al, 2012). It has become increasingly central to the National Security Strategy of several countries; in the United Kingdom becoming a Tier One threat. While it is apparent to some governments that cybercrimes are now as much of a ‘problem’ as some forms of organised crime, little is known about the perceptions of the broad network of what we call public and private sector ‘eCrime controllers’ in the United Kingdom. A survey of 104 members of the UK Information Assurance community garnered data on the perceptions of the eCrime problem. The results showed an association of cooperation and consumption of data sources with perceptions. It is likely that perceptions within non-specialist corporate and public domains (non-IT and Finance) will begin to change as new cooperation arrangements are introduced as part of the UK Cyber Security Strategy. These findings call for a more in-depth qualitative understanding of the cooperation between eCrime controllers and their data consumption practices. Ascertaining what shapes this cooperation (and non-cooperation) and how perceptions compare with ‘actual’ threats and risks is necessary if we are to better understand the ‘social construction’ of the problem and subsequent policy and operational outcomes

Multi-agency partnerships in cybercrime reduction: Mapping the UK information assurance network cooperation space

Purpose – This paper aims to map out multi-agency partnerships in the UK information assurance (UKIA) network in the UK. Design/methodology/approach – The paper surveyed members of the UKIA community and achieved a 52 percent response rate (n=104). The paper used a multi-dimensional scaling (MDS) technique to map the multi-agency cooperation space and factor analysis and ordinary least squares regression to identify predictive factors of cooperation frequency. Qualitative data were also solicited via the survey and interviews with security managers. Findings – Via the quantitative measures, the paper locates gaps in the multi-agency cooperation network and identifies predictors of cooperation. The data indicate an over-crowded cybersecurity space, problems in apprehending perpetrators, and poor business case justifications for SMEs as potential inhibitors to cooperation, while concern over certain cybercrimes and perceptions of organisational effectiveness were identified as motivators. Practical implications – The data suggest that the neo-liberal rationality that has been evoked in other areas of crime control is also evident in the control of cybercrimes. The paper concludes divisions exist between the High Policing rhetoric of the UK's Cyber Security Strategy and the (relatively) Low Policing cooperation outcomes in “on the ground” cyber-policing. If the cooperation outcomes advocated by the UK Cyber Security Strategy are to be realised, UKIA organisations must begin to acknowledge and remedy gaps and barriers in cooperation. Originality/value – This paper provides the first mixed-methods evidence on the multi-agency cooperation patterns amongst the UKIA community in the UK and highlights significant gaps in the network

Hate speech, machine classification and statistical modelling of information flows on Twitter: interpretation and communication for policy decision making

In 2013, the murder of Drummer Lee Rigby in Woolwich, UK led to an extensive public social media reaction. Given the extreme terrorist motive and public nature of the actions it was feasible that the public response could include written expressions of hateful and antagonistic sentiment towards a particular race, ethnicity and religion, which can be interpreted as ‘hate speech’. This provided motivation to study the spread of hate speech on Twitter following such a widespread and emotive event. In this paper we present a supervised machine learning text classifier, trained and tested to distinguish between hateful and/or antagonistic responses with a focus on race, ethnicity or religion; and more general responses. We used human annotated data collected from Twitter in the immediate aftermath of Lee Rigby’s murder to train and test the classifier. As “Big Data” is a growing topic of study, and its use is in policy and decision making is being constantly debated at present, we discuss the use of supervised machine learning tools to classify a sample of “Big Data”, and how the results can be interpreted for use in policy and decision making. The results of the classifier are optimal using a combination of probabilistic, rule-based and spatial based classifiers with a voted ensemble meta-classifier. We achieve an overall F-measure of 0.95 using features derived from the content of each tweet, including syntactic dependencies between terms to recognise “othering” terms, incitement to respond with antagonistic action, and claims of well founded or justified discrimination against social groups. We then demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of hate speech in a sample of Twitter data