text: We extend previous work about general election-related abuse of UK MPs with two new time periods, one in late 2018 and the other in early 2019, allowing previous observations to be extended to new data and the impact of key stages in the UK withdrawal from the European Union on patterns of abuse to be explored. The topics that draw abuse evolve over the four time periods are reviewed, with topics relevant to the Brexit debate and campaign tone showing a varying pattern as events unfold, and a suggestion of a "bubble" of topics emphasized in the run-up to the highly Brexit-focused 2017 general election. Brexit stance shows a variable relationship with abuse received. We find, as previously, that in quantitative terms, Conservatives and male politicians receive more abuse. Gender difference remains significant even when accounting for prominence, as gauged from Google Trends data, but prominence, or other factors related to being in power, as well as gender, likely account for the difference associated with party membership. No clear relationship between ethnicity and abuse is found in what remains a very small sample (BAME and mixed heritage MPs). Differences are found in the choice of abuse terms levelled at female vs. male MPs.
  @cite_7: This paper presents the first criminological analysis of an online social reaction to a crime event of national significance, in particular the detection and propagation of cyberhate on social media following a terrorist attack. We take the Woolwich, London terrorist attack in 2013 as our event of interest and draw on Cohen’s process of warning, impact, inventory and reaction to delineate a sequence of incidents that come to constitute a series of deviant responses following the attack. This paper adds to contemporary debates in criminology and the study of hate crime in three ways: (1) it provides the first analysis of the escalation, duration, diffusion and de-escalation of cyberhate in social media following a terrorist event; (2) it applies Cohen’s work on action, reaction and amplification and the role of the traditional media to the online context and (3) it introduces and provides a case study in ‘computational criminology’.
  @cite_32: Increasingly, scholars are examining the ways in which the Internet allows the hate movement to retrench and reinvent itself as a viable collective. The many electronic means available to the movement – blogs, newsgroups, ’zines, etc. – allow an ease of communication and dissemination of their views never before possible. While there are obvious points of convergence across the various Klan groups, or identity churches, or skinhead organizations, the hate movement has historically been varied and, in fact, fractured. Internet communication facilitates the creation of the collective identity that is so important to movement cohesiveness. Clearly, this has strengthened the domestic presence of these groups in countries like the United States, Germany and Sweden. Yet relatively less attention has been paid to the way in which the Web facilitates the consolidation of a global movement. Internet communication knows no national boundaries. Consequently, it allows the hate movement to extend its collective ident...
  @cite_6: Incivility in public discussions has received increasing attention from academic and popular commentators in recent years. In an effort to better understand the nature and determinants of such incivility, this study examined a 3-week census of articles and comments posted to a local newspaper’s website—totaling more than 300 articles and 6,400 comments. The results of the content analysis show that incivility occurs frequently and is associated with key contextual factors, such as the topic of the article and the sources quoted within the article. We also find that, contrary to popular perceptions, frequent commenters are more civil than are infrequent commenters, and uncivil commenters are no less likely than civil commenters to use evidence in support of their claims.
  @cite_0: The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby's murder to train and test a supervised machine learning text classifier that distinguishes between hateful and or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.
  @cite_15: User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.
summary: A larger body of work has looked at hatred on social media more generally @cite_32 @cite_6 @cite_15 . Williams @cite_7 and Burnap @cite_0 present work demonstrating the potential of Twitter for evidencing social models of online hate crime that could support prediction, as well as exploring how attitudes co-evolve with events to determine their impact. use natural language processing (NLP) to identify the groups targeted for hatred on Twitter and Whisper. Munger presents intriguing work on automated (bot) social sanctions, (e.g. munger2017tweetment munger2017tweetment ).