This thesis unites law and social science so as to give a comprehensive account of the phenomenon of racial hate speech in South Africa as an obstacle to transformation. Hate speech is presented as a form of violent language and an affront to the constitutional rights of freedom of speech, equality and dignity. To establish the nature of hate speech, the fluid quality of language is explored so as to show how language can be manipulated, on the one hand, as a means to harm, and employed, on the other hand, as a tool to heal and reconcile. This double gesture is illustrated through the South African linguistic experience of past hate and segregation and the current transformation agenda. It is through this prism that hate speech regulation is discussed as an uneasy fit in a country where freedom of expression is constitutionally protected and where language plays an important role in bringing about reconciliation, and yet words are still being employed to divide and dehumanise. This reality necessitates a clearly articulated stance on the regulation of language. The thesis accordingly interrogates the current legal standards in relation to hate speech with reference to international law that binds South Africa and the constitutional standard set for the regulation of language and the prohibition of hate speech. Thereafter, the current and proposed legislative prohibitions on hate speech, the residual common law provisions governing expression and the regulation of language in the media are outlined and analysed. These legal frameworks are explored in terms of their content and their application in various fora so as to ascertain what the South African approach to hate speech prohibition is, whether it is consistent and, ultimately if it is indeed suitable to the South African experience and the realities of language. This thesis concludes that contemporary hate speech measures lack a coherent understanding of what hate speech entails and a general inconsistency in approach as well as application is found in the treatment of hate speech complaints in South Africa. This is explained through the fallibility of language as a medium to regulate expression and solutions are offered to not only taper current and proposed hate speech provisions but to also consider alternative forms of resolving hate speech complaints

This thesis focuses on how well Hatescan, a hate speech detector built on the same Natural Language Processing and AI algorithms used in most online hate speech detectors, can detect different categories of antisemitism as well as whether or not it is worse at detecting implicit antisemitism than explicit antisemitism. The ability of hate speech detectors to detect antisemitic hate speech is a pressing issue. Jews have not only persevered through unparalleled historical oppression, but additionally, antisemitism is very much alive and kicking online, which poses not only a direct threat to individual Jews themselves (since there is a clear link between antisemitic expressions and antisemitic violence) but to the idea of liberal democracy itself. This thesis evaluated the efficacy of the hate speech detector, Hatescan, regarding its ability to detect antisemitism and to assess whether or not it was better or worse at detecting explicit antisemitism or implicit antisemitism, expressed in Swedish. Thus, the research questions posed for this thesis were: 1. How well does Hatescan detect antisemitism? 2. Is Hatescan equally efficient at detecting different categories of antisemitism? 3. Is Hatescan equally efficient at detecting implicit antisemitism and explicit antisemitism? To answer these questions, this thesis used the research strategy experiment, the data collection method documents, qualitative analysis methods (discourse analysis) for annotation, and quantitative analysis methods (descriptive statistics) for calculating performance metrics (precision, recall, F1-score, and accuracy). A dataset was created using three other previously existing datasets containing hate speech expressed in Swedish on Reddit, Flashback, and Twitter. The data collected was collected used search terms presumed to appear in antisemitic content. The datasets were created by the supervisor of this thesis and her research team for use in previous studies. These datasets were combined and made into one dataset (in a spreadsheet). Duplicates were deleted, adn each remaining sentence was annotated according to hatefulness, category of antisemtism and explicit versus implicit antisemitism. Each sentence was manually run through Hatescan’s web interface to generate a Hatescan output and said output was documented in the spreadsheet containing the data. Based on a threshold of 70% for generated Hatescan output, the Hatescan output for each sentence was annotated as either being a true positive, false positive, false negative, or true negative using IFS formulas in the spreadsheet. Precision, recall, and F1-score were calculated for the dataset as a whole, and accuracy rates were calculated for all categories of antisemitism as well as for explicit and implicit antisemitism. Results showed that while performance metrics on the antisemitic dataset (precision 0.93, recall 0.85, F1-score 0.89) were similar to the performance metrics in the development of Hatescan (precision 0.89, recall 0.94, F1-score 0.91), there were significant differences in accuracy between the different annotated categories in the dataset (accuracy ranging from 27 percent to 92 percent).

no / Verbal-textual hostility plays a significant role in victims¿ subjective perceptions of hatred and police officers¿ assessment of a hate crime. Yet, to date, the role of hate speech in hate crime has been largely uninterrogated. The aim of this research project is to assess and evaluate the forensic possibilities contained in a closer reading of the words used in hate crimes. Through a critical discourse analysis of incident characteristics and officers¿ narratives of incidents, this report maps out how key hate speech-text indicators may assist to better evaluate the force and effects of hate crimes. It is expected that this type of contextual analysis will lead to the development of more sophisticated risk assessment tools for use in frontline policing, and more targeted service-enhancements for victims of hate crimes.

Hateful content and offensive language are commonplace on social media platforms. Many surveys prove that high percentages of social media users experience online harassment. Previous efforts have been made to detect and remove online hate content automatically. However, removing users' content restricts free speech. A complementary strategy to address hateful content that does not interfere with free speech is to counter the hate with new content to divert the discourse away from the hate. In this dissertation, we complement the lack of previous work on counterhate arguments by analyzing and detecting them. Firstly, we study the relationships between hateful tweets and replies. Specifically, we analyze their fine-grained relationships by indicating whether the reply counters the hate, provides a justification, attacks the author of the tweet, or adds additional hate. The most obvious finding is that most replies generally agree with the hateful tweets; only 20% of them counter the hate. Secondly, we focus on the hate directed toward individuals and detect authentic counterhate arguments from online articles. We propose a methodology that assures the authenticity of the argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative compared to counterhate generation approaches that may hallucinate unsupported arguments. Thirdly, we investigate the replies to counterhate tweets beyond whether the reply agrees or disagrees with the counterhate tweet. We analyze the language of the counterhate tweet that leads to certain types of replies and predict which counterhate tweets may elicit more hate instead of stopping it. We find that counterhate tweets with profanity content elicit replies that agree with the counterhate tweet. This dissertation presents several corpora, detailed corpus analyses, and deep learning-based approaches for the three tasks mentioned above.

I explore the relationships between hate group activity, community factors, and the likelihood of hate crime occurrence within a county area. I integrate considerations raised by Routine Activity and Social Control theorists as well as current hate crime literature to frame my concept of the hate manager, an agent of social control that utilizes hate crimes as a means of enacting extralegal self-help for hate groups. I explore the relationship between hate managers and hate crime by testing a model relating hate group activity and hate crime occurrences by location. Next, I correlate hate crime occurrences with hate group activity at the county level for the state of Virginia using public data. I find that a hate group's presence holds greater predictive power than nearly any other factor for hate crime likelihood. My findings illustrate the nature of hate crime as a means of social control; whereby hate groups act as a parochial order and maintain hierarchical relations between offenders and victims through means of disciplinary crimes. I conclude by outlining suggestions for future research into the role of the hate manager. / Master of Science / In my thesis, I ask the question of how hate groups methodically encourage where hate crimes occur. I do this by creating the concept of the hate manager. Hate managers are figures which influence would-be criminals into their illegal acts. They do this by stoking the fears necessary for them to act outside legal boundaries in reaction to some feeling of threat, an act known as self-help. Hate crimes, I argue, are a form of self-help where the feeling of threat is directed towards individuals belonging to some marginalized group. By looking at data collected by various agencies in the state of Virginia, I discover that the presence of a hate group in a county is a stronger predictor for such acts than any other factor for hate crime likelihood. By doing so, I demonstrate that hate crimes are a form of social control. That is, I argue that hate groups maintain a sense of order or ranking by means of illegal and disciplinary self-help in the form of hate crimes. I conclude my thesis by outlining suggestions for future exploration of the hate manager’s role.

Lesbian, gay, bisexual, transgender and intersex people (LGBTI) are discriminated and subjected to violence in societies across the world, and ensuring their rights is on the international agenda. On a European level, nationalism, state-led persecution and rhetoric of hate have slowed down the process of ensuring human rights for LGBTI people particularly in Eastern Europe, where they are subject to violence, discrimination and hostility. One type of hostility targeting LGBTI people is hate speech. As literature has shown, hate speech can have harmful effects on its targets. Additionally, with the increased accessibility and use of the Internet and social media networks, hate speech has become more widespread and new challenges have appeared. Our research objective has addressed the gap identified by Brown (2018); to contribute to the research on hate speech and its harmful effects in general, comparing the effects of online and offline hate speech in particular. Additionally, by comparing the findings from Moldova and Ukraine, we have investigated if the context in which the hate speech is produced has any effect on the harmful effects experienced by targets. The research was conducted using a mixed method with a parallel convergent design, giving equal priority to qualitative and quantitative data. Data collection took place in Moldova (Chisinau) and in Ukraine (Kyiv) during Pride in the respective countries. Due to the nature of this research, results are not representative, and conclusions drawn can neither be applied to the entire LGBTI community in Moldova nor in Ukraine. Conclusions can, however, provide interesting insights for further research. Constitutive and consequential harms from online and offline hate speech are experienced by the LGBTI activists and community in both countries. In terms of constitutive harms, LGBTI community have suffered from harms like negative impact on self-esteem, silencing, psychological distress and restrictions on freedom of movement and association. Experienced consequential harms were negative stereotyping, physical violence and normalization of discrimination. Further, this research indicated that there is a difference in terms of harms caused by online and offline hate speech when it comes to the constitutive harms, as the harms from offline hate speech seemed to be experienced to a larger extent. Comparing results from Moldova and Ukraine, it can be concluded that the content of hate speech and harmful effects of hate speech are experienced differently depending on the context in which hate speech was produced. In general, hate speech in Ukraine seemed to be more violent and aggressive while in Moldova it was more related to the structural violence.

The purpose of this essay is to study the consequences of the swedish concept ”näthat” (hate on the internet hate), similar to the english hate speech, against swedish journalists and it's consequences for journalism as a whole in a democracy. How does it affect swedish journalists to have sensitive information about themselves and their families on public websites? How does threats from anonymous people affect them?   The study focus on the work ethics of journalism and it's function in a democratic society, how net hate affects it, and what consequences net hate may have on democracy.   The method of choice is a qualitative interview study, where the results from the interviews were analyzed against the theoretical science. The following people were chosen for the interviews: Helena Giertta, editor in chief at the newspaper Journalisten, Björn Wiman, head of culture at the newspaper Dagens Nyheter, Ann Persson, reporter at Dagens Nyheter, Johan Everljung, legally responsible publisher at the Swedish Television in Umeå, and Josefine Holgersson, reporter at the Swedish Television in Umeå.   In short: The study concludes that net hate is a big threat against journalism, in the sense that it can, and is, being used as a tool to silent journalists and sources that speaks about sensitive subjects as feminism, immigrants and related matters. It is also clear that some groups are more frequently using it, such as xenophobic groups. The problem is therefore that many people, at the risk of being subject of net hate, decide not to participate in neither the public conversations in society or as sources in the media. As a consequence, some matters will not be featured and reported on, and that is very problematic not only for journalism as a whole, but also for democracy. / Syftet med uppsatsen var att undersöka vilka konsekvenser så kallat ”näthat”, har på journalistiken: Hur påverkas journalister av att bli uthängda och hotade av anonyma? Vad får det för konsekvenser för journalistiken? Tyngdpunkten i undersökningen ligger på den journalistiska yrkesrollen i en samhällelig kontext, hur den påverkas av näthat, vad näthatet kan få för konsekvenser för demokratin och även att problematisera och studera begreppet näthat.   Metodologiskt bygger studien på en kvalitativ intervjustudie där resultaten ska jämföras med, och analyseras mot, normativ teori i form av befintlig litteratur och empirisk forskning. För att få svar på studiens frågeställningar valdes fem informanter ut genom strategiskt och snöbollsurval efter sin yrkesmässiga relevans. Valda informanter var Helena Giertta, chefredaktör på tidningen Journalisten, Björn Wiman, kulturchef på Dagens Nyheter, Ann Persson, nyhetsreporter på DN, Johan Everljung, ansvarig utgivare på SVT i Umeå och Josefine Holgersson, nyhetsreporter på SVT i Umeå. Med hjälp av materialet (intervjuer, och litteratur) ville studien ge en bild av hur näthatet påverkar journalistiken och journalisters yrkesutövande.   Kortfattat kom studien fram till att hot och hat på nätet är ett stort problem för journalistiken och det kan få stora konsekvenser för demokratin. Näthat används som ett verktyg för att tysta journalister och källor som vissa grupper (oftast främlingsfientliga) ogillar. Det är tydligt att vissa ämnen som ”feminism”, ”invandrare” och ”flyktingar” är extra känsliga och ofta generar näthat. Att det dessutom ofta är redan utsatta grupper som påverkas (exempelvis kvinnor och flyktingar) gör problemet ännu allvarligare då det kan leda till att dessa än mer utesluts eller väljer att inte medverka i det offentliga samtalet, vilket urholkar journalistikens demokratiuppdrag: att förmedla information, vara ett forum för debatt och diskussion, kommentera samhällsskeenden och granska politiken.

The legal restriction of hate speech – i.e. speech that expresses contempt for people on the basis of their ethnicity, religion, or sexuality – is now commonplace in liberal legal systems outside the United States. This thesis takes up the question of whether restrictions on hate speech are generally justifiable. I begin by explaining why liberals should not dismiss anti-hate speech law from the outset as an intolerable violation of free speech. My analysis of the case for anti-hate speech law is thereafter framed by two main concerns. Firstly, I stress that if we are to impose legal restrictions on hate speech, we must establish not just that there are harmful outcomes associated with hate speech, but that those who engage in hate speech are responsible for those outcomes. Secondly, I argue that restrictions on hate speech should be assessed in two distinct classes. Inquiries into the justificatory bases of anti-hate speech law are typically conducted as if informative generalisations can be made about how the law should respond to anything that is properly called hate speech. Against this approach, I argue that while the liberal state can and should impose restrictions on directly harmful hate speech (in which hate speech is used to threaten, harass, and incite violence), restrictions on indirectly harmful hate speech – in which hate speech (allegedly) contributes to identity-based social hierarchies and their concomitant harms – are not justifiable. The problem with restrictions on indirectly harmful hate speech is not the structure of the liability-ascription framework under which they operate. Rather, I argue, the problem is epistemic: we cannot confidently judge that hate-speakers are in fact responsible for contributing, more than trivially, to the harmful patterns of identity-based inequality and disadvantage in light of which restrictions on indirectly harmful hate speech may be defended in principle.

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This essay is about the hate Ghostbusters (2016) has received. To find out why the film is hated, a reception study has been done. YouTube and IMDb are the two websites used in this study to collect the hate-comments and reviews. Ghostbusters (2016) is a remake of the original film with the same name from 1984, the original solely has men in the main roles. A big difference between the two films is that the remake contains women instead of men in the main roles. The trailer of Ghostbusters (2016) got many “downvotes” on YouTube and there after the hatred streamed in. Why is the film hated?The theories used in this study contains subjects of representation, attraction and fat studies. As a method Stuart Hall’s Encoding/decoding was used to analyze the reception of the film. This essay contains content which can be connected to cultural studies and feminism.