New Dissertations

This article investigates the identification of hate speech on social media using machine learning and deep learning techniques. The research uses metrics such as F-measure, AUC-ROC, precision, accuracy, and recall assessing the effectiveness of various tactics. The findings indicate that deep learning models, particularly the bidirectional long short-term memory (BiLSTM) architecture, consistently outperform other methods in categorization tasks. The research emphasizes the importance of sophisticated neural network designs in identifying the intricacies of hostile and offensive content online. The study offers insights for promoting early identification and prevention of cyberbullying, improving secure and inclusive online environments. Future research may explore real-time detection systems, hybrid approaches, or the integration of complementary components to enhance and improve innovative technology in tackling this significant social issue. A sample tweet was annotated by specialists who categorize tweets as hate speech, offensive language, or neutral. The researchers applied shallow learning methodologies and integrated word embeddings like Word2Vec and GloVe to enhance the efficacy of deep learning models. The results indicate that BiLSTM surpasses shallow learning methods in detecting hate speech on Twitter, highlighting the efficacy of deep learning approaches in recognizing and tracking hate speech on social media platforms. When comparing different deep learning and machine learning models on different datasets, the results reveal that deep learning techniques are usually more effective. A reasonably high level of accuracy is achieved by KNN and SVM among classical algorithms, whereas Naïve Bayes performs the poorest. While deep learning approaches provide better results, tree-based models such as Random Forest and Decision Trees offer more consistent accuracy. Models based on neural networks, such as LSTM, CNN, and BI-LSTM, perform well, with LSTM-based methods excelling in particular. The most successful strategy for classification problems is the model presented, which obtains the greatest accuracy, precision, recall, F1-score of 95%. The research aids in the development of advanced tools and methodologies to mitigate hate speech on social media and foster positive online interactions. Future research may investigate alternative deep learning architectures, such as transformers, to enhance hate speech detection efficacy. The advancement of interpretable AI methodologies for identifying hate speech and delivering transparent forecasts might enhance user confidence and facilitate better content moderation decisions.

We are living in the era of technology and internet is gift of technology. The advantage of internet is blessings but misuse of internet results into the headache. Online hate speech is one of the issues of the modern era. The legal mechanism is also going through big challenge regarding the protection of online hate speech in India. Section 66 A of the Information Technology Act, 2002, was struck down by the Hon’ble Supreme Court and it created challenge for the protection of online hate speech. We have seen number of incidence where fake news/objectionable materials/hate speech/rumors are put through computer resources and communication devices. It had spread hate and violence in the large number of cases. The researcher put light on the notion of hate speech and discuss in detail the international law, domestic law and judicial pronouncements. Civilized Society is facing enormous crisis to deal with online hate speech. The researcher is examining the existing legal mechanism on online hate speech with the relevant provisions of the law and judgments. Electronic evidence is vital to judge the online hate speech. The legal mechanism and judicial pronouncements on the electronic evidence are scrutinized in

The internet has become an everyday tool to communicate and network with people around the globe, but its perceived anonymity, availability, and instant access have made it an environment conducive to spreading hateful content and connecting to like-minded individuals with similar hateful ideologies. Hate speech and other prejudice-motivated behavior, however, need to be considered on a continuum of victimization, and "like other social processes, [be seen as] dynamic and in a state of constant movement and change, rather than static and fixed" (Bowling, 1993, p. 238). It is a social process that is marked by multiple, repeat, and constant victimization (Bowling, 1993), with victims no longer distinguishing between specific hateful events, and rather normalizing experiences of hateful conduct "as an everyday, unwanted but routine reality of being 'different'" (Chakraborti, 2016, p. 581). Understanding hateful behavior and victimization as a process allows us to connect "low-level" incidents of hateful behavior to the more serious and life-threatening incidents at the more extreme end of the spectrum (Bowling & Phillips, 2002). The Christchurch attacks in New Zealand and their link to hateful communication on the online platform 8chan is only one such example of how online hate speech and cyberhate can escalate to "in real life" attacks, leaving the online sphere and spilling into the offline world. As per Allport's (1954) scale of prejudice, more extreme forms of prejudice-motivated violence are founded on "lower level" acts of prejudice and bias, therefore, hateful content online should not be ignored. Intervening online to interrupt or counter hateful behavior already at the lower end of the scale of prejudice becomes important; online interventions which are to be identified and synthesized through this systematic review. Allport's (1954) scale of prejudice will be the basis for this systematic review. Early on, Allport (1954) asserted that individuals with negative attitudes toward groups are likely to act out on these prejudices "somehow, somewhere" (p. 14), and that the more intense such negative attitudes are, the more hostile the action will be. Allport (1954) put forward a scale of acts of prejudice to illustrate different degrees of acting out negative attitudes, a scale that starts with antilocution (or what we call hate speech), described as explicitly expressing prejudices through negative verbal remarks to either friends or strangers (Allport, 1954). Avoidance is the next level on the scale of prejudice, with people avoiding members of certain groups, followed by discrimination, where distinctions are made between people based on prejudices, which leads to the active exclusion of members from certain groups (Allport, 1954). This level of acting on prejudices is routed in institutional or systemic prejudices, for example, in the differential treatment of people within employment or education practices, but also within the criminal justice system, or through social exclusion of certain minority group members. Physical attack is the next level on the scale of prejudice, which includes violence against members of certain groups by physically acting on negative attitudes or prejudices. The last level is extermination, which is the ultimate act of violence against members of specific groups, an expression of prejudice that systematically eradicates an entire group of people (e.g., genocide or lynchings; Allport, 1954). Allport's (1954) scale of prejudice makes it clear how hate speech/cyberhate is connected to more extreme forms of violence motivated by specific prejudices and biases, with hate speech (or antilocutions) being only the starting point on a 5-point continuum (Bilewicz & Soral, 2020). The importance of this scale of prejudice is not only that it clearly illustrates a range of different ways and intensity levels to act out prejudices, but also the "progression from verbal aggression to physical violence or, in other words, the performative potential of hate speech" (Allport, 1954; Kopytowska & Baider, 2017, p. 138). This is where interventions at the lower level of the scale of prejudices, interventions targeting hate speech/cyberhate, become important. There is no universal definition of hateful conduct online, but there is some consensus that hate speech targets disadvantaged social groups (Jacobs & Potter, 1998). Bakalis (2018) more narrowly defines cyberhate as "any use of technology to express hatred towards a person or persons because of a protected characteristic—namely race, religion, gender, sexual orientation, disability and transgender identity" (p. 87). Another definition that also points out the ambiguity and challenges involved with identifying more subtle forms of hate speech, and also making reference to the potential threat of hate speech escalating to offline violence, is that put forward by Fortuna and Nunes (2018), who analyzed various definitions of hate speech "Hate speech is language that attacks or diminishes, that incites violence or hate against groups, based on specific characteristics such as physical appearance, religion, descent, national or ethnic origin, sexual orientation, gender identity or other, and it can occur with different linguistic styles, even in subtle forms or when humour is used" (p. 5). In this systematic review, we also distinguish hate speech/cyberhate specifically from other forms of harmful online activity, such as cyber-bullying, harassment, trolling or flaming, as perpetrators of such online behavior repeatedly and systematically target specific individuals to cause upset, to seek out negative reactions, or to create discord on the internet. In contrast, hate speech/cyberhate is more general and does not necessarily target a specific individual (Al-Hassan & Al-Dossari, 2019), instead hate speech/cyberhate heavily features prejudice, bias and intolerance toward certain groups within society. With the majority of hate speech happening online, interventions that take place online are an important way to challenge prejudice and bias, potentially reaching masses of people across the globe. The unique feature of the internet is that such individual negative attitudes toward minority groups and more extreme hateful ideology can find its way onto certain platforms and can instantly connect people sharing similar prejudices. By closing the social and spatial distance, the internet creates a form of collective identity (Perry, 2000, p. 123) and can convince individuals with even the most extreme ideologies that others out there share their views (Gerstenfeld et al., 2003). In addition, the enormous frequency of hate speech/cyberhate within online environments creates a sense of normativity to hatred and the potential for acts of intergroup violence or political radicalization (Bilewicz & Soral, 2020, p. 9). It is, therefore, important to challenge this hate speech epidemic (Bilewicz & Soral, 2020), especially since hate movements have increasingly crossed into the mainstream (Perry, 2000). With hate speech/cyberhate posing a threat to the social order by violating social norms (Soral et al., 2018), perceptions of social norms as either supporting or opposing prejudice has been found to have an influence on how individuals react online (Hsueh et al., 2015). Seeing other people post prejudiced (opposed to antiprejudiced) comments online can lead to the adoption of an online group's biases and can influence an individual's own perceptions and feelings toward the targeted stigmatized group (Hsueh et al., 2015). In addition, research around desensitization also suggests that being exposed to hate speech leads to desensitization, which further leads to an increase in outgroup prejudice toward groups targeted by such speech (Soral et al., 2018). With society increasingly recognizing that it is inappropriate to express prejudices in public settings, many interventions will include some form of social norms nudging to reduce such prejudices; interventions that "nudge behavior in the desired direction" (Titley et al., 2014, p. 60). Therefore, hate speech not only affects minority group members, but also has an influence on opinions of majority group members (Soral et al., 2018), which makes strategies that can elicit change in people's prejudice-related attitudes crucial (see, e.g., Zitek & Hebl, 2007). Governments around the world face increased demand for understanding and countering hateful ideology and violent extremism both online and offline (e.g., the Christchurch Call in New Zealand). The U.S. Government's 2011 CVE Strategy highlights the importance of ongoing research and analysis, the sharing of knowledge and best practices internationally, and the countering of hateful ideologies and propaganda (see also Department of Homeland Security, 2016, 2019). The goal of this systematic review is to use an integrated and interdisciplinary approach to examine the effectiveness of online campaigns and strategies for reducing hate speech and cyberhate. The internet also provides an opportunity to reach masses of people who have been exposed to hateful content and ideology online, therefore, this systematic review will focus on online interventions addressing online hate speech and cyberhate. The specific settings where we would expect to see the online interventions deployed will be on websites, text messaging applications, and online and social media platforms including, but not limited to, Facebook, Instagram, TikTok, WhatsApp, Google, YouTube, and Snapchat. As mentioned previously, many online interventions will be based on social norm nudges to reduce online hate. These interventions aim to change people's online behavior and encourage individuals or groups to conform to established social norms. The communication of social norms can happen through establishing community standards on online platforms themselves (e.g., Facebook, Twitter, etc.), through more formal online training courses, or through anti-hate speech/anti-cyberhate campaigns teaching people to recognize hate, embrace diversity, and stand up to bias. Such prevention campaigns are designed to challenge bias and build ally behaviors by supplying people with constructive responses to combat, for example, antisemitism racism, and homophobia, as well as provide resources to help people explore and critically reflect on current events. Other interventions may add messages to hateful online comments, counter hateful content or extremist ideology, or redirect people to more credible sources. Both peers and parents have been found to foster racial consciousness and identity development, define interracial relationships and cultivate ethnic heritage and culture (Hagerman, 2016). Socialization influences how children understand their group's social position and their membership within that group by providing an understanding of racial, religious, and sexual privilege (Bowman & Howard, 1985). Socialization often reflects peers' and parents' experiences with racism, discrimination, and their ideological perspectives about race, religion, or sexuality (Umaña-Taylor & Fine, 2004). This is important because peers and parents who feel discriminated against or believe that the "other" is a threat may impart their prejudices to their children or friends, which could lead them to interpret the social world with similar discriminatory views and/or behavior. Individuals who feel socially alienated or rejected are especially vulnerable to such socialization practices as they feel that adopting these views will provide them with a sense of acceptance and belonging (Leiken, 2012). Regardless of how an individual develops certain racial, religious, or sexual biases, the online interventions under review are expected to target and reduce the production of original hateful content such as antisemitic Tweets and/or homophobic blog posts as well as the consumption of hate speech material (e.g., watching or reading hate speech videos or blogs). For example, some interventions take a rather broad messaging approach by implementing racial sensitivity and diversity training through Public Service Announcements, peer-to-peer dialogue workshops, or films that provide opportunities for youth and adults to self-reflect and learn about historical oppression, people of color, women, and the LGBTQIA+ community from credible sources. The factual understanding of diverse groups is often supplemented by experiences with people within the group. These educational programs often identify a cultural guide who is willing to introduce youth to new experiences and who can aid in processing thoughts, feelings, and behaviors. These interventions intend to dispute and contradict negative stereotypes associated with specific cultures, people, and institutions by sharing different points of view based on human rights values such as openness, respect for difference, freedom, and equality (Gomes, 2017). Moreover, such interventions tend to involve blanket bans on specific behaviors enforced through the public promotion of norms or individual sanctions enforced by moderators. Other interventions, such as the "Redirect Method," are narrower in their messaging. These interventions generate curated playlists and collections of authentic content that challenge hate speech/cyberhate narratives and propaganda (Helmus & Klein, 2018). For instance, people who are directly searching for extremist content online may be linked to videos and written content that confronts such claims. These videos are designed to be objective in appearance instead of containing material that explicitly counters extremist propaganda. The underlying goal of this type of interventions is to provide credible content that effectively undermines extremist messaging but does not overtly attack the source of propaganda. In addition to confronting hate speech narratives, these interventions provide users with links to numerous social services such as anger management training, drug and alcohol treatment, and mental health resources. Online platforms, such as Twitter and Facebook, have also started to employ a similar method, redirecting people who comment on or share "fake news" or conspiracy theories, which often are fraught with prejudicial undertones and are harmful to minority groups, to more credible content and news sources. The aforementioned interventions are designed to counter-balance these biased perceptions (e.g., unsupported claims of the Black community as criminal or the LGBTQIA+ community as pathologized) Blacks as criminals, LGBTQIA+ as pathologized) by blunting the occurrence of racist discourse and reducing the likelihood these individuals will internalize and normalize racial, religious, and/or sexual prejudices (Qian et al., 2019). Being in new situations is uncomfortable and often awakens fears and apprehensions that can block our experiential development. Acquiring information or being exposed to minority-run businesses, poverty, and writings from minority authors allows a person to understand the thoughts, hopes, fears, and aspirations of the people outside their racial perspective rather than from the perspective of the majority society (Dunham et al., 2013; Lee et al., 2017). Doing so, counters racist programming by challenging hegemonic beliefs, which can lead to the acceptance of tolerant attitudes and the reduction of hateful expressions online. Findings from the proposed review will enhance our understanding of the effectiveness of online anti-hate speech/anti-hate interventions, will help ensure that programming funds are dedicated to the most-effective efforts, and will play a critical role in helping individual programs improve the quality of service provisions. It will inform governments and policymakers of the current state of such online efforts, what works and which modes of interventions to implement, and help guide economically viable investments in nation-state security. Our search of the scholarly literature identified one review, Blaya (2019), as similar to the proposed topic. Blaya's (2019) review, however, focused on the prevalence, type, and characteristics of existing interventions for counteracting cyberhate and did not include a meta-analysis. Two other similar reviews focused on exposure to extremist online content (Hassan et al., 2018) and communication channels associated with cyber-racism (Bliuc et al., 2018). A search of the Campbell Library using key terms (hate OR radical*) returned two protocols and one review identified for further inspection to assess potential overlap. The protocols include "Psychosocial processes and intervention strategies behind Islamist deradicalization: A scoping review" by de Carvalho et al. (2019) and "Police programs that seek to increase community connectedness for reducing violent extremism behavior, attitudes and beliefs" by Mazerolle et al. (2020). A further review on a similar topic is a recently completed Campbell review (January 2020), "Counter-narratives for the prevention of violent radicalization: A systematic review of targeted interventions" by Carthy et al. (2018) at the National University of Ireland, Galway. Our proposed review is distinguished from the de Carvalho et al. (2019) review in that we are focusing on hate speech and cyberhate generally without delimiting our approach to a specific type of radicalization (e.g., Islamist). Furthermore, we are electing to complete a systematic review and meta-analysis. Likewise, the protocol by Mazerolle et al. (2020) focuses on interventions involving police officers either as initiators, recipients, or implementers of community connectedness interventions. Our review will focus specifically on any online intervention, which may or may not involve police, but police will not be the focus nor be the basis of the online intervention strategy. Judging from Carthy et al. (2018) protocol, we anticipate our review will also capture counter-narrative interventions, but will differ based on setting, timing, and scope of interventions. Specifically, we are interested in online interventions that extend beyond counter-messaging campaigns to include a broad array of interventions outlined above and extend beyond radicalization to include everyday hate and prejudice. In addition to conducting a meta-analysis, the proposed review would build on Blaya's (2019) work by expanding the population parameters to include both adolescents as well as adults. Blaya (2019) limited her search to include interventions aimed toward youth, young people, children, young adults, adolescents, children, and teenagers and did not focus on extremism. The main objective of this review is to synthesize the available evidence on the effectiveness of online interventions aimed at reducing the creation and/or consumption of online hate speech/cyberhate material. To what extent are online interventions effective in reducing online hate speech/cyberhate? How is effectiveness related to the type of online hate speech/cyberhate intervention used? How is effectiveness related to the characteristics of individuals experiencing the online hate speech/cyberhate intervention (e.g., age, gender, race/ethnicity, offense history, childhood trauma)? Both experimental and quasi-experimental quantitative studies will be included. These study designs will address research questions #1 to #3. Eligible quantitative study designs include the following: Eligible experimental designs must involve random assignment of participants to distinct treatment and control group(s). Designs that involve quasi-random assignment of participants such as alternate case assignment are also eligible and will be coded as experimental designs. All eligible quasi-experimental designs must include a comparison group of participants compared to participants in the treatment condition. Eligible studies include those that report matching procedures (individual- or group-level) and statistical procedures employed to achieve equivalency between groups. Statistical procedures may but are not limited to, analysis, and Furthermore, in of a limited quantitative evidence we will also include quasi-experimental studies with comparison groups that provide of for both groups. will also be included. Eligible include designs with a control group and designs with or without a control group than quasi-experimental designs include studies that a comparison group of participants who either to in the study or who in a but out to the of a Eligible comparison include other online interventions or in which participants not or an online Both youth and participants of any gender sexual orientation, or will be eligible for this review. The eligible youth population will be study participants with a of through The eligible population will be study participants with a of and in which only a of the is eligible for example, a study in both online and offline hate speech be not anticipate studies based on as our will be and we will take to studies that only online interventions. will of the of a study for through and be we will elicit the of a the of and studies will be these studies will be they will be and be in the and any related Blaya's (2019) of intervention strategies to the potential of eligible interventions. The intervention is the of responses to hate speech/cyberhate, which includes the countering of violent extremism and to address online interventions that are eligible range from hateful content online specific (e.g., of social media to to online hate using targeted strategies (e.g., through hateful of studies focusing on online include the and of online and content online content and & 2018), hateful online comments to comments et al., 2018), and to users out of online are also interested in interventions such as the of 8chan this online platform linked to "in real life" attacks in New Zealand and the and interventions that further hateful online content and radicalization similar events. hateful content online such has up speech as well as around online users and hateful groups on to other online to hateful content online using targeted strategies therefore, been as an effective online include using the from & 2020), the use of to online responses to in online where hate speech has been (Qian et al., 2019), and redirecting online users to videos for example, Our systematic review will include a range of online interventions, many of which have only recently Two other strategies identified by Blaya (2019) are the and of hate speech/cyberhate using technology as well as the creation of online and These interventions include online counter-narrative the and/or use of online counter online interventions, online training, and online narrowly to address extremist ideologies and hate speech that incites targeted violence and In such interventions seek to or the occurrence of violent extremism or the of hate speech and extremist by channels and opportunities to such groups. The and intervention eligible for this systematic review educational programs for example, provide people with online and challenge 2019). will include online programs with an online (e.g., and and educational and online interventions. of these interventions may by individuals no longer in the creation and/or consumption of cyberhate and extremist material online. These online interventions may be by and internet service or or in the case of interventions. The comparison may be routine exposure and to hate speech/cyberhate or online The of is the creation and/or consumption of hateful content online. By we to the production and of original hateful content such as antisemitic racist and/or homophobic blog The consumption of hate speech material may include or being a of a hate watching or reading hate speech videos or being a target of online hate speech/cyberhate, or hate speech material. of include and of study participants such as and attitudes toward hate Eligible studies must report a or (or to be included. There will be no exclusion on the source of for the and can be from any institutional or completed by will include any of from strategies to increase the scale of of potentially effective anti-hate speech and interventions for These could include to or to the creation of and behaviors. can also include such as a of hate speech/cyberhate to other platforms instead of a reduction of hate All described in eligible studies will be in the will focus on the between and the current The starting with the when the internet to a and community et al., are for an approach in the lower end of our search to the may be it is hate speech/cyberhate online through or and some studies may capture Our population of studies will also be limited to studies in and but of studies completed in any as we are focused on online content that can be and across and nation-state The language parameters reflect the language of the review Our will where studies the of study in will be between the members of the review These will be and as a from the protocol in the review. In the of a change in we will search online OR OR internet OR Twitter OR OR 8chan OR OR OR OR OR OR OR OR OR OR speech" OR cyberhate OR OR OR OR OR speech OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR OR peer-to-peer OR OR OR

Minority members are often targeted by online hate speech. In this article, we advance the literature by conceptualizing online hate speech as a social identity threat, whose consequences can adversely affect intergroup relations at large. Within two experiments, we examine the effects of homophobic (Study 1, N = 225) and racist (Study 2, N = 194) hate speech on target minority members’ attitudes toward the majority group and intentions to avoid it. In addition, we test the potential mitigating role of counter-speech presence and source (minority vs. majority member). Drawing from theoretical accounts of social identity and perceived opinion climate, we also shed light on novel underlying mechanisms that explain the effect of online hate speech on intergroup relations: perceived identity threat (PIT) and perceived negative representation (PNR). Across both studies, more severe hate speech resulted in greater PIT and PNR, which was related to more hostile attitudes toward the majority group and/or intentions to avoid majority members. Counter-speech was largely ineffective in alleviating these negative outcomes. However, Study 2 revealed that counter-speech from a majority member could mitigate PIT, but only for less severe forms of hate speech. We discuss theoretical and practical implications for research in media psychology.

Recent research shows that online hate-speech is on the rise in western societies, including in Norway. This submission builds on a broader 4-year research initiative on: “Violence-inducing Behavior Prevention in Social-Cyber Space of Local Communities” aimed at gaining deeper understanding of the complexities surrounding online hate as well as developing radical technological solutions (i.e., real-time artificial intelligence tool) to support authorities in their work against the vice. This paper addresses the former with a focus on the work of the police as one of several other community resilience stakeholders engaging in the fight against online hate-speech. The paper posits a socio-cultural, technological, and ethical analysis of the challenges, posed particularly in the enforcement of legislation on hate-speech (paragraph 185). Reflections are also included on the implication of the findings for AI development for community resilience against online hate speech.

There is ample evidence that online hate speech in Pakistan is directed against women, religious minorities, journalists, voices of dissent and activists. The targeting of many of these groups is an expansion online of the traditional hostility and abuse they face offline. However, the internet has made such abuse easier and online hate speech is growing as internet use rises in the country. Those responsible vary somewhat: women and religious minorities are typically targeted by religio-political parties and their followers, while journalists and activists are often targeted by government/the military. In all cases, online hate speech can have a serious offline impact, including physical violence, and restrictions on people’s freedom/ability to work/post online. This review, looking at online hate speech in Pakistan in relation to particular groups, draws largely on reports by think-tanks/NGOs as well as media articles and blogs. Relatively little academic literature was found on the subject, but grey literature was quite extensive, especially on certain religious minorities (Ahmadis) and women.

Taking a neo-Kohlbergian approach, we explore the moral reasoning of 486 young Italian users of social network sites exposed to moral dilemmas concerning online hate speech. The aims are to understand what moral reasoning schemas they use as they face homophobic, racist, or sexist online hate speech, and what influence personal values and moral disengagement might have on their moral reasoning process. The results reveal the prevalence of Maintaining Norms reasoning (conformity to rules and authority) in making moral decisions concerning online hate speech and confirms the mediating role of Hate Speech Moral Disengagement in the relationship between personal values and the moral reasoning process.

Most adolescents and young adults frequently encounter hate speech online. Although online bystander intervention is essential to combating such hate, young bystanders may need support with initiating interventions online. Thus, to illuminate the factors of young bystanders’ intervention, we conducted a nationwide, quota-based, quantitative online survey of 1180 young adults in Germany. Among the results, perceived personal responsibility for combating online hate speech positively predicted online bystanders’ direct and indirect intervention. Moreover, frequent exposure to online hate speech was positively associated with bystander intervention, whereas, a perceived threat or low self-efficacy reduced the likelihood of intervention. Also, a greater acceptance of negative consequences and being educated about online hate speech through peers or campaigns all positively predicted some direct and indirect forms of online bystander intervention.

To what extent did online hate speech and white nationalist rhetoric on Twitter increase over the course of Donald Trump’s 2016 presidential election campaign and its immediate aftermath? The prevailing narrative suggests that Trump’s political rise — and his unexpected victory — lent legitimacy to and popularized bigoted rhetoric that was once relegated to the dark corners of the Internet. However, our analysis of over 750 million tweets related to the election, in addition to almost 400 million tweets from a random sample of American Twitter users, provides systematic evidence that hate speech did not increase on Twitter over this period. Using both machine-learning-augmented dictionary-based methods and a novel classification approach leveraging data from Reddit communities associated with the alt-right movement, we observe no persistent increase in hate speech or white nationalist language either over the course of the campaign or in the six months following Trump’s election. While key campaign events and policy announcements produced brief spikes in hateful language, these bursts quickly dissipated. Overall we find no empirical support for the proposition that Trump’s divisive campaign or election increased hate speech on Twitter.

The definition of “on-line hate speech” covers all forms of expression that propagate, incite, promote or justify hatred based on intolerance, including that expressed in the form of discrimination and hostility against minorities. Moreover, the concept of hatred includes other sub-concepts such as Homophobia, Racism, Chauvinism, Terrorism, Nationalism, Tolerance/Intolerance, and so on. Specifically, on-line hate speech is used in cases of cyber-harassment, to harm others deliberately, repeatedly and aggressively, in a way so to weaken victims psychologically. To contrast this phenomenon, EC has allocated a relevant amount of H2020 funds for the completion of specific research projects, the goal of which is the construction of computer tools to locate, evaluate and eventually block on-line hate speech. Today, the automatic tackling of online hatred is a daily-performed operation on Social Forums like Facebook, Twitter and Instagram. However, the algorithms these Social Forums use are stochastic/statistical, therefore not suitable to contextualize syntactically and semantically the words used inside posts. Therefore, with on-line hate speech tackling, statistical algorithms may produce inaccurate or even false results, with rather serious consequences.

Purpose The purpose of this paper is to elaborate the opinions and perceptions of internet users regarding online hate speech, and bring cyberhate to the attention of internet users and policy stakeholders. Design/methodology/approach A sectional research design and survey method was adopted throughout the study to examine the opinions and perceptions of internet users regarding cyberhate by descriptively exploring the existing situation from various perspectives. The participants of the study were determined by purposive sampling methods to attain maximum variety among internet users who are considered as highly literate in technology use. The data were collected through a personal data form and a survey (Cyberhate Perception Scale) from 372 internet users living in Turkey and the USA. Findings The findings of the study revealed that the majority of participants do not perceive cyberhate as a part of freedom of speech and they believe that online hate behaviors, which they also consider to be a violation of human rights, should be deemed illegal and be punished accordingly. The findings, which were discussed in line with the existing research, indicated some significant predictors of internet users’ perceptions on cyberhate. Originality/value As cyberhate is an understudied area that raises concerns in terms of internet user exposure, the objective of this research is to understand tendencies about the opinions and perceptions of internet users regarding online hate speech, and bring cyberhate to the attention of internet users and policy stakeholders.

The rapid expansion of digital technologies has transformed modes of communication and social interaction, while also facilitating harmful phenomena such as online hate speech. This paper examines the dynamics, targets, and consequences of online hate speech, with a focus on its impact on the Roma minority in Greece. Drawing on existing literature and prior empirical research, the study identifies recurrent negative constructions of Roma in social media discourse, ranging from stereotypes of criminality and dependency to explicit calls for exclusion and elimination. The analysis highlights the profound psychological, social, and civic consequences of such rhetoric, including anxiety, depression, self-ghettoisation, polarisation, and the normalisation of intolerance. The paper argues that conventional counter-speech strategies, while necessary, are insufficient to disrupt entrenched discriminatory narratives. Instead, it proposes a multidimensional framework designed to enhance critical engagement, foster empathy, and build digital resilience among both Roma and non-Roma populations. This framework emphasizes collaborative learning environments, culturally responsive pedagogy, and the development of competencies to manage and challenge harmful online discourses, as well as to promote inclusive civic engagement. By addressing both individual and structural dimensions, the study underscores the urgency of multi-level strategies to counteract the glocal hostility—where globalized hate speech tropes intersect with local socio-cultural prejudices.

Nowadays, adolescents have extensive access to Information and Communication Technologies, which allow them to engage in social networking activities that may expose them to Online Hate Speech (OHS). While there are few cross-sectional studies about the effects of OHS Exposure on attitudes and aggressive behavior, no study aims to analyze the tendency to Speak Up when exposed to certain content (e.g., reporting, etc.). In addition, no instruments have yet been validated to assess these constructs. The aim of the present study, focused on Online ethnic Hate Speech (OeHS), is double: (a) develop a scale to measure OeHS Exposure and the tendency to Speak Up and analyze its psychometric properties; (b) analyze the longitudinal association between Xenophobia (XEN), OeHS Exposure, and Speaking Up against OeHS, while taking into account gender differences and the nested nature of the data. Six hundred sixty-six Italian high school students (52.7 percent male; MAge = 15[0.64]), nested in 36 ninth grade classes (10 schools), took part in the longitudinal study. The first wave of data collection occurred in early 2020, before the COVID-19 pandemic. The second and third waves took place 12 and 15 months later, respectively. Findings suggest that the OeHS Scale has good psychometric properties. Moreover, according to the findings, while the three variables of interest are always cross-sectionally correlated, a longitudinal negative association have been found between XEN and both Exposure and Speaking Up. Regarding the impact of OeHS Exposure, the good news is related to the absence of a longitudinal association with both XEN and Speaking Up.

<p><em>Facebook has become one of the fastest growing social media platforms. At the end of 2013, Facebook had 1,23bn monthly active users and 757 million daily users who log onto Facebook. Within this online space, there are also a growing number of online virtual communities, and hate groups who are using this space to share a violent, Islamophobic and racist narrative which attempts to create a hostile virtual environment. It is important to analyse these ‘new’ communities by monitoring the activities they conduct, because the material they post, potentially can have a damaging impact on community cohesion within society. Moreover, as a result of recent figures that show an increase in online anti-Muslim abuse, there is a pertinent need to address the issue about Islamophobia on social media. This research examined 100 different Facebook pages, posts and comments and found 494 instances of online hate speech directed against Muslim communities. The findings revealed some interesting parallels and common characteristics shared within these groups, which helped the author to create a typology of five characteristics of anti-Muslim hate espoused on Facebook. Overall, this study found Muslims being demonised and vilified online which had manifested through negative attitudes, discrimination, stereotypes, physical threats and online harassment which all had the potential to incite violence or prejudicial action because it disparages and intimidates a protected individual or group.</em></p>

Globalization and its effects became key objects of concern in the late 1990s. Politicians, journalists and academics shared this concern, often expressed as a requirement for ‘less state and more market’ in the new global world. Most failed, however, to support their arguments with evidence. But in recent years, academic social scientists have sought to subject the notion of globalization and consideration of its effects to more balanced and empirically supported analysis. Various studies have also pointed to significant changes in welfare states in the 1980s and 1990s, especially in Europe, some even suggesting the demise of the classic European social model. Whilst there is an ever-growing literature on globalization,1 matched by a similar, if somewhat less rapidly burgeoning literature on welfare state change,2 comparatively little has been written on the interaction between the two.3 The key question with which this book concerns itself brings these two areas of study together and is: How far, and in what ways, has the process of globalization been implicated in recent changes to European welfare states? This question is deceptively simple and needs to be unpacked into a number of contributory questions and issues for analysis. What, exactly, is the globalization process, and which of its multiple aspects are most likely to affect national welfare states? What have been the most significant changes in European welfare states in recent years? What factors, besides globalization, have been implicated in such changes? Last, and not least, how far do these same welfare state changes affect and, indeed, mould the globalization process?