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This chapter is about online hate speech propagated via platforms operated by social media companies (SMCs). It examines the options open to states in forcing SMCs to take responsibility for the hateful content that appears on their sites. It examines the technological and legal context for imposing legal obligations on SMCs, and analyses initiatives in Germany, the United Kingdom, the European Union and elsewhere. It argues that while SMCs can play a role in controlling online hate speech, there are limitations to what they can achieve.

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.

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.

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.

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.

Social media (SM) is a new communication tool that substantially contribute to facilitating online hate speech (OHS). In emphasis of the question “what role can SM play in an individual’s life?”, this study aims to understand Bangladeshi university students’ personal experiences and opinions of OHSs related to SM.

The authors used an online survey method to collect data and retrieved responses from 410 students. Mann–Whitney U test, Kruskal–Wallis test and Spearman’s rank correlation analysis were used to test the hypotheses.

This study found that hate speech is a familiar term among students. Students’ political views or opinions, religion and gender have become the most targeted instruments for OHSs. Comparing students’ use of SM, the authors found that Facebook was the most used SM site to spread hate speech in Bangladesh. In terms of personal experiences, the findings indicated that 45.6% of students became victims of OHSs at least once or more times, and the majority of students tended to simply avoid OHSs. Another significant finding was that OHS has real-life effects on the students, resulting in various personal and psychological distress.

Although some research has been conducted on hate speech at the local level, to the best of the authors’ knowledge, no study has focused on the student community. To the best of the authors’ knowledge, this study is the first attempt in Bangladesh to focus on OHSs from a student’s personal viewpoint.

Online hate speech (OHS) is becoming a serious problem worldwide including in Bangladesh. This study aims to understand the nature of OHS against religious groups and explore its impact on their social life and mental health.

A qualitative approach was followed and 11 in-depth interviews (IDIs) were conducted with the selected OHS victims. This study conducted a semi-structural interview using Google Form following the design questionnaire for selecting IDIs participants.

This study found that religious minorities experience online hatred through online media by the major religious group in Bangladesh. Natures of OHS are commenting on social media posts, sharing hateful memes and sending private messages using slang language targeting religious identity, religious food habits and ethnic identities. Victims were offended, abused and bullied by unknown persons, their university friends and colleagues. Victims of OHS did not take any action against it due to fear of insecurity. Victims of OHS felt low-minded, helpless and anxious after the experience of OHS; they felt more insecure and vulnerable socially and mentally.

The findings of this study suggest that policymakers identify the nature of OHS and take proper steps for reducing the frequency of OHS in Bangladesh. To combat the OHS, authorities have to make legal enforcement equal for everyone.

Purpose – This chapter investigates if Ronald Aker’s Social Structure Social Learning (SSSL) theory can help explain who is involved with the production of online materials considered hateful or extremist.

Methodology/Approach – After discussing how SSSL can account for becoming exposed to online extremism and then becoming involved in its production, the authors conduct a logistic regression on data from 1,008 American adults that predicts if they produced online hate materials with variables derived from SSSL.

Findings – Results strongly support SSSL. While structural factors such as the respondents’ differential social organization, differential social location, and differential location in the social structure predict production of online hate materials, the effect of these factors is largely mediated once social learning variables are included in the model. Specifically, the respondents’ general definitions related to violence, specific definitions related to hate speech, and differential association accounts for variation in the production of online hate materials.

Originality/Value – This research contributes to the literature in two primary ways: (1) the authors investigate a critical, yet understudied, factor involved in the radicalization process; and (2) the authors demonstrate that a leading criminological theory applies to this form of deviance. This research also suggests key variables for creating strategies for countering violent extremism.

The socializing of hate and its saturation on platforms as a resonant and emotional connection online reveal the networked nature of convergent platforms which pump hate as a mechanism of connection and fracture in society in the post-digital age. The violence of hate and negative sentiments online morph to appropriate a multitude of manifestations from cyberbullying and revenge porn to trolling and memes as subversive, denigrative humour. Social media, designed through an architecture for sharing and transaction, distributes hate as a popular sentiment, building connections with disparate communities through the articulation of hate for fellow humans and humanity at large. Trauma induced through hatred and bullying as an active aspect of social media platforms and interactivity distribute sentiments through its excess and disproportionality. This chapter interrogates the sentiment of hate and its workings on social media as a technology of trauma in distributing hate as a form of communion.

Analyse the presence of hate speech in society, placing special emphasis on social media. In this sense, the authors strive to build a formula to moderate this type of content, in which platforms and public institutions cooperate, from the fields of corporate social responsibility and public diplomacy, respectively.

To this aim, it is important to focus efforts on the creation of counter-narratives; the establishment of content moderation guidelines, which are not necessarily imposed by unilateral legislation; the promotion of suitable scenarios for the involvement of civil society; transparency on the part of social media companies; and supranational cooperation that is as transnational as possible. To exemplify the implementation of initiatives against hate speech, two cases are analysed that are paradigmatic for assuming two effective approaches to the formula indicated by the authors.

The authors analyse, in the case of the European Union, its “Code of conduct to counteract illegal online hate speech”, which included the involvement of different social media companies. And in the case of Canada, the authors discuss the implementation of the bill to include a definition of hate speech and the establishment of specific sanctions for this in the Canadian Human Rights Act and the Canadian Penal Code.

The case of the European Union was a way of seeking consensus with social media companies without legislation, while the case of Canada involved greater legislative and penalisation. Two ways of seeking the same goal: curbing hate speech.

The prevalence of online hate material is a public concern, but few studies have analyzed the extent to which young people are exposed to such material. This study investigated the extent of exposure to and victimization by online hate material among young social media users.

The study analyzed data collected from a sample of Finnish Facebook users (n = 723) between the ages of 15 and 18. Analytic strategies were based on descriptive statistics and logistic regression models.

A majority (67%) of respondents had been exposed to hate material online, with 21% having also fallen victim to such material. The online hate material primarily focused on sexual orientation, physical appearance, and ethnicity and was most widespread on Facebook and YouTube. Exposure to hate material was associated with high online activity, poor attachment to family, and physical offline victimization. Victims of the hate material engaged in high levels of online activity. Their attachment to family was weaker, and they were more likely to be unhappy. Online victimization was also associated with the physical offline victimization.

While the online world has opened up countless opportunities to expand our experiences and social networks, it has also created new risks and threats. Psychosocial problems that young people confront offline overlap with their negative online experiences. When considering the risks of Internet usage, attention should be paid to the problems young people may encounter offline.

This study expands our knowledge about exposure to online hate material among users of the most popular social networking sites. It is the first study to take an in-depth look at the hate materials young people encounter online in terms of the sites where the material was located, how users found the site, the target of the hate material, and how disturbing users considered the material to be.

The purpose of this paper is to explore internet users’ experiences of cyberhate in the aspects of ethnicity, religion, sexual preference and political perspective.

Researchers employed an exploratory survey method to examine internet users’ experiences of cyberhate. The participants of the study were determined by purposive sampling methods to attain maximum variety among internet users with high-level digital literacy skills. The data were collected from 355 internet users affiliated with two universities in Turkey and the USA using a personal data form and a survey (Cyberhate Experience Survey) of which reliability and validity indexes were ensured.

The results indicated that participants have observed and experienced cyberhate at different levels targeting their ethnic, religious, gender-based and political identities. The findings also pointed out that gender, income and socio-political identities are significant variables on exposure to cyberhate regardless of cultural and educational boundaries. The majority of survey respondents reported that they had encountered cyberhate mostly on social media platforms.

The findings of the study imply that to address the hate speech problem comprehensively, the author must enlighten people, change their way of thinking and broaden their perspectives by using measures such as intercultural dialogue, critical thinking, media literacy, education on tolerance and diversity.

This research was intended to contribute into the need to elaborate on various aspects of cyberhate, which is shared by academics, thinkers, journalists and educators. It may also serve to clarify how frequently internet users encounter hateful content and harassment online, which can have social consequences and influence young people’s trust to other people.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.