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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 present a quantitative and qualitative synthesis of the diverse academic proposals and initiatives for preventing and eliminating hate speech on the internet.

The foundation for this study is a systematic review of papers devoted to the analysis of hate speech. It has been conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol and applied to an initial corpus of 436 academic texts. Having implemented the suitability, screening and inclusion criteria, this corpus was refined to a sample of 74 articles.

The main subject categories studied in this corpus of academic research are legal issues and social media. In the majority of the articles, the use of hate speech via social media is associated with five typologies: religion, cyber racism, political slurs, misogyny and attacks on the LGTBI community. The absence of ethical reflection is one of the major shortcomings of IT-focused research and analysis devoted to online hate speech.

To date various systematic reviews have been presented, and they focus on detecting or describing hate speech. These have used either the search appraisal synthesis analysis framework or the Cochrane network. The PRISMA protocol was applied for this study, and both Scopus and texts in German were included. To date no major, rigorous systematic review has been undertaken of proposals to combat hate speech.

The link between hate speech and poverty has not been studied in depth within the academic sphere. Tolerance and ethical compassion are not granted the attention they merit when it comes to analysing the phenomenon of hate speech.

Purpose – This chapter has three general purposes: to trace Canada’s hate speech laws from their policy inception to their current state; to identify the importance that media and mass communication have played in the creation and development of Canada’s hate speech laws; and to demonstrate the critical relationship that media has had to significant legal cases on hate speech. Methodology/Approach – This chapter historically maps the policy development of and legal challenges to Canada’s hate speech laws. It takes directed notice of the relationship of media and mass communication to the development and implementation of those laws. It engages with libertarian and egalitarian arguments on free speech throughout the chapter testing these ideas through an examination of the legal cases cited. Findings – Canadian legislators and courts have long grappled with the balancing of rights with respect to the issue of “hate speech.” Advances in mass communication technology have added intricate challenges to that legal balancing. Awareness of media’s allure to hatemongers and racial extremists and of media’s protean characteristics make regulation of its hateful content a continuous legal challenge. Canada’s greatest challenge yet to the regulation of hate speech will be its adaptive response to the growing phenomenon of online hate. Originality/Value – This chapter highlights the little recognized prescient statements made by the Cohen Committee about the allure of media and the dangers of its technological advancements in Canadian free speech debates. Providing a comprehensive survey of Canada’s “hate speech” laws, it recognizes the importance that advancements in mass communication have played in the creation and development of Canada’s “hate speech” laws.

Purpose – This chapter demonstrates the power that Google, Apple, Facebook, Amazon and Microsoft (or the “GAFAM”) exercise over platforms within society, highlights the alt-right’s use of GAFAM sites and services as a platform for hate, and examines GAFAM’s establishment and use of hate content moderation apparatuses to de-platform alt-right users and delete hate content. Approach – Drawing upon a political economy of communications approach, this chapter demonstrates GAFAM’s power in society. It also undertakes a reading of GAFAM “terms of service agreements” and “community guidelines” documents to identify GAFAM hate content moderation apparatuses. Findings – GAFAM are among the most powerful platforms in the world, and their content moderation apparatuses are empowered by the US government’s cyber-libertarian approach to Internet law and regulation. GAFAM are defining hate speech, deciding what’s to be done about it, and censoring it. Value – This chapter probes GAFAM’s hate content moderation apparatuses for Internet platforms, and shows how GAFAM enable and constrain the alt-right’s hate speech on their platforms. It also reflexively assesses the politics of empowering GAFAM to de-platform the alt-right.

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.

This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language.

This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW).

Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus.

The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.

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.

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.

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.

The Hon’ble Supreme Court of India, in a landmark judgment, scrapped a draconian law [Section 66 (A)] that gave the police absolute power to put behind bars anybody who was found posting offensive or annoying comments online. This paper aims to examine the take of people on the “Free Speech via Social Media” issue and their attitude towards the way sensitive messages/information are posted, shared and forwarded on social media, especially, Facebook.

The research was carried out on a sample of 200 social media users, all picked up randomly, from five Indian states/Union Territories. Data were collected through a questionnaire, and users were contacted through e-mail. Data collected were analyzed through the Kolmogorov–Smirnov (K-S) Z test.

The findings indicate that hate posts/messages are on the rise, and more and more users are joining in. Besides, prosecution happens only when the aggrieved party is influential or powerful.

The findings of this research give a strong insight into the social media behaviour of users in relation to hate contents/posts. The study establishes the fact that Indian people are in favour of free speech, but with a sense of restraint and responsibility. The work could form the basis for future research on various aspects of hate speech on social media. Researchers could study the trials and prosecutions that have happened over the past few years and whether punishment has acted as a deterrent.

The research is likely to be important for those involved in work on freedom of speech or hate speech through social media. Social networking sites such as Facebook would also get some insights into users’ perception towards free and hate speech mechanism on social media.