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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.

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.

The present study was designed to investigate eight research questions that are related to the analysis and the detection of dialectal Arabic hate speech that targeted African refugees and illegal migrants on the YouTube Algerian space.

The transfer learning approach which recently presents the state-of-the-art approach in natural language processing tasks has been exploited to classify and detect hate speech in Algerian dialectal Arabic. Besides, a descriptive analysis has been conducted to answer the analytical research questions that aim at measuring and evaluating the presence of the anti-refugee/migrant discourse on the YouTube social platform.

Data analysis revealed that there has been a gradual modest increase in the number of anti-refugee/migrant hateful comments on YouTube since 2014, a sharp rise in 2017 and a sharp decline in later years until 2021. Furthermore, our findings stemming from classifying hate content using multilingual and monolingual pre-trained language transformers demonstrate a good performance of the AraBERT monolingual transformer in comparison with the monodialectal transformer DziriBERT and the cross-lingual transformers mBERT and XLM-R.

Automatic hate speech detection in languages other than English is quite a challenging task that the literature has tried to address by various approaches of machine learning. Although the recent approach of cross-lingual transfer learning offers a promising solution, tackling this problem in the context of the Arabic language, particularly dialectal Arabic makes it even more challenging. Our results cast a new light on the actual ability of the transfer learning approach to deal with low-resource languages that widely differ from high-resource languages as well as other Latin-based, low-resource languages.

This research is a critical discourse analysis (CDA) of Trump's speech on January 6, 2021, which results in his supporters' storming the US Capitol in order to challenge certifying Biden's victory. The Democrats accused Trump of incitement of insurrection. Consequently, Trump was impeached. This article investigates Trump's speech to label it as hate speech or free speech.

Analytical framework is tri-dimensional. The textual analysis is based on Halliday's notion of process types and Huckin's discourse tools of foregrounding and topicalization. The socio-cognitive analysis is based on Van Dijk's ideological square and his theory of mental models. The philosophical dimension is founded on Habermas's theory of discourse. These parameters are the cornerstones of the barometer that will be utilized to reach an objective evaluation of Trump's speech.

Findings suggest that Trump usually endows “I, We, You” with topic positions to lay importance on himself and his supporters. He frequently uses material process to urge the crowds' action. He categorizes Americans into two conflicting poles: He and his supporters versus the media and the Democrats. Mental models are created and activated so that the other is always negatively depicted. Reports about corruption are denied in court. Despite that, Trump repeats such reports. This is immoral in Habermas's terms. The study concludes that Trump delivered hate speech in order to incite the mob to act in a manner that may change the election results.

The study is original in its tri-dimensional framework and its data of analysis.

The purpose of this article is to draw attention to how harmful and inaccurate discourses pertaining to disaster responsibility is produced, the negative implications such narratives pose and the role of the media in the ways in which discourses about queerness and disaster are reported.

Throughout this paper, the authors detail examples of media reporting on discourses relating to people with diverse sexual orientation, gender identity, gender expression and sex characteristics (SOGIESC) being blamed and held responsible for disasters across the world. The authors examine the value of such reporting as well as describing the harm blame narratives have on queer people and communities.

There is little value in reporting on accounts of people publicly declaring that people with diverse SOGIESC are to blame for disaster. More sensitivity is needed around publishing on blame discourses pertaining to already marginalised communities.

This article contributes to the developing scholarship on lesbian, gay, bisexual, transgender, queer, intersex, agender, asexual and aromantic individuals, plus other gender identities and sexual orientations (LGBTQIA+/SOGIESC) and disasters by detailing the harm of blame discourses as well as drawing attention to how the media have a role to play in averting from unintentionally providing a platform for hate speech and ultimately enhancing prejudice against people with diverse SOGIESC.