Hate speech law can be found throughout the world. But it is also the subject of numerous principled arguments, both for and against. These principles invoke a host of morally relevant features ...(e.g., liberty, health, autonomy, security, non-subordination, the absence of oppression, human dignity, the discovery of truth, the acquisition of knowledge, self-realization, human excellence, civic dignity, cultural diversity and choice, recognition of cultural identity, intercultural dialogue, participation in democratic self-government, being subject only to legitimate rule) and practical considerations (e.g., efficacy, the least restrictive alternative, chilling effects). The book develops and then critically examines these various principled arguments. It also attempts to de-homogenize hate speech law into different clusters of laws/regulations/codes that constrain uses of hate speech, so as to facilitate a more nuanced examination of the principled arguments.
Hate Speech in Asia and Europe Kang, Myungkoo; Rive-Lasan, Marie-Orange; Kim, Wooja ...
2020, 20200316, 2020-03-27
eBook
This edited collection provides a timely review of the current state of hate speech research in Asia and Europe, through the comparative examples of Korea, Japan and France.
Extending the study of ...hate speech studies beyond the largely western emphasis on European and US contexts dominant in the field, this book’s comparative framework aims to examine hate speech as a global phenomenon spanning Asian and European contexts. An innovative range of nuanced empirical case studies explores hate speech by analyzing gendered hate speech and nationality, French cartoon humour, official counter-radicalization narratives and the use of international law to inform domestic legislation in the Philippines and Japan. A fresh perspective on Asian and European hate speech, this book’s evaluation of current hate speech research also identifies future directions for the development of theory and method.
Filling a critical gap in the literature, Hate Speech in Asia and Europe will appeal to students and scholars of law, politics, religion, history, social policy and social science more broadly, as well as Asian Studies.
Online toxic discourses could result in conflicts between groups or harm to online communities. Hate speech is complex and multifaceted harmful or offensive content targeting individuals or groups. ...Existing literature reviews have generally focused on a particular category of hate speech, and to the best of our knowledge, no review has been dedicated to hate speech datasets. This paper systematically reviews textual hate speech detection systems and highlights their primary datasets, textual features, and machine learning models. The results of this literature review are integrated with content analysis, resulting in several themes for 138 relevant papers. This study shows several approaches that do not provide consistent results in various hate speech categories. The most dominant sets of methods combine more than one deep learning model. Moreover, the analysis of several hate speech datasets shows that many datasets are small in size and are not reliable for various tasks of hate speech detection. Therefore, this study provides the research community with insights and empirical evidence on the intrinsic properties of hate speech and helps communities identify topics for future work.
There is an enormous growth of social media which fully promotes freedom of expression through its anonymity feature. Freedom of expression is a human right but hate speech towards a person or group ...based on race, caste, religion, ethnic or national origin, sex, disability, gender identity, etc. is an abuse of this sovereignty. It seriously promotes violence or hate crimes and creates an imbalance in society by damaging peace, credibility, and human rights, etc. Detecting hate speech in social media discourse is quite essential but a complex task. There are different challenges related to appropriate and social media-specific dataset availability and its high-performing supervised classifier for text-based hate speech detection. These issues are addressed in this study, which includes the availability of social media-specific broad and balanced dataset, with multi-class labels and its respective automatic classifier, a dataset with language subtleties, dataset labeled under a comprehensive definition and well-defined rules, dataset labeled with the strong agreement of annotators, etc. Addressing different categories of hate separately, this paper aims to accurately predict their different forms, by exploring a group of text mining features. Two distinct groups of features are explored for problem suitability. These are baseline features and self-discovered/new features. Baseline features include the most commonly used effective features of related studies. Exploration found a few of them, like character and word n-grams, dependency tuples, sentiment scores, and count of 1st, 2nd person pronouns are more efficient than others. Due to the application of latent semantic analysis (LSA) for dimensionality reduction, this problem is benefited from the utilization of many complex and non-linear models and CAT Boost performed best. The proposed model is compared with related studies in addition to system baseline models. The results produced by the proposed model were much appreciating.
With the multiplication of social media platforms, which offer anonymity, easy access and online community formation and online debate, the issue of hate speech detection and tracking becomes a ...growing challenge to society, individual, policy-makers and researchers. Despite efforts for leveraging automatic techniques for automatic detection and monitoring, their performances are still far from satisfactory, which constantly calls for future research on the issue. This paper provides a systematic review of literature in this field, with a focus on natural language processing and deep learning technologies, highlighting the terminology, processing pipeline, core methods employed, with a focal point on deep learning architecture. From a methodological perspective, we adopt PRISMA guideline of systematic review of the last 10 years literature from ACM Digital Library and Google Scholar. In the sequel, existing surveys, limitations, and future research directions are extensively discussed.
hate speech research; discourse analysis of social media; corpus linguistics methods; C.O.N.T.A.C.T. project European Union; hate speech in reaction to news; racism in online comments forum; ...discourse analytic research
Hate Speech: Linguistic Approaches Powell, Richard
The international journal of speech, language and the law,
01/2020, Letnik:
27, Številka:
1
Journal Article
Recenzirano
A book announcement is presented for: Victoria Guillén Nieto. (2021). Hate Speech: Linguistic Approaches. De Gruyter Mouton. ISBN 9783110672466. 200 pp.
Hate Speech in social media is a complex phenomenon, whose detection has recently gained significant traction in the Natural Language Processing community, as attested by several recent review works. ...Annotated corpora and benchmarks are key resources, considering the vast number of supervised approaches that have been proposed. Lexica play an important role as well for the development of hate speech detection systems. In this review, we systematically analyze the resources made available by the community at large, including their development methodology, topical focus, language coverage, and other factors. The results of our analysis highlight a heterogeneous, growing landscape, marked by several issues and venues for improvement.
Hate speech often spreads on social media and harms individuals and the community. Machine learning models have been proposed to detect hate speech in social media; however, several issues presently ...limit the performance of current approaches. One challenge is the issue of having diverse comprehensions of hate speech constructs which will lead to many speech categories and different interpretations. In addition, certain language-specific features, and short text issues, such as Twitter, exacerbate the problem. Moreover, current machine learning approaches lack universality due to small datasets and the adoption of a few features of hateful speech. This paper develops and builds new feature sets based on frequencies of textual tokens and psychological characteristics. Then, the study evaluates several machine learning methods over a large dataset. Results showed that the Random Forest and BERT methods are the most valuable for detecting hate speech content. Furthermore, the most dominant features that are helpful for hate speech detection methods combine psychological features and Term-Frequency Inverse Document-Frequency (TFIDF) features. Therefore, the proposed approach could identify hate speech on social media platforms like Twitter.
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