Thirty years of research into hate speech: topics of interest and their evolution

Alice Tontodimamma (), Eugenia Nissi (), Annalina Sarra () and Lara Fontanella ()
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Alice Tontodimamma: University G. d’Annunzio of Chieti–Pescara
Eugenia Nissi: University G. d’Annunzio of Chieti–Pescara
Annalina Sarra: University G. d’Annunzio of Chieti–Pescara
Lara Fontanella: University G. d’Annunzio of Chieti–Pescara

Scientometrics, 2021, vol. 126, issue 1, No 7, 157-179

Abstract: Abstract The exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: “general debate hate speech versus freedom of expression”,“hate-speech automatic detection and classification by machine-learning strategies”, and “gendered hate speech and cyberbullying”. The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech.

Keywords: Online hate speech; Bibliometrics analysis; Topic models; Latent Dirichlet allocation; MSC 62-02; MSC 62P25 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11192-020-03737-6

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