Dynamics of online hate and misinformation

Abstract Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a co...

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Autores principales:	Matteo Cinelli, Andraž Pelicon, Igor Mozetič, Walter Quattrociocchi, Petra Kralj Novak, Fabiana Zollo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ed8256497a1b4f2ca0646267f13994e7
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id	oai:doaj.org-article:ed8256497a1b4f2ca0646267f13994e7
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spelling	oai:doaj.org-article:ed8256497a1b4f2ca0646267f13994e72021-11-14T12:24:19ZDynamics of online hate and misinformation10.1038/s41598-021-01487-w2045-2322https://doaj.org/article/ed8256497a1b4f2ca0646267f13994e72021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01487-whttps://doaj.org/toc/2045-2322Abstract Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin’s law, online debates tend to degenerate towards increasingly toxic exchanges of views.Matteo CinelliAndraž PeliconIgor MozetičWalter QuattrociocchiPetra Kralj NovakFabiana ZolloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matteo Cinelli Andraž Pelicon Igor Mozetič Walter Quattrociocchi Petra Kralj Novak Fabiana Zollo Dynamics of online hate and misinformation
description	Abstract Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin’s law, online debates tend to degenerate towards increasingly toxic exchanges of views.
format	article
author	Matteo Cinelli Andraž Pelicon Igor Mozetič Walter Quattrociocchi Petra Kralj Novak Fabiana Zollo
author_facet	Matteo Cinelli Andraž Pelicon Igor Mozetič Walter Quattrociocchi Petra Kralj Novak Fabiana Zollo
author_sort	Matteo Cinelli
title	Dynamics of online hate and misinformation
title_short	Dynamics of online hate and misinformation
title_full	Dynamics of online hate and misinformation
title_fullStr	Dynamics of online hate and misinformation
title_full_unstemmed	Dynamics of online hate and misinformation
title_sort	dynamics of online hate and misinformation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ed8256497a1b4f2ca0646267f13994e7
work_keys_str_mv	AT matteocinelli dynamicsofonlinehateandmisinformation AT andrazpelicon dynamicsofonlinehateandmisinformation AT igormozetic dynamicsofonlinehateandmisinformation AT walterquattrociocchi dynamicsofonlinehateandmisinformation AT petrakraljnovak dynamicsofonlinehateandmisinformation AT fabianazollo dynamicsofonlinehateandmisinformation
_version_	1718429262922383360

Dynamics of online hate and misinformation

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