Dynamics of online hate and misinformation (original) (raw)

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. Public debates on social media platforms are often heated and polarised 1-3. Back in the 90s, Mike Godwin coined a theorem, today known as Godwin's law, stating that "As an online discussion grows longer, the probability of a comparison involving Nazis or Hitler approaches to one". More recently, with the advent of social media, an increasing number of people is reporting exposure to online hate speech 4 , leading institutions and online platforms to investigate possible solutions and countermeasures 5. To prevent and counter the spread of hate speech online, for example, the European Commission agreed with Facebook, Microsoft, Twitter, YouTube, Instagram, Snapchat, Dailymotion, Jeuxvideo.com, and TikTok on a "Code of conduct on countering illegal hate speech online" 6. In addition to fuelling the toxicity of the online debate, hate speech may have severe offline consequences. Some researchers hypothesised a causal link between online hate and offline violence 7-9. Furthermore, there is empirical evidence that online hate may induce fear of offline repercussions 10. However, the detection and contrast of hate speech is complicated. There are still ambiguities in the very definition of hate speech, with academic and relevant stakeholders providing their own interpretations 4 , including social media companies such as Facebook 11 , Twitter 12 , and YouTube 13. We use the term "hate speech" to cover whole spectrum of language used in online debates, from normal, acceptable to the extreme, inciting violence. On the extreme end, violent speech covers all forms of expression which spread, incite, promote or justify racial hatred, xenophobia, antisemitism or other forms of hatred based on intolerance, including: intolerance expressed by aggressive nationalism and ethnocentrism, discrimination and hostility against minorities, migrants and people of immigrant origin 14. Less extreme forms of unacceptable speech include inappropriate (e.g., profanity) and offensive language (e.g., dehumanisation, offensive remarks), which is not illegal, but deteriorates public discourse and can lead to a more radicalised society. In this work, we analyse a corpus of more than one million comments on Italian YouTube videos related to COVID-19 to unveil the dynamics and trends of online hate. First, we manually annotate a large corpus of YouTube comments for hate speech, and train and fine-tune a hate speech deep learning model to accurately detect it. Then, we apply the model to the entire corpus, aiming to characterise the behaviour of users producing hate, and shed light on the (possible) relationship between the consumption of misinformation and usage of hate and toxic language. The reason for performing hate speech detection on the Italian language is twofold: First, Italy was one of the countries most affected by the COVID-19 pandemic and especially by the early application of non-pharmaceutical interventions (strict lockdown happened on March 9, 2020). Such an event, by forcing people at home, increased the internet use and was likely to exacerbate the public debate and foment hate speech against specific targets such as the government and politicians. Second, Italian is a less studied language