Hate Contagion: Measuring the spread and trajectory of hate on social media

Jd, Gallacher; Bright, Jon

doi:10.31234/osf.io/b9qhd

Cited by 19 publications

(12 citation statements)

References 101 publications

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“…Given its harmful consequences, hate speech should be regarded as a growing societal concern. Recent evidence suggests that, without any preventive action, hate speech (especially online) can develop and spread in an epidemic manner (Bilewicz & Soral, 2020; Gallacher & Bright, 2021), as those who are frequently exposed to hate speech tend to express hate speech more frequently themselves. There are at least two mechanisms that might be responsible for this hate contagion.…”

Section: Hate Speechmentioning

confidence: 99%

The role of empathy in reducing hate speech proliferation. Two contact-based interventions in online and off-line settings.

Soral¹,

Malinowska²,

Bilewicz³

2022

Peace and Conflict: Journal of Peace Psychology

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Antirefugee hate speech is one of the largest obstacles to successful acculturation and well-being of people seeking asylum. Two empirical studies tested the hypothesis that empathy-inducing interventions can modify the way majority group members respond to hate speech against refugees. The first study used a prolonged and elaborated direct contact intervention, whereas the second study used a short-term, light-touch vicarious contact intervention. In both studies contact led to a significant increase in empathy. Additionally, the prolonged direct contact intervention resulted in an increase of sensitivity to antirefugee hate speech and support for hate-speech prohibition, and reduced intentions to use derogatory language in the future. The effects of the vicarious contact intervention led to an increase in sensitivity to antirefugee hate speech but did not affect intentions to prohibit or use hate speech. The analysis of indirect effects suggests that outgroup empathy plays a significant role in sensitizing people to hate speech.

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Section: Hate Speechmentioning

confidence: 99%

The role of empathy in reducing hate speech proliferation. Two contact-based interventions in online and off-line settings.

Soral¹,

Malinowska²,

Bilewicz³

2022

Peace and Conflict: Journal of Peace Psychology

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“…Computational methods for the detection of hate speech and abusive language range from SVM and logistic regression (Davidson et al 2017;Waseem and Hovy 2016;Nobata et al 2016;Magu, Joshi, and Luo 2017), to neural architectures such as RNNs and CNNs (Zhang, Robinson, and Tepper 2016;Gambäck and Sikdar 2017;Del Vigna12 et al 2017;Park and Fung 2017). Transformer-based architectures achieved significant improvements, see (Mozafari, Farahbakhsh, and Crespi 2019;Aluru et al 2020;Samghabadi et al 2020;Salminen et al 2020;Qian et al 2021;Kennedy et al 2020;Arviv, Hanouna, and Tsur 2021), among others.…”

Section: Related Workmentioning

confidence: 99%

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Israeli¹,

Tsur²

2022

Preprint

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Social platforms such as Gab and Parler, branded as 'freespeech' networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make 16.1% of Parler active users, and that they have distinct characteristics comparing to other user groups. We find that hate mongers are more active, more central and express distinct levels of sentiment and convey a distinct array of emotions like anger and sadness. We further complement our analysis by comparing the trends discovered in Parler and those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level, and the first annotated dataset to be made available to the community.

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“…Lima et al (2018) aims to understand what users join Gab and what kind of content they share, while Jasser et al (2021) conduct a qualitative analysis studying Gab's platform norms, given the lack of moderation. Gallacher and Bright (2021) explore whether users seek out Gab in order to express hate, or that the toxic attitude is adopted after joining the platform. The diffusion dynamics of the content posted by hateful and non-hateful Gab users is modeled by Mathew et al (2019) and by .…”

Section: Related Workmentioning

confidence: 99%

Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

Israeli¹,

Tsur²

2022

Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)

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Social platforms such as Gab and Parler, branded as 'free-speech' networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating the limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make about 16% of Parler active users, and that they have distinct characteristics comparing to other user groups. We find that hate mongers are more active, more central, express distinct levels of sentiment, and convey a distinct array of emotions like anger and sadness. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.

show abstract

Hate Contagion: Measuring the spread and trajectory of hate on social media

Cited by 19 publications

References 101 publications

The role of empathy in reducing hate speech proliferation. Two contact-based interventions in online and off-line settings.

The role of empathy in reducing hate speech proliferation. Two contact-based interventions in online and off-line settings.

Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab

Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

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