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. 2022 Dec 7;2(1):pgac281.
doi: 10.1093/pnasnexus/pgac281. eCollection 2023 Jan.

Moralized language predicts hate speech on social media

Kirill Solovev  1 , Nicolas Pröllochs  1
Affiliations

Moralized language predicts hate speech on social media

Kirill Solovev et al. PNAS Nexus. .

Erratum in

Abstract

Hate speech on social media threatens the mental health of its victims and poses severe safety risks to modern societies. Yet, the mechanisms underlying its proliferation, though critical, have remained largely unresolved. In this work, we hypothesize that moralized language predicts the proliferation of hate speech on social media. To test this hypothesis, we collected three datasets consisting of N = 691,234 social media posts and ∼35.5 million corresponding replies from Twitter that have been authored by societal leaders across three domains (politics, news media, and activism). Subsequently, we used textual analysis and machine learning to analyze whether moralized language carried in source tweets is linked to differences in the prevalence of hate speech in the corresponding replies. Across all three datasets, we consistently observed that higher frequencies of moral and moral-emotional words predict a higher likelihood of receiving hate speech. On average, each additional moral word was associated with between 10.76% and 16.48% higher odds of receiving hate speech. Likewise, each additional moral-emotional word increased the odds of receiving hate speech by between 9.35 and 20.63%. Furthermore, moralized language was a robust out-of-sample predictor of hate speech. These results shed new light on the antecedents of hate speech and may help to inform measures to curb its spread on social media.

Keywords: hate speech; moralized language; social media.

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Figures

Fig. 1.
Fig. 1.
(A) Example of moralized language in a source tweet and hate speech in a reply. Here, moral words are highlighted in blue and moral-emotional words are highlighted in pink. (B and C) Complementary cumulative distribution functions (CCDFs) for the number of moral and moral-emotional words per source tweet. (D) CCDFs showing the mean share of hateful replies individual users received per source tweet.
Fig. 2.
Fig. 2.
Multilevel binomial regression estimating the effects of moral words, moral-emotional words, and further controls on the likelihood of receiving hate speech. Shown are the coefficient estimates with 99% CIs. User-specific random effects are included.
See this image and copyright information in PMC

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