BWL XI: Paper in PNAS Nexus
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BWL XI: Paper in PNAS Nexus

A new research paper studying the antecedents of hate speech on social media has been accepted for publication in PNAS Nexus. Based on three large-scale datasets across three domains (politics, news media, and activism), the study demonstrates that moralized language in social media posts fosters the proliferation of hate speech.

Title: Moralized language predicts hate speech on social media

Authors: Kirill Solovev & Nicolas Pröllochs 


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 10.66% 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.


The paper is available here (open access).