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Moralized language predicts hate speech on social media
Kirill Solovev1, Nicolas Pröllochs1
1JLU Giessen, Licher Straße 62, D-35394 Giessen, Germany.
PNAS Nexus
|January 30, 2023
Summary
Moralized language in social media posts predicts increased hate speech in replies. This finding suggests that the use of moral and moral-emotional words can escalate online toxicity and harm.
Area of Science:
- Computational Social Science
- Natural Language Processing
- Social Psychology
Background:
- Hate speech on social media poses significant risks to individual mental health and societal safety.
- The underlying mechanisms driving the proliferation of online hate speech remain largely unclear.
Purpose of the Study:
- To investigate the hypothesis that moralized language predicts the spread of hate speech on social media.
- To analyze the relationship between moralized language in source tweets and hate speech prevalence in replies.
Main Methods:
- Collected three datasets of social media posts and replies from Twitter (N = 691,234 posts, ~35.5 million replies).
- Analyzed posts from societal leaders in politics, news media, and activism.
- Employed textual analysis and machine learning to quantify moralized language and hate speech.
Main Results:
- Higher frequencies of moral and moral-emotional words in source tweets consistently predicted a higher likelihood of receiving hate speech in replies across all datasets.
- Each additional moral word increased the odds of receiving hate speech by 10.76%–16.48%.
- Each additional moral-emotional word increased the odds of receiving hate speech by 9.35%–20.63%.
Conclusions:
- Moralized language is a significant predictor of hate speech proliferation on social media.
- Findings offer insights into the antecedents of online hate speech.
- Results may inform strategies to mitigate the spread of hate speech on digital platforms.
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