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Moralized language predicts hate speech on social media.

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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.

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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.