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<p>With the prosperity of social media, toxic language spreading over social media has become an unignorable challenge for individual mental health and social harmony. Many researchers have studied toxic language identification to control or mitigate it. However, it still leaves a blank in the cognitive patterns of toxic language. Metaphors as a common feature in natural language connect literal and metaphorical meanings, which could be a useful tool to study the underlying cognitive patterns of the text. In this paper, we utilize a metaphor processing tool, MetaPro, to process a public toxic language dataset and analyze the cognitive biases between toxic and non-toxic language, multiple levels and subtypes of toxic language as well as toxic language mentioning different genders, sexual orientations, and races. Our study demonstrates that significant differences exist in cognitive patterns of the above-mentioned categories and analyzes the differences with machine learning methods.</p>

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Discovering the Cognitive Bias of Toxic Language Through Metaphorical Concept Mappings

  • 期刊论文

  • 发表日期：

    2025年2月14日

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  • Mengshi Ge,
  • Rui Mao,
  • Erik Cambria

摘要

With the prosperity of social media, toxic language spreading over social media has become an unignorable challenge for individual mental health and social harmony. Many researchers have studied toxic language identification to control or mitigate it. However, it still leaves a blank in the cognitive patterns of toxic language. Metaphors as a common feature in natural language connect literal and metaphorical meanings, which could be a useful tool to study the underlying cognitive patterns of the text. In this paper, we utilize a metaphor processing tool, MetaPro, to process a public toxic language dataset and analyze the cognitive biases between toxic and non-toxic language, multiple levels and subtypes of toxic language as well as toxic language mentioning different genders, sexual orientations, and races. Our study demonstrates that significant differences exist in cognitive patterns of the above-mentioned categories and analyzes the differences with machine learning methods.