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Discovering the Cognitive Bias of Toxic Language Through Metaphorical Concept Mappings

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Abstract

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.

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Data Availability

No datasets were generated or analyzed during the current study.

Notes

  1. Metaphors are shown in italics.

  2. We show concept mappings in (target, source) format, where the target and source concepts are separated by a comma. To distinguish, we enumerate concept examples in (concept1; concept2;...;) format, where the concepts are separated by semicolons.

  3. For example, let us just coddle [treat] these idiotic standoff perps and let them have their way. The source concept for coddle is darling.

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Funding

This research/project is supported by the Ministry of Education, Singapore under its MOE Academic Research Fund Tier 2 (STEM RIE2025 Award MOE-T2EP20123-0005)

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  1. College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore

    Mengshi Ge, Rui Mao & Erik Cambria

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  1. Mengshi Ge
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Contributions

Mengshi Ge: methodology, formal analysis, data curation, visualization, writing—original draft; Rui Mao: validation, formal analysis, data curation, investigation, writing—review and editing; Erik Cambria: resources, writing - review and editing, supervision, project administration.

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Correspondence to Erik Cambria.

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Ge, M., Mao, R. & Cambria, E. Discovering the Cognitive Bias of Toxic Language Through Metaphorical Concept Mappings. Cogn Comput 17, 65 (2025). https://doi.org/10.1007/s12559-025-10423-3

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