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<p>This article examines how personification metaphors shape public perceptions of generative AI through a computational analysis of Tweets posted during the emergence of ChatGPT (2022–2023). Using a hybrid methodology combining rule-based NLP and the DeepMet neural network model, we identify AI personification patterns and discuss their ideological implications. Results reveal that most metaphors employ Subject–Verb–Object constructions framing AI as an active agent. The major source domains are COGNITION (e.g. <i>think</i>), ACTION/CHANGE (e.g., <i>replace</i>), COMMUNICATION (e.g., <i>listen</i>), and EMOTION (e.g., <i>love</i>, <i>fear</i>). Sentiment analysis shows predominantly positive attitudes, particularly when framing AI as a collaborative assistant, while negative sentiment is strongly associated with job displacement concerns. These patterns suggest that users assign agency and autonomy to AI systems with critical implications: personification underpins an implicit social contract where augmentation is embraced and replacement resisted, yet it simultaneously obscures accountability by positioning AI as an independent actor rather than a product of corporate design and deployment. Moreover, as LLM outputs become more sophisticated, the figurative distance between computational systems and human behavior risks collapsing entirely. Our findings show that metaphorical language dynamically constructs social realities about generative AI, raising stakes for public trust, responsibility attribution, and technology governance.</p>

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‘Thinking’, ‘helping’, and ‘replacing’: what personification metaphors reveal about the social integration of generative AI

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Thi Ngoc Quyen Pham,
  • Cameron Morin

摘要

This article examines how personification metaphors shape public perceptions of generative AI through a computational analysis of Tweets posted during the emergence of ChatGPT (2022–2023). Using a hybrid methodology combining rule-based NLP and the DeepMet neural network model, we identify AI personification patterns and discuss their ideological implications. Results reveal that most metaphors employ Subject–Verb–Object constructions framing AI as an active agent. The major source domains are COGNITION (e.g. think), ACTION/CHANGE (e.g., replace), COMMUNICATION (e.g., listen), and EMOTION (e.g., love, fear). Sentiment analysis shows predominantly positive attitudes, particularly when framing AI as a collaborative assistant, while negative sentiment is strongly associated with job displacement concerns. These patterns suggest that users assign agency and autonomy to AI systems with critical implications: personification underpins an implicit social contract where augmentation is embraced and replacement resisted, yet it simultaneously obscures accountability by positioning AI as an independent actor rather than a product of corporate design and deployment. Moreover, as LLM outputs become more sophisticated, the figurative distance between computational systems and human behavior risks collapsing entirely. Our findings show that metaphorical language dynamically constructs social realities about generative AI, raising stakes for public trust, responsibility attribution, and technology governance.