The Double-Edged Sword: (A Corpus-Assisted Conceptual Metaphor Analysis of Artificial Intelligence in Web-based English Texts) - Zainah Theib Alshahrani (Journal of Arts, Literature, Humanities and Social Sciences, 2026)
The present study explores how artificial intelligent is conceptualised and constructed in English web-based texts. Using a conceptual metaphor corpus assisted approach, a large dataset composed of web-based English language texts was examined to see how the notion of artificial intelligence was constructed using different metaphorical patterns. Corpus analysis of salient lexical items is combined with the qualitative discourse analysis to uncover and interpret the underlying source domains and the mappings of their features onto the domain of artificial intelligence. The findings revealed that this concept is predominantly constructed through interrelated metaphorical domains, particularly Human (personification), Force/Power, and Transformation. These varied metaphorical structures produce conflicting representations, simultaneously portraying artificial intelligence as a helpful person, beneficial tool, a transformative force, and a potential threat. Furthermore, the analysis shows that through personification, capacities of agency and decision-making are attributed to artificial intelligence, thus partially displacing human responsibility for technological outcomes. The study also shows that metaphorical representations of artificial intelligence are not merely descriptive. Rather, they play an important role in in shaping people’s attitudes toward technological innovation, thus influencing perceptions of its benefits, risks and future impact on human life. By examining the metaphorical patterns across diverse web-based contexts, the study contributes to research on discourse and metaphor by showing how figurative language shapes public understandings of such a technological system. Methodologically, it highlights the value of combining corpus-based techniques with conceptual metaphor analysis in exploring large-scale data discourse.