The invisible author: Citizen sociolinguistic perspectives on identifying human and AI-generated narrative texts

Abstract

The widespread usage of generative AI (genAI) challenges traditional notions of authorship, raising questions about how readers perceive and attribute textual origin. As this sociolinguistic issue is recognized by people, the study presented in this paper was initiated from a collaboration between professional researchers and citizens engaging in encounters discussing language-related social issues. Following a citizen sociolinguistic approach, we engaged 576 participants in a questionnaire study assessing their ability to distinguish AI-generated (GPT-3.5) from human-authored narrative texts in Hungarian. The results of the study show that respondents were significantly more successful than chance (66%) in identifying the author of the text regardless of their age, gender, occupation, or self-reported AI skills. However, the performance score was dependent on the author of the text, as AI-authored texts were identified more successfully than those written by humans. We also found that respondents preferred texts they believed to be human-authored over those they assumed to be generated by AI, demonstrating an anthropocentric bias. Beyond these results, the study highlights how the growing invisibilization of genAI technologies challenges long-standing ideas about authorship, even in the case of narrative texts. • Respondents identified the author with greater accuracy than random chance, but did not reach perfect accuracy. • Performance scores were not affected by respondents' occupation, gender, age, self-reported AI skills and AI literacy. • Performance scores were significantly higher for AI-generated texts compared to those authored by humans. • Respondents preferred texts they believed were written by a human over texts assumed to be written by AI.

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