Mapping AI ethics narratives: evidence from Twitter discourse between 2015 and 2022

Mengyi Wei, Puzhen Zhang, Chuan Chen (), Dongsheng Chen, Chenyu Zuo and Liqiu Meng
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Mengyi Wei: Technical University of Munich
Puzhen Zhang: Technical University of Munich
Chuan Chen: Technical University of Munich
Dongsheng Chen: Technical University of Munich
Chenyu Zuo: ETH Zurich
Liqiu Meng: Technical University of Munich

Palgrave Communications, 2025, vol. 12, issue 1, 1-13

Abstract: Abstract The ethical issues that arise in the development of AI technologies are closely linked to public engagement. Although Twitter, as an online public sphere, provides a platform for exploring AI ethics discourse, it is difficult for current research to effectively extract fine-grained but meaningful information from the vast amount of social media data. To address this challenge, this paper proposes a research framework for the fine-grained exploration of AI ethics discourse on Twitter. The framework consists of two main parts: (1) combining neural networks with large-scale language models to construct a hierarchically structured topic framework that not only extracts popular topics of public interest, but also highlights smaller, yet significant voices; (2) using narrative metaphors to achieve the integration of fragmented information across levels and topics, ultimately presenting a complete story to help the public better understand the evolution of topics within AI ethics discourse. Our research has revealed that the most significant concern in the current AI ethics discourse is the lag in AI-related laws and ethical guidelines. It also shows that the integration of AI technology with the humanities is essential to promote a good public society. Through cross-level fine-grained mining, this study uncovers information hidden beneath the noise interference, which helps policymakers make targeted adjustments or improvements to policies. In addition, this research framework provides a reference for fine-grained mining of other specific issues in social media data.

Date: 2025
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DOI: 10.1057/s41599-025-04469-9

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