Using Machine Learning to Learn Machines: A Cross-Cultural Study of Users’ Responses to Machine-Generated Artworks

ABSTRACT

Drawing from prior literature on machine-generated news, this study examines machine-generated artworks in a cross-cultural context. It combines machine learning approaches with online experiments and investigates how different genres of artworks and different authorship cues influence participants’ open-ended responses to machine-generated works. Results suggest that while genres and cultures affected participants’ discussion topics and word use, the differences between participants’ responses to machine-generated artworks and human-generated ones were not evident. This study tests the explanatory power of machine heuristic and demonstrates the feasibility of integrating multiple methods in future AI-based media research.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the the National Social Science Fund of China [No. 18BXW046].

Notes on contributors

Kun Xu

Kun Xu (Ph.D., Temple University) is an assistant professor of emerging media at the College of Journalism and Communications, University of Florida. His research centers on the concept of presence in human-robot interaction, virtual/augmented reality, and other emerging technologies.

Fanjue Liu

Fanjue Liu (M.A., University of Florida) is a doctoral student at the University of Florida. Her research interests include human-machine communication and computer-mediated communication. She seeks to investigate how and why people make sense of and interact with social bots.

Yi Mou

Yi Mou (Ph.D., University of Connecticut) is a special researcher at the School of Media and Communication of Shanghai Jiao Tong University. Her research interests include new media effects, human-machine communication, and health communication.

Yuheng Wu

Yuheng Wu (M.A., Shanghai Jiao Tong University) is a doctoral student at the School of Media and Communication of Shanghai Jiao Tong University. His research interests include new media effects, human-machine communication, and health communication.

Jing Zeng

Jing Zeng (Ph.D., Queensland University of Technology) is a senior research and teaching associate at the University of Zurich. Her research interests include science communication, online misinformation, and digital methods.

Mike S. Schäfer

Mike S. Schäfer (Ph.D., Free University of Berlin) is full professor at the University of Zurich. His working topics are science communication, environmental and climate communication, and social media debates about science and technology.