Exploring Robot Personality through Big Data Mining: A Century-Long Analysis from Google Books

AbstractHuman–robot interactions (HRIs) are significantly influenced by the personality of robots. However, research on robot personality from the perspective of big data text mining remains scarce. To address this gap, our study delves into the portrayal of Big Five personality traits in robots across millions of Google Books, spanning from 1920 to 2019. In this study, we identify intriguing trends in how robot personalities have been described over the years. Notably, we observe that the trait of openness has consistently been the most frequently cited Big Five personality factor throughout the twentieth century. Following closely are conscientiousness, agreeableness, extraversion, and neuroticism. However, a noteworthy shift occurs in the late twentieth century, where extraversion garners increasing attention, ultimately becoming the most prominent Big Five personality factor after 2010. Furthermore, our analysis uncovers a fascinating positivity bias in the portrayal of robot personality. Robots are more commonly depicted as extroverted rather than introverted and open rather than reserved. These trends also correlate with the evolution of core personality words. For instance, the term intellectual robot gradually yields to intelligent robot over the course of the twentieth century. Additionally, in the twenty-first century, social robot emerges as the most prevailing topic. Understanding the interplay between human records and their perception of robot personalities provides valuable insights into both real descriptions and ideal expectations of robots. This research serves as a critical reference for further advancements in robot personality studies, shedding light on the dynamic nature of HRIs.Keywords: Robot personalityBig Fivebig data miningrobot historyGoogle Books Author contributionsConceptualization: Liang Xu; methodology: Liang Xu; formal analysis and investigation: Liang Xu; writing – original draft preparation: Liang Xu and Chiju Chao; writing – review and editing: Chiju Chao; funding acquisition: Liang Xu; supervision: Chiju Chao.Ethical approvalThis is a retrospective analysis of publicly available data. The Zhejiang University of Technology's Research Ethics Committee has confirmed that no ethical approval is required. All the data collection and analysis methods were carried out in accordance with relevant guidelines and regulations.Disclosure statementThe authors declare no conflict of interest.Data availability statementThe data are accessible at http://storage.googleapis.com/books/ngrams/books/datasetsv3.htmlMaterials and code availability statementThe materials and code can be made available upon reasonable request from the corresponding author.Additional informationFundingThis work was supported by the Start-up Foundation of Zhejiang University of Technology, Grant Number 2022161080009.Notes on contributorsLiang XuLiang Xu obtained his BS in applied psychology and PhD in psychology from Zhejiang University, in 2015 and 2020, respectively. He is currently a lecturer at Zhejiang University of Technology, with research interests spanning human–computer interaction, behavioral psychology, and personality psychology.Chiju ChaoChiju Chao is a PhD candidate for design at Tsinghua University in Beijing. She holds a Bachelor's degree in computer science from Zhejiang University and a Master's degree in design from Tsinghua University. Her research revolves around the design of emotionally intelligent products.