The Influence of Personality Traits in <scp>Human‐Humanoid</scp> Robot Interaction

Chien, Shih‐Yi; Chen, Chih‐Ling; Chan, Yao-Cheng

doi:10.1002/pra2.644

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Robot personality is also a potential research direction for HRE studies. Research on robot personality has shown that humans tend to project personality onto robots (Fussell et al, 2008) as well as recognize a robot's personality through its designs (Chien et al, 2022). Our interview data also demonstrated that some participants projected a robot personality onto the robot, such as the “friendly” mentioned above.…”

Section: Discussionmentioning

confidence: 99%

Understanding Reactions in Human‐Robot Encounters with Autonomous Quadruped Robots

Chan,

Hauser

2023

Proceedings of the Association for Information Science and Tech

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Incidental human‐robot encounters are becoming more common as robotic technologies proliferate, but there is little scientific understanding of human experience and reactions during these encounters. To contribute towards addressing this gap, this study applies Grounded Theory methodologies to study human reactions in Human‐Robot Encounters with an autonomous quadruped robot. Based upon observation and interviews, we find that participants' reactions to the robot can be explained by their attitudes of familiarity, certainty, and confidence during their encounter and by their understanding of the robot's capabilities and role. Participants differed in how and whether they utilized opportunities to resolve their unfamiliarity, uncertainty, or lack of confidence, shedding light on the dynamics and experiential characteristics of Human‐Robot Encounters. We provide an emerging theory that can be used to unravel the complexity of the field as well as assist hypothesis generation in future research in designing and deploying mobile autonomous service robots.

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Section: Discussionmentioning

confidence: 99%

Understanding Reactions in Human‐Robot Encounters with Autonomous Quadruped Robots

Chan,

Hauser

2023

Proceedings of the Association for Information Science and Tech

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“…Personality detection aims to identify the personality traits implied in social media posts that offering a deeper insight into human behavior (Nutescu and Mocanu 2023), emotional processes (Lian, Liu, and Tao 2022), and mental health (Zanwar et al 2023). Besides, it can provide timely and objective support for downstream applications, such as human-computer interaction systems (Chien, Chen, and Chan 2022), virtual dialogue systems (Yang, Chen, and Narasimhan 2021), and recommendation systems (Yang et al 2022;Shen et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Data Augmented Graph Neural Networks for Personality Detection

Zhu,

Xia,

et al. 2024

AAAI

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Personality detection is a fundamental task for user psychology research. One of the biggest challenges in personality detection lies in the quantitative limitation of labeled data collected by completing the personality questionnaire, which is very time-consuming and labor-intensive. Most of the existing works are mainly devoted to learning the rich representations of posts based on labeled data. However, they still suffer from the inherent weakness of the amount limitation of labels, which potentially restricts the capability of the model to deal with unseen data. In this paper, we construct a heterogeneous personality graph for each labeled and unlabeled user and develop a novel psycholinguistic augmented graph neural network to detect personality in a semi-supervised manner, namely Semi-PerGCN. Specifically, our model first explores a supervised Personality Graph Neural Network (PGNN) to refine labeled user representation on the heterogeneous graph. For the remaining massive unlabeled users, we utilize the empirical psychological knowledge of the Linguistic Inquiry and Word Count (LIWC) lexicon for multi-view graph augmentation and perform unsupervised graph consistent constraints on the parameters shared PGNN. During the learning process of finite labeled users, noise-invariant learning on a large scale of unlabeled users is combined to enhance the generalization ability. Extensive experiments on three real-world datasets, Youtube, PAN2015, and MyPersonality demonstrate the effectiveness of our Semi-PerGCN in personality detection, especially in scenarios with limited labeled users.

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