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Human-chatbot interaction studies through the lens of bibliometric analysis.

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Název: Human-chatbot interaction studies through the lens of bibliometric analysis.
Autoři: Chen, Jiahao, Guo, Fu, Ren, Zenggen, Wang, Xueshuang, Ham, Jaap
Zdroj: Universal Access in the Information Society; Mar2025, Vol. 24 Issue 1, p79-98, 20p
Témata: BIBLIOMETRICS, ARTIFICIAL intelligence, SYSTEMS design, SCIENCE databases, WEB databases
Abstrakt: Since chatbots have been integrated into people's lives from various industries, human-chatbot interaction has begun to attract widespread attention in academia. Still, contributions to the systematic mapping of this field are lacking. This paper is the first to present a systematic review of human-chatbot interaction research using bibliometric analysis. A total of 3013 publications (from the year 2000 to 2022) from Web of Science database were analysed to uncover the current status and research trend in human-chatbot interaction domain. The analysis focused on temporal and geographical distribution of these publications and identified the most influential publication outlets, institutes, articles, and authors. Additionally, keyword co-occurrence analysis and temporal distribution of keywords showed that primary topics in human-chatbot interaction mainly concentrate on techniques and methods in chatbot systems design, extensive applications in various fields, user experience and emotional expression, humanizing features design, and perceived privacy risk and ethics. Finally, this paper sheds light on a comprehensive understanding of human-chatbot interaction research and provides directions for future research in this field. [ABSTRACT FROM AUTHOR]
Copyright of Universal Access in the Information Society is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Since chatbots have been integrated into people's lives from various industries, human-chatbot interaction has begun to attract widespread attention in academia. Still, contributions to the systematic mapping of this field are lacking. This paper is the first to present a systematic review of human-chatbot interaction research using bibliometric analysis. A total of 3013 publications (from the year 2000 to 2022) from Web of Science database were analysed to uncover the current status and research trend in human-chatbot interaction domain. The analysis focused on temporal and geographical distribution of these publications and identified the most influential publication outlets, institutes, articles, and authors. Additionally, keyword co-occurrence analysis and temporal distribution of keywords showed that primary topics in human-chatbot interaction mainly concentrate on techniques and methods in chatbot systems design, extensive applications in various fields, user experience and emotional expression, humanizing features design, and perceived privacy risk and ethics. Finally, this paper sheds light on a comprehensive understanding of human-chatbot interaction research and provides directions for future research in this field. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Universal Access in the Information Society is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2025
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