Exploring the Future Development of Artificial Intelligence (AI) Applications in Chatbots: A Bibliometric Analysis
International Journal of Social Robotics, ISSN: 1875-4805, Vol: 15, Issue: 5, Page: 703-716
2023
- 15Citations
- 89Captures
- 2Mentions
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Review Description
Chatbots are fast becoming a key instrument in the research community and enterprise, while few researchers have discussed the focus of existing research and future research directions from the perspective of bibliometrics. This study aimed to explore the evolution tracks of AI applications in chatbots through strict and systematic bibliometric analysis. The analysis began with data retrieval in Web of Science using defined search terms associated with AI and chatbots. Bibliometric tools, including BibExcel and CiteSpace, were employed to conduct performance analysis and co-citation network analysis. Results showed that 1320 documents have been identified until 2022 and research on chatbots exploded after 2016. The USA contributed the most publications and the leading journal mainly focused on engineering and science. The co-citation analysis revealed that the development of AI-based chatbots and the related topics can be summarized as user-centered design, implementation techniques, performance requirements, and media type and future directions of AI in chatbots can mainly focus on improvement of technological means, increased demand, and expansion of applications. This study can help researchers have a broader and deeper understanding of AI-based chatbots and provide an insight into aggregate performance in the AI-based chatbot field.
Bibliographic Details
Springer Science and Business Media LLC
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