Artificial Intelligence-Enabled Social Science: A Bibliometric Analysis

DOI

10.2991/978-94-6463-040-4_242How to use a DOI?

Keywords

social science; artificial intelligence; bibliometrics; interdisciplinary; knowledge graph

Abstract

Artificial intelligence (AI)-driven big data analysis, algorithms and computing power introduce new thinking models and decision-making methods for social science research, reconfigure the research paradigm, and thus enhance the depth and breadth of understanding of social phenomena and the laws of social development, offer better solutions to many social problems, and drive the transformation and upgrading of society, economy and culture. This paper uses Scopus and Web of Science (WOS) databases as data sources, and employs bibliometric methods and knowledge graphs to visualize the distribution of research power, author collaboration networks, keyword co-occurrence networks, and research performance to demonstrate the current research prospects and future research directions of AI-enabled social sciences. The results show that:(1) the United States and China are the countries that use AI the most for social science research. Closer academic collaboration networks have formed between European countries such as Germany, Italy, and the Netherlands, and Asian countries such as India, Iran, Turkey, South Korea, and Malaysia, respectively; (2) AI-enabled social science research is currently booming with high research prestige, and the main research themes include various AI algorithms for prediction, optimization, classification, decision support, risk assessment, semantic analysis of social networks, sentiment recognition, smart cities, Industry 4.0, innovation, automation, trustworthiness, and ethics; (3) Research hotspots focus on big data and services, AI ethics, privacy issues in medical big data, electronic health records, medical AI, data-intensive machine learning methods for brain disorders, machine learning for clinical psychology and psychiatry, machine learning for user experience, financial and stock market analysis and prediction, tourism prediction, load time series prediction, energy and power demand prediction for smart grid, wind speed prediction, supply chain and collision prediction, AI in agriculture, industrial internet and digital twin. This study will contribute to the digital transformation of the social sciences and inform future research.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yajun Yuan
AU  - Wei Zhu
PY  - 2022
DA  - 2022/12/27
TI  - Artificial Intelligence-Enabled Social Science: A Bibliometric Analysis
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 1602
EP  - 1608
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_242
DO  - 10.2991/978-94-6463-040-4_242
ID  - Yuan2022
ER  -