Understanding Scientific Collaboration in Artificial Intelligence Research: A Bibliometric Analysis

Artificial intelligence (AI) is a rapidly growing field, and social scientists are increasingly interested in understanding its implications. This study uses a network approach to examine the relationship between AI and social science research. The authors analyzed over 19,000 scientific documents and identified keywords that define and represent the direction of AI research. They created a network diagram that shows the connections between these keywords and reveals their common associations and unique characteristics.
The study found three main themes in AI research: machine learning, COVID-19, and big data. Machine learning is the most prominent theme, represented by keywords such as "neural networks" and "deep learning." COVID-19 is the second theme, with keywords related to pandemic management and public health. Big data is the third theme, with keywords that refer to the vast amounts of data generated by AI systems.
The authors also identified clusters of keywords that are highly interconnected, indicating important areas of research in AI. For example, there is a cluster of keywords related to natural language processing, including "text analysis" and "sentiment analysis." Another cluster is focused on computer vision, with keywords such as "image recognition" and "object detection."
The study demonstrates the power of network analysis for understanding the complex relationships between AI and social science research. By visualizing the connections between keywords, the authors were able to identify patterns and trends that would be difficult to detect through other methods. This approach can be useful for policymakers, researchers, and other stakeholders who want to understand the potential impact of AI on society.
In conclusion, this study provides a comprehensive overview of AI research in social sciences using network analysis. It identifies major themes and clusters of keywords that are highly interconnected, revealing important areas of research in AI. The findings demonstrate the value of network analysis for understanding complex relationships between AI and social science research, providing insights that can inform policy and future research directions.