Dumbach P, Schwinn L, Löhr T, Elsberger T, Eskofier B (2023)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2023
Pages Range: 1-22
URI: https://link.springer.com/article/10.1007/s41060-023-00483-9
DOI: 10.1007/s41060-023-00483-9
Open Access Link: https://link.springer.com/article/10.1007/s41060-023-00483-9
Current research on trend detection in artificial intelligence (AI) mainly concerns academic data sources and industrial applications of AI. However, we argue that industrial trends are influenced by public perception and political decisions (e.g., through industry subsidies and grants) and should be reflected in political data sources. To investigate this hypothesis, we examine the AI trend development in German business and politics from 1998 to 2020. Therefore, we propose a web mining approach to collect a novel data set consisting of business and political data sources combining 1.07 million articles and documents. We identify 246 AI-related buzzwords extracted from various glossaries. We use them to conduct an extensive trend detection and analysis study on the collected data using machine learning-based approaches. This study successfully detects an AI trend and follows its evolution in business and political data sources over the past two decades. Moreover, we find a faster adoption of AI in business than in politics, with a considerable increase in policy discourse in recent years. Finally, we show that the collected data can be used for trend detection besides AI-related topics using topic clustering and the COVID-19 pandemic as examples.
APA:
Dumbach, P., Schwinn, L., Löhr, T., Elsberger, T., & Eskofier, B. (2023). Artificial intelligence trend analysis in German business and politics: a web mining approach. International Journal of Data Science and Analytics, 1-22. https://dx.doi.org/10.1007/s41060-023-00483-9
MLA:
Dumbach, Philipp, et al. "Artificial intelligence trend analysis in German business and politics: a web mining approach." International Journal of Data Science and Analytics (2023): 1-22.
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