Machine Learning applications in bibliometrics. Scopus bibliometric analysis of consumer behavior

Abstract

This article explores the applications of machine learning in bibliometrics, specifically by applying this knowledge to a bibliometric study of consumer behaviour. The exponential growth of scientific publications has outstripped the capabilities of traditional bibliometric tools, creating a need for more advanced techniques to analyse large amounts of data. Machine learning, a branch of artificial intelligence, allows computers to learn from data and make predictions without explicit instruction. The study uses a descriptive bibliometric methodology, using 2563 articles from Scopus and machine learning algorithms such as Latent Dirichlet Allocation (LDA) for article classification. Five main topics were identified: market research, online influence on food consumption and brand perception, social implications of consumption, health implications, and the relationship between shopping behaviour and marketing. The results reveal a large international collaborative network with the US and UK leading the way and key authors including Rifkin, Almotairi, Gruber, Kunz, Bonnevie, Buil, Hieke, Lal, Wang and Carlson. Machine learning is emerging as a key tool for bibliometrics, allowing processes to be automated, complex patterns to be identified and the efficiency of analysis of large databases to be improved. This innovative approach facilitates the understanding of trends and the development of knowledge about consumer behaviour.