Abstract
The meaning of the term “Big Data” is still subject to debate, in spite of being widely used in biomedical publications. This confusion in definition leads to missed opportunities for peers to exchange knowledge and practices. A better understanding of Big Data may help researchers to identify themselves with the Big Data community. In this study, we investigate the most distinguishing features in “Big Data”-labelled publications by comparing them against publication without this label (non-Big Data), using text mining and machine learning methods. Furthermore, the usage of the term Big Data was analysed over time. Our models could successfully make a distinction between publications labelled with ‘Big Data’ and those without. The most distinguishing features consisted of terms such as ‘omics’, ‘computing’, ‘storage’, and ‘mining’. We observed that publications that do not use the term Big Data may also address topics that fall under well-accepted definitions of Big Data. Trends suggest that, while the use of the term Big Data increased, it is used less reliably now as compared to earlier years.
Published Version
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