  The General Data Protection Regulation (GDPR) was enforced in 2018. After
this enforcement, many fines have already been imposed by national data
protection authorities in the European Union (EU). This paper examines the
individual GDPR articles referenced in the enforcement decisions, as well as
predicts the amount of enforcement fines with available meta-data and text
mining features extracted from the enforcement decision documents. According to
the results, articles related to the general principles, lawfulness, and
information security have been the most frequently referenced ones. Although
the amount of fines imposed vary across the articles referenced, these three
particular articles do not stand out. Furthermore, good predictions are
attainable even with simple machine learning techniques for regression
analysis. Basic meta-data (such as the articles referenced and the country of
origin) yields slightly better performance compared to the text mining
features.
