A large‑scale semi‑automated approach for assessing document‑type classifcation errors in bibliometric databases

The accuracy of bibliometric databases in classifying document types (DTs)—such as research articles, conference proceedings, reviews, short notes, letters, book chapters, etc.—is crucial for the academic community, as bibliometric indicators may signifcantly infuence research funding, decision-making, and academic reputation. This study presents a semi-automated methodology to assess the accuracy of DT classifcation in bibliometric databases, such as Scopus and Web of Science (WoS). The methodology can handle large document volumes and adapt to diferent DT categories without predefned correspondences. The frst phase of the methodology automatically identifes discrepancies in DT classifcations between Scopus and WoS, in order to fnd potentially misclassifed documents; the second phase involves manually analyzing these documents to confrm and attribute classifcation errors. The methodology is applied to a sample of several tens of thousands of papers from the teaching staf of two major universities in Turin (Italy). The results show overall error rates of approximately 2.7% for Scopus and 2.3% for WoS. The paper also analyzes the most common types of errors found in both databases, providing an interpretation of these inaccuracies and some insights for possible improvements in the quality of these databases.