30 years of parasitology research analysed by text mining

Abstract

Bibliometric methods were used to analyse the major research trends, themes and topics over the last 30 years in the parasitology discipline. The tools used were SciMAT, VOSviewer and SWIFT-Review in conjunction with the parasitology literature contained in the MEDLINE, Web of Science, Scopus and Dimensions databases. The analyses show that the major research themes are dynamic and continually changing with time, although some themes identified based on keywords such as malaria, nematode, epidemiology and phylogeny are consistently referenced over time. We note the major impact of countries like Brazil has had on the literature of parasitology research. The increase in recent times of research productivity on 'antiparasitics' is discussed, as well as the change in emphasis on different antiparasitic drugs and insecticides over time. In summary, innovation in parasitology is global, extensive, multidisciplinary, constantly evolving and closely aligned with the availability of technology.

Keywords: Bibliometric analyses; database; parasitology; publishing; science mapping analysis; topic models; trends.

Fig. 1.

Schematic representing an overview of the main analyses described in this study.

Fig. 2.

Multidisciplinary nature of Parasitology as displayed through Web of Science categories. The Web of Science was searched with the keywords ‘Parasitology’ or ‘Parasite’ for the years 1989 to 2019 inclusive.

Fig. 3.

VOSviewer visualization map of 6874 publications from a search of Web of Science with the keyword ‘Parasitology’ for the years 1989–2019. Cluster analyses identify 27 clusters that are summarized in Supplementary Material 1. The size of the nodes representing keywords is proportional to the frequency of occurrence of that particular keyword.

Fig. 4.

Example of strategic maps produced from 109 000 publications of the parasitology category in Web of Science using SciMAT for (a) 1989–1994, and (b) 2015–2019. The co-word analyses performed in SciMAT generates a series of clusters that represent groups of keywords and which correspond to the main research topics. The clusters (represented as circles in the figure) are automatically labelled by the most common keyword in the cluster. The axes represent Callon's centrality and density; centrality (on the X-axis) is a measure of the level of interaction amongst the clusters and so is considered a representation of the importance of a cluster (topic) in the development of the entire research field analysed. Density (on the Y-axis) is a measure of the internal strength of the cluster and therefore represents the theme's development. The size of each cluster reflects the number of documents assigned to that cluster.

Fig. 5.

A strategic map produced from the ~ 109 000 papers in the parasitology category of Web of Science using SciMAT for the period 1989–2019. The size of each cluster reflects the H-index assigned to that cluster.

Fig. 6.

VOSviewer visualization map of the 2000 most relevant words present in 174 300 publications identified in the Dimensions database using a search based on ‘parasite’ in title or abstract for the years 1989–2019 inclusive. Analysis in VOSviewer is based on publication title only.