Which topics are best represented by science maps? An analysis of clustering effectiveness for citation and text similarity networks

TL;DR

This study evaluates how effectively different scientific topics are represented in science maps created from citation and text similarity networks, revealing which categories are best or poorly represented.

Contribution

It provides an analysis of topic representation effectiveness in science maps, comparing citation and text similarity networks for biomedical publications.

Findings

Diseases and psychology are well represented in science maps.

Natural sciences and geographical entities are poorly represented.

Citation networks better represent certain topics in smaller clusters.

Abstract

A science map of topics is a visualization that shows topics identified algorithmically based on the bibliographic metadata of scientific publications. In practice not all topics are well represented in a science map. We analyzed how effectively different topics are represented in science maps created by clustering biomedical publications. To achieve this, we investigated which topic categories, obtained from MeSH terms, are better represented in science maps based on citation or text similarity networks. To evaluate the clustering effectiveness of topics, we determined the extent to which documents belonging to the same topic are grouped together in the same cluster. We found that the best and worst represented topic categories are the same for citation and text similarity networks. The best represented topic categories are diseases, psychology, anatomy, organisms and the techniques and equipment used for diagnostics and therapy, while the worst represented topic categories are natural science fields, geographical entities, information sciences and health care and occupations. Furthermore, for the diseases and organisms topic categories and for science maps with smaller clusters, we found that topics tend to be better represented in citation similarity networks than in text similarity networks.