Abstract
In this paper we utilize an opportunity to construct ground truths for topics in the field of atomic, molecular and optical physics. Our research questions in this paper focus on (i) how to construct a ground truth for topics and (ii) the suitability of common algorithms applied to bibliometric networks to reconstruct these topics. We use the ground truths to test two data models (direct citation and bibliographic coupling) with two algorithms (the Leiden algorithm and the Infomap algorithm). Our results are discomforting: none of the four combinations leads to a consistent reconstruction of the ground truths. No combination of data model and algorithm simultaneously reconstructs all micro-level topics at any resolution level. Meso-level topics are not reconstructed at all. This suggests (a) that we are currently unable to predict which combination of data model, algorithm and parameter setting will adequately reconstruct which (types of) topics, and (b) that a combination of several data models, algorithms and parameter settings appears to be necessary to reconstruct all or most topics in a set of papers.
Highlights
Reconstructing research topics from networks of papers is considered a major challenge that keeps attracting attention, and for which new solutions are suggested (Gläser et al, 2017; Held & Velden, 2019; Klavans & Boyack, 2017b; Šubelj et al, 2016)
This is highly unsatisfying because there is no cumulative growth of knowledge, which means that the local progress made by many bibliometricians does not translate into progress of topic identification as a research area
Our research questions in this paper focus on (i) how to construct a ground truth for topics and (ii) the suitability of common algorithms applied on bibliometric networks to reconstruct these topics
Summary
Reconstructing research topics from networks of papers is considered a major challenge that keeps attracting attention, and for which new solutions are suggested (Gläser et al, 2017; Held & Velden, 2019; Klavans & Boyack, 2017b; Šubelj et al, 2016). If we neglect the validation of approaches to topic reconstruction, we are unable to create knowledge that can be built upon, i.e. we hamper cumulative research and progress in the field: So far, approaches to and results of topic identification exercises appear to be incommensurable. This is highly unsatisfying because there is no cumulative growth of knowledge, which means that the local progress made by many bibliometricians does not translate into progress of topic identification as a research area. Progress of a research area is possible only when findings can be related to each other and be placed in a larger consistent framework, which evolves with each contribution that is placed in it. (Gläser et al, 2017: 987)
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