EconPapers    
Economics at your fingertips  
 

Challenges to the validity of topic reconstruction

Matthias Held (), Grit Laudel () and Jochen Gläser ()
Additional contact information
Matthias Held: TU Berlin
Grit Laudel: TU Berlin
Jochen Gläser: TU Berlin

Scientometrics, 2021, vol. 126, issue 5, No 37, 4536 pages

Abstract: 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.

Keywords: Mapping; Topic reconstruction; Research trails; Validity; Infomap; Network clustering (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-03920-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03920-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-021-03920-3

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03920-3