Details
| Original language | English |
|---|---|
| Pages (from-to) | 5759-5795 |
| Number of pages | 37 |
| Journal | SCIENTOMETRICS |
| Volume | 126 |
| Issue number | 7 |
| Early online date | 15 May 2021 |
| Publication status | Published - Jul 2021 |
Abstract
The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
Keywords
- Interpretable Machine Learning, Multidimensional Scaling, Non-Negative Matrix Factorization, Publication Dynamics, Topic Models
ASJC Scopus subject areas
- Social Sciences(all)
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Library and Information Sciences
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In: SCIENTOMETRICS, Vol. 126, No. 7, 07.2021, p. 5759-5795.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Topic space trajectories
T2 - A case study on machine learning literature
AU - Schaefermeier, Bastian
AU - Stumme, Gerd
AU - Hanika, Tom
N1 - Funding Information: This work was funded by the German Federal Ministry of Education and Research (BMBF) in its program “Quantitative Wissenschaftsforschung” as part of the REGIO project under Grant 01PU17012 as well as the German Research Foundation (DFG) priority programme (SPP) 1894 project topikos. Open Access funding enabled and organized by Projekt DEAL.
PY - 2021/7
Y1 - 2021/7
N2 - The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
AB - The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
KW - Interpretable Machine Learning
KW - Multidimensional Scaling
KW - Non-Negative Matrix Factorization
KW - Publication Dynamics
KW - Topic Models
UR - http://www.scopus.com/inward/record.url?scp=85106227550&partnerID=8YFLogxK
U2 - 10.1007/s11192-021-03931-0
DO - 10.1007/s11192-021-03931-0
M3 - Article
AN - SCOPUS:85106227550
VL - 126
SP - 5759
EP - 5795
JO - SCIENTOMETRICS
JF - SCIENTOMETRICS
SN - 0138-9130
IS - 7
ER -