On author self-citations as carriers of scientific topic structure information
Bibliographic Data
| ID | 21255913 |
|---|---|
| Authors | Paul Donner (0000-0001-5737-8483, German Centre for Higher Education Research and Science Studies), Edwin Henneken (0000-0003-4264-2450, Center for Astrophysics Harvard & Smithsonian) |
| Year | 2025 |
| Volume | 6 |
| Pages | 445-462 |
| Publication date | 2025-04-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quantitative Science Studies (JOURNAL) |
| Journal identifiers | ISSN: 2641-3337 • E-ISSN: 2641-3337 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/qss_a_00357 |
| OpenAlex | W4407942239 |
| Language | EN |
| Citations received | 1 |
| References cited | 35 |
Author self-citations are a somewhat controversial phenomenon. Some scholars maintain they are a normal, even indispensable, part of scientific referencing practice, while others claim they are frequently an expression of vanity and self-promotion. Citations are the basic data for citation network clustering, an important approach to creating bottom-up, data-driven, global taxonomic systems of research publications. Thus, the topical information content of self-citations is of particular interest in this context. Because it is not yet known how author self-citations affect such systems, we study the question of their influence on cluster solution topic quality in a citation network by experimentally reweighting self-citation edges by increasing and decreasing their link weights according to self-citation status and strength. As a case study, we investigate data in the field of astronomy and astrophysics. We assess the effects of self-citation manipulations by evaluating the quality of the resulting clustering solutions using diverse external data containing meaningful topic-structural information, namely topical journal special issues, cousage data from scientific literature database search logs, grant funding data, and intellectual paper-level classification assignments. We find that we can reliably improve clustering solution quality by emphasizing self-citation link weights
Data science · Information retrieval · Computer Science · Psychology · Topic Modeling
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |