FOCAS: Penalising friendly citations to improve author ranking

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Silva, J (Autor) Outra; Aparicio, D (Autor) Outra; Pedro Ribeiro (Autor) FCUP; Silva, F (Autor) FCUP

Ata de Conferência Internacional

Título: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Páginas: 1852-1860

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00R-ZAW

DOI: 10.1145/3341105.3373991

Abstract (EN): Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9