Forecasting Publication’s Success Using Machine Learning

Rand Alchokr (Corresponderende auteur), Rayed Haider (Corresponderende auteur), Yusra Shakeel (Corresponderende auteur), Thomas Leich, Gunter Saake, Jacob Krüger

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

Measuring the success and impact of a scientific publication is an important, thus controversial matter. Despite all the criticism, it is widespread that citation counts is considered a popular indication of a publication‘s success. Therefore, in this paper, we use a machine learning framework to test the ability of alternative metrics (altmetrics) to predict the future impact of papers reflected in the citation counts. To achieve the experiment, we extracted 7,588 papers from 10 computer science journals. To build the feature space for the prediction problem, 14 different altmetric indices were collected, 3 feature selection approaches, namely, Variance threshold, Pearson’s Correlation, and Mutual information method, were used to minimize the feature space and rank the features according to their contribution to the original dataset. To identify the classification performance of these features, three classifiers were used: Decision Tree, Random Forest, and Support Vector Machines. According to the experimental data, altmetrics can predict future citations and the most useful altmetrics indications are social media count, tweets, news count, capture count, and full-text view, with Random Forest outperforming the other classifiers.
Originele taal-2Engels
TitelBIR 2023 : Bibliometric-enhanced Information Retrieval
SubtitelProceedings of the 13th International Workshop on Bibliometric-enhanced Information Retrieval co-located with 45th European Conference on Information Retrieval (ECIR 2023)
RedacteurenIngo Frommholz, Philipp Mayr, Guillaume Cabanac, Suzan Verberne, Jordan Brennan
UitgeverijCEUR-WS.org
Pagina's77-89
Aantal pagina's13
StatusGepubliceerd - 2023
Evenement13th International Workshop on Bibliometric-enhanced Information Retrieval, BIR 2023 - Dublin, Ierland
Duur: 2 apr. 20232 apr. 2023

Publicatie series

NaamCEUR Workshop Proceedings
Volume3617
ISSN van elektronische versie1613-0073

Workshop

Workshop13th International Workshop on Bibliometric-enhanced Information Retrieval, BIR 2023
Verkorte titelBIR 2023
Land/RegioIerland
StadDublin
Periode2/04/232/04/23

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