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Forecasting Publications’ Success Using Machine Learning Prediction Models

Alchokr, Rand; Haider, Rayed; Shakeel, Yusra ORCID iD icon 1; Leich, Thomas; Saake, Gunter; Krüger, Jacob
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000168686
Veröffentlicht am 28.02.2024
Scopus
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Karlsruher Institut für Technologie (KIT)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 1613-0073
KITopen-ID: 1000168686
Erschienen in Proceedings of the 13th International Workshop on Bibliometric-enhanced Information Retrieval co-located with 45th European Conference on Information Retrieval (ECIR 2023)
Veranstaltung 13th International Workshop on Bibliometric-enhanced Information Retrieval (2023), Dublin, Irland, 02.04.2023
Verlag CEUR-WS
Seiten 77 – 89
Serie CEUR workshop proceedings ; 3617
Vorab online veröffentlicht am 15.01.2024
Externe Relationen Abstract/Volltext
Schlagwörter Bibliometric, alternative metrics, machine learning, computer science
Nachgewiesen in Scopus
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