[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123147-en":3,"doc-seo-123147-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123147,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Forecasting Publications’ Success Using Machine Learning","Measuring the success and impact of scientific publications remains a widely discussed challenge, with citation counts often treated as a popular proxy. This study applies a machine learning framework to evaluate whether alternative metrics (altmetrics) can predict future paper impact as reflected in citation counts. From 7,588 papers across 10 computer science journals, the authors compile 14 altmetric indices, perform feature selection, and compare three classifiers. Results show altmetrics can predict future citations, with social media count, tweets, news count, capture count, and full-text views emerging as most informative, and Random Forest achieving the best performance.","Forecasting Publication’s Success Using Machine Learning  \nCitation for published version (APA):  \nAlchokr, R. , Haider, R. , Shakeel, Y. , Leich, T. , Saake, G. , & Krüger, J. (2023) . Forecasting Publication’s Success Using Machine Learning. In I. Frommholz, P. Mayr, G. Cabanac, S. Verberne, & J. Brennan (Eds.), BIR 2023 : Bibliometric-enhanced Information Retrieval: Proceedings of the 13th International Workshop on Bibliometricenhanced Information Retrieval co-located with 45th European Conference on Information Retrieval (ECIR 2023) (pp. 77-89) . (CEUR Workshop Proceedings; Vol. 3617) . [CEUR-WS.org](CEUR-WS.org). [https://jacobkrueger.github.io/assets/papers/Alchokr2023ForcastingSuccess.pdf](https://jacobkrueger.github.io/assets/papers/Alchokr2023ForcastingSuccess.pdf)  \nDocument license:  \nCC BY  \nDocument status and date:  \nPublished: 01/01/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 01. Feb. 2025  \nForecasting Publications’ Success Using Machine Learning Prediction Models  \nRand Alchokr1,∗ , Rayed Haider3,∗ , Yusra Shakeel1,2,∗ , Thomas Leich3,4, Gunter Saake1 and Jacob Krüger5  \n1 Otto-von-Guericke University, Magdeburg, Germany 2 Karlsruhe Institute of Technology, Karlsruhe, Germany  \n3 Hochschule Harz, Wernigerode, Germany 4 METOP GmbH, Magdeburg, Germany  \n5 Eindhoven University of Technology, The Netherlands  \nAbstract  \nMeasuring 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","cbCaiujLElhgHyeM","https://ap.wps.com/l/cbCaiujLElhgHyeM","pdf",508291,1,14,"English","en",105,"# Abstract\n# 1. Introduction\n## Success measurement and citation controversy\n## Forecasting impact for evaluation decisions\n# Methodology overview\n## Data extraction from journals\n## Altmetric feature engineering and selection\n## Classifiers and evaluation focus","[{\"question\":\"What problem does the paper address about publication success?\",\"answer\":\"The paper targets how to measure and forecast the future success and impact of scientific papers, noting that citation counts are commonly used but also controversial.\"},{\"question\":\"How is machine learning used to predict future impact?\",\"answer\":\"A machine learning framework tests whether alternative metrics (altmetrics) can predict future citation-based impact. It builds a feature space from 14 altmetric indices and evaluates classifiers.\"},{\"question\":\"Which altmetrics were found most useful and which model performed best?\",\"answer\":\"The most useful indications were social media count, tweets, news count, capture count, and full-text view. Random Forest outperformed Decision Tree and Support Vector Machines.\"}]","Forecasting Publications’ Success Using Machine Learning | PDF",1785814896,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-publications-success-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-publications-success-using-machine-learning/123147/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address about publication success?","Question",{"text":75,"@type":76},"The paper targets how to measure and forecast the future success and impact of scientific papers, noting that citation counts are commonly used but also controversial.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is machine learning used to predict future impact?",{"text":80,"@type":76},"A machine learning framework tests whether alternative metrics (altmetrics) can predict future citation-based impact. It builds a feature space from 14 altmetric indices and evaluates classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"Which altmetrics were found most useful and which model performed best?",{"text":84,"@type":76},"The most useful indications were social media count, tweets, news count, capture count, and full-text view. 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