[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123148-en":3,"doc-seo-123148-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123148,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Forecasting Publication’s Success Using Machine Learning","Measuring the success and impact of scientific publications is a controversial yet widely used task, often relying on citation counts. This work applies a machine learning framework to test whether alternative metrics (altmetrics) can predict a paper’s future impact as reflected in later citations. Using 7,588 papers from 10 computer science journals, the study collects 14 altmetric indices, applies feature selection (variance threshold, Pearson correlation, mutual information), and evaluates three classifiers: decision tree, random forest, and support vector machines.","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","cbCaimwstEwtasBE","https://ap.wps.com/l/cbCaimwstEwtasBE","pdf",508291,1,14,"English","en",105,"# Abstract\n# 1. Introduction\n## Measuring publication success and citation impact\n## Forecasting scientific impact for evaluation decisions","[{\"question\":\"为什么文献成功通常会受到争议？\",\"answer\":\"文献成功与影响的衡量方式存在争议，但引用次数仍被广泛用作流行指标。文档指出，这一做法在学界受到批评且问题仍然突出。\"},{\"question\":\"该研究如何预测论文的未来影响？\",\"answer\":\"研究使用机器学习框架，将替代指标（altmetrics）与未来被引用情况联系起来进行预测建模。文档说明通过收集多种altmetric指标并构建特征空间实现。\"},{\"question\":\"实验使用了哪些特征选择方法和分类器？\",\"answer\":\"特征选择包括方差阈值、皮尔逊相关以及互信息方法，用于缩减并排序特征。分类器则使用决策树、随机森林和支持向量机。\"},{\"question\":\"哪些altmetrics在预测中最有用？\",\"answer\":\"结果表明，最有用的altmetrics包括社交媒体计数、推文数、新闻数、被捕获数以及全文查看次数。并且随机森林在分类表现上优于其他模型。\"}]","Forecasting Publication’s Success Using Machine Learning | PDF",1785814898,35,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"forecasting-publications-success-using-machine-learning","",{"@graph":36,"@context":89},[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/123148/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"为什么文献成功通常会受到争议？","Question",{"text":75,"@type":76},"文献成功与影响的衡量方式存在争议，但引用次数仍被广泛用作流行指标。文档指出，这一做法在学界受到批评且问题仍然突出。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"该研究如何预测论文的未来影响？",{"text":80,"@type":76},"研究使用机器学习框架，将替代指标（altmetrics）与未来被引用情况联系起来进行预测建模。文档说明通过收集多种altmetric指标并构建特征空间实现。",{"name":82,"@type":73,"acceptedAnswer":83},"实验使用了哪些特征选择方法和分类器？",{"text":84,"@type":76},"特征选择包括方差阈值、皮尔逊相关以及互信息方法，用于缩减并排序特征。分类器则使用决策树、随机森林和支持向量机。",{"name":86,"@type":73,"acceptedAnswer":87},"哪些altmetrics在预测中最有用？",{"text":88,"@type":76},"结果表明，最有用的altmetrics包括社交媒体计数、推文数、新闻数、被捕获数以及全文查看次数。并且随机森林在分类表现上优于其他模型。","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]