A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemi...

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Autores principales:	Naif Radi Aljohani, Ayman Fayoumi, Saeed-Ul Hassan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	citations context classification citation network analysis deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/63788d17205f424ea64ac02a8cce9b0c
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id	oai:doaj.org-article:63788d17205f424ea64ac02a8cce9b0c
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spelling	oai:doaj.org-article:63788d17205f424ea64ac02a8cce9b0c2021-11-25T16:42:25ZA Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network10.3390/app1122109702076-3417https://doaj.org/article/63788d17205f424ea64ac02a8cce9b0c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10970https://doaj.org/toc/2076-3417We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.Naif Radi AljohaniAyman FayoumiSaeed-Ul HassanMDPI AGarticlecitations context classificationcitation network analysisdeep learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10970, p 10970 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	citations context classification citation network analysis deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	citations context classification citation network analysis deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Naif Radi Aljohani Ayman Fayoumi Saeed-Ul Hassan A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network
description	We investigated the scientific research dissemination by analyzing the publications and citation data, implying that not all citations are significantly important. Therefore, as alluded to existing state-of-the-art models that employ feature-based techniques to measure the scholarly research dissemination between multiple entities, our model implements the convolutional neural network (CNN) with fastText-based pre-trained embedding vectors, utilizes only the citation context as its input to distinguish between important and non-important citations. Moreover, we speculate using focal-loss and class weight methods to address the inherited class imbalance problems in citation classification datasets. Using a dataset of 10 K annotated citation contexts, we achieved an accuracy of 90.7% along with a 90.6% f1-score, in the case of binary classification. Finally, we present a case study to measure the comprehensiveness of our deployed model on a dataset of 3100 K citations taken from the ACL Anthology Reference Corpus. We employed state-of-the-art graph visualization open-source tool Gephi to analyze the various aspects of citation network graphs, for each respective citation behavior.
format	article
author	Naif Radi Aljohani Ayman Fayoumi Saeed-Ul Hassan
author_facet	Naif Radi Aljohani Ayman Fayoumi Saeed-Ul Hassan
author_sort	Naif Radi Aljohani
title	A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network
title_short	A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network
title_full	A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network
title_fullStr	A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network
title_full_unstemmed	A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network
title_sort	novel deep neural network-based approach to measure scholarly research dissemination using citations network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/63788d17205f424ea64ac02a8cce9b0c
work_keys_str_mv	AT naifradialjohani anoveldeepneuralnetworkbasedapproachtomeasurescholarlyresearchdisseminationusingcitationsnetwork AT aymanfayoumi anoveldeepneuralnetworkbasedapproachtomeasurescholarlyresearchdisseminationusingcitationsnetwork AT saeedulhassan anoveldeepneuralnetworkbasedapproachtomeasurescholarlyresearchdisseminationusingcitationsnetwork AT naifradialjohani noveldeepneuralnetworkbasedapproachtomeasurescholarlyresearchdisseminationusingcitationsnetwork AT aymanfayoumi noveldeepneuralnetworkbasedapproachtomeasurescholarlyresearchdisseminationusingcitationsnetwork AT saeedulhassan noveldeepneuralnetworkbasedapproachtomeasurescholarlyresearchdisseminationusingcitationsnetwork
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A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network

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