Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach

Resonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level...

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Main Authors: Tai Wang, Zongkui Zhou, Xiangen Hu, Zhi Liu, Yi Ding, Zhiqiang Cai
Format: Article
Language:English
Published: Elsevier 2018-06-01
Series:Heliyon
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844018302822
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spelling doaj-e01211d739214aa4bd6db33fa87656842020-11-25T02:07:05ZengElsevierHeliyon2405-84402018-06-0146e00659Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approachTai Wang0Zongkui Zhou1Xiangen Hu2Zhi Liu3Yi Ding4Zhiqiang Cai5National Engineering Research Center for E-learning, Central China Normal University, Wuhan, 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, China; Corresponding author.Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, China; School of Psychology, Central China Normal University, Wuhan, 430079, ChinaKey Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, China; School of Psychology, Central China Normal University, Wuhan, 430079, China; Department of Psychology, University of Memphis, TN, 38152, USANational Engineering Research Center for E-learning, Central China Normal University, Wuhan, 430079, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430079, ChinaDepartment of Psychology, University of Memphis, TN, 38152, USAResonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level. To make up the missing link between word and document level resonances, we devoted our contributions to topic resonances in a novel and natural setting: academic commentaries. Ninety-three academic commentaries from ninety-three authors, along with their references and original papers, are analyzed by a latent Dirichlet allocation based natural language processing approach. This approach can decompose a corpus written and read by an author into several topics with different weights, which can reveal the phenomena ignored at word or document level. We found that (1) topic resonances commonly exist between commenters' fundamental input and output topics; (2) output words are re-allocated by commenters to echo salient input topics; (3) commenters are more prone to associate references which focus on the non-dominant input topics; and (4) topic resonance can even be predicted by a Hebbian-like model which matches the aforementioned findings. These findings will continue to enrich our understanding on the relationship among probe, feedback and context.http://www.sciencedirect.com/science/article/pii/S2405844018302822Information scienceLinguisticsPsychology
collection DOAJ
language English
format Article
sources DOAJ
author Tai Wang
Zongkui Zhou
Xiangen Hu
Zhi Liu
Yi Ding
Zhiqiang Cai
spellingShingle Tai Wang
Zongkui Zhou
Xiangen Hu
Zhi Liu
Yi Ding
Zhiqiang Cai
Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
Heliyon
Information science
Linguistics
Psychology
author_facet Tai Wang
Zongkui Zhou
Xiangen Hu
Zhi Liu
Yi Ding
Zhiqiang Cai
author_sort Tai Wang
title Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
title_short Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
title_full Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
title_fullStr Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
title_full_unstemmed Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
title_sort latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
publisher Elsevier
series Heliyon
issn 2405-8440
publishDate 2018-06-01
description Resonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level. To make up the missing link between word and document level resonances, we devoted our contributions to topic resonances in a novel and natural setting: academic commentaries. Ninety-three academic commentaries from ninety-three authors, along with their references and original papers, are analyzed by a latent Dirichlet allocation based natural language processing approach. This approach can decompose a corpus written and read by an author into several topics with different weights, which can reveal the phenomena ignored at word or document level. We found that (1) topic resonances commonly exist between commenters' fundamental input and output topics; (2) output words are re-allocated by commenters to echo salient input topics; (3) commenters are more prone to associate references which focus on the non-dominant input topics; and (4) topic resonance can even be predicted by a Hebbian-like model which matches the aforementioned findings. These findings will continue to enrich our understanding on the relationship among probe, feedback and context.
topic Information science
Linguistics
Psychology
url http://www.sciencedirect.com/science/article/pii/S2405844018302822
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