research-article

Detecting topic evolution in scientific literature: how can citations help?

Authors:

Prasenjit Mitra,

Lee GilesAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 957 - 966

https://doi.org/10.1145/1645953.1646076

Published: 02 November 2009 Publication History

Abstract

Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.

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Cited By

Paul DChakdar DSaha SMathew J(2024)Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325350211:1(1013-1022)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3253502
Paul DChakdar DSaha SMathew J(2023)Multiview Deep Online Clustering: An Application to Online Research Topic Modeling and RecommendationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318734210:5(2566-2578)Online publication date: Oct-2023
https://doi.org/10.1109/TCSS.2022.3187342
Encinas Quille RBarros JJúnior MDe Almeida FCorrêa P(2023)Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic ModelingIEEE Access10.1109/ACCESS.2023.326966011(41535-41545)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269660
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Index Terms

Detecting topic evolution in scientific literature: how can citations help?
1. Mathematics of computing
  1. Information theory

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2009

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November 2 - 6, 2009

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Cited By

Paul DChakdar DSaha SMathew J(2024)Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325350211:1(1013-1022)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3253502
Paul DChakdar DSaha SMathew J(2023)Multiview Deep Online Clustering: An Application to Online Research Topic Modeling and RecommendationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318734210:5(2566-2578)Online publication date: Oct-2023
https://doi.org/10.1109/TCSS.2022.3187342
Encinas Quille RBarros JJúnior MDe Almeida FCorrêa P(2023)Detecting Favorite Topics in Computing Scientific Literature via Dynamic Topic ModelingIEEE Access10.1109/ACCESS.2023.326966011(41535-41545)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269660
Ma JWang LZhang YYuan WGuo W(2023)An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to localExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118695212:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118695
Taher Harikandeh SAliakbary STaheri S(2023)An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomainsScientometrics10.1007/s11192-023-04642-4128:3(1567-1582)Online publication date: 31-Jan-2023
https://doi.org/10.1007/s11192-023-04642-4
Geller MVasconcelos VPinheiro F(2023)Toxicity in Evolving Twitter TopicsComputational Science – ICCS 202310.1007/978-3-031-36027-5_4(40-54)Online publication date: 26-Jun-2023
https://doi.org/10.1007/978-3-031-36027-5_4
Zheng ZSieber R(2023)Interpreting the Smart City Through Topic ModelingIntelligence for Future Cities10.1007/978-3-031-31746-0_3(29-46)Online publication date: 2-Jun-2023
https://doi.org/10.1007/978-3-031-31746-0_3
Jung SSegev AAlam MBuscaldi DCochez MOsborne FRefogiato Recupero DSack H(2022)Analyzing the generalizability of the network-based topic emergence identification methodSemantic Web10.3233/SW-21295113:3(423-439)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/SW-212951
Nguyen NBoros ELejeune GDoucet ADelahaut T(2022)Utilizing Keywords Evolution in Context for Emerging Trend Detection in Scientific PublicationsProceedings of the 11th International Symposium on Information and Communication Technology10.1145/3568562.3568640(247-253)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3568562.3568640
Gialitsis NKotitsas SPapageorgiou H(2022)SciNoBo: A Hierarchical Multi-Label Classifier of Scientific PublicationsCompanion Proceedings of the Web Conference 202210.1145/3487553.3524677(800-809)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524677
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