IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i7d10.1007_s11192-022-04439-x.html
   My bibliography  Save this article

Evolution analysis of online topics based on ‘word-topic’ coupling network

Author

Listed:
  • Hengmin Zhu

    (Nanjing University of Posts and Telecommunications)

  • Li Qian

    (Nanjing University of Posts and Telecommunications)

  • Wang Qin

    (University of Southern California)

  • Jing Wei

    (Nanjing University of Posts and Telecommunications)

  • Chao Shen

    (Nanjing University of Posts and Telecommunications)

Abstract

Analyzing topic evolution is an effective way to monitor the overview of topic spreading. Existing methods have focused either on the intensity evolution of topics along a timeline or the topic evolution path of technical literature. In this paper, we aim to study topic evolution from a micro perspective, which not only captures the topic timeline but also reveals the topic status and the directed evolutionary path among topics. Firstly, we construct a word network by co-occurrence relationship between feature words. Secondly, Latent Dirichlet allocation (LDA) model is used to automatically extract topics and capture the mapping relationship between words and topics, and then a ‘word-topic’ coupling network is built. Thirdly, based on the ‘word-topic’ coupling network, we describe the topic intensity evolution over time and measure topic status considering the contribution of feature words to a topic. The concept of topic drifting probability is proposed to identify the evolutionary path. Experimental results conducted on two real-world data sets of “COVID-19” demonstrate the effectiveness of our proposed method.

Suggested Citation

  • Hengmin Zhu & Li Qian & Wang Qin & Jing Wei & Chao Shen, 2022. "Evolution analysis of online topics based on ‘word-topic’ coupling network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3767-3792, July.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04439-x
    DOI: 10.1007/s11192-022-04439-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04439-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-022-04439-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Juanjuan Zhao & Weili Wu & Xiaolong Zhang & Yan Qiang & Tao Liu & Lidong Wu, 2014. "A short-term trend prediction model of topic over Sina Weibo dataset," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 613-625, October.
    2. Hong Wu & Huifang Yi & Chang Li, 2021. "An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6301-6321, August.
    3. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Liu, Ziqiang & Yuan, Guoting, 2020. "Topic-linked innovation paths in science and technology," Journal of Informetrics, Elsevier, vol. 14(2).
    4. Chen, Baitong & Tsutsui, Satoshi & Ding, Ying & Ma, Feicheng, 2017. "Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 11(4), pages 1175-1189.
    5. Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weibin Lin & Xianli Wu & Zhengwei Wang & Xiaoji Wan & Hailin Li, 2022. "Topic Network Analysis Based on Co-Occurrence Time Series Clustering," Mathematics, MDPI, vol. 10(16), pages 1-17, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    2. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    3. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    4. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
    5. Chen, Hongshu & Jin, Qianqian & Wang, Ximeng & Xiong, Fei, 2022. "Profiling academic-industrial collaborations in bibliometric-enhanced topic networks: A case study on digitalization research," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Jian Xu & Yi Bu & Ying Ding & Sinan Yang & Hongli Zhang & Chen Yu & Lin Sun, 2018. "Understanding the formation of interdisciplinary research from the perspective of keyword evolution: a case study on joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 973-995, November.
    7. Yating Li & Ye Chen & Qiyu Wang, 2021. "Evolution and diffusion of information literacy topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4195-4224, May.
    8. Zhentao Liang & Jin Mao & Kun Lu & Gang Li, 2021. "Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9519-9542, December.
    9. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    10. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2023. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3313-3335, June.
    11. Huixin Wang & Jing Xie & Shixian Luo & Duy Thong Ta & Qian Wang & Jiao Zhang & Daer Su & Katsunori Furuya, 2023. "Exploring the Interplay between Landscape Planning and Human Well-Being: A Scientometric Review," Land, MDPI, vol. 12(7), pages 1-24, June.
    12. Mauricio Marrone, 2020. "Application of entity linking to identify research fronts and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 357-379, January.
    13. Lukun Zheng & Yuhang Jiang, 2022. "Combining dissimilarity measures for quantifying changes in research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3751-3765, July.
    14. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    15. Ba, Zhichao & Meng, Kai & Ma, Yaxue & Xia, Yikun, 2024. "Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    16. Qian, Yue & Liu, Yu & Sheng, Quan Z., 2020. "Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence," Journal of Informetrics, Elsevier, vol. 14(3).
    17. Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
    18. Huichen Gao & Shijuan Wang, 2022. "The Intellectual Structure of Research on Rural-to-Urban Migrants: A Bibliometric Analysis," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
    19. Keye Wu & Ziyue Xie & Jia Tina Du, 2024. "Does science disrupt technology? Examining science intensity, novelty, and recency through patent-paper citations in the pharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5469-5491, September.
    20. Doo-San Kim & Byeong-Cheol Lee & Kwang-Hi Park, 2021. "Determination of Motivating Factors of Urban Forest Visitors through Latent Dirichlet Allocation Topic Modeling," IJERPH, MDPI, vol. 18(18), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04439-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.