IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v103y2015i2d10.1007_s11192-015-1554-1.html
   My bibliography  Save this article

A decade of research in statistics: a topic model approach

Author

Listed:
  • Francesca De Battisti

    (Università degli Studi di Milano)

  • Alfio Ferrara

    (Università degli Studi di Milano)

  • Silvia Salini

    (Università degli Studi di Milano)

Abstract

Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they are not related to the other important metadata usually available with publications, such as authors affiliation, publication venue, and publication year. In this paper, we propose a methodological approach to topic modeling and post-processing of topic models results to the end of describing in depth a field of research over time. In particular, we work on a selection of publications from the international statistical literature, we propose an approach that allows us to identify sophisticated topic descriptors, and we analyze the links between topics and their temporal evolution.

Suggested Citation

  • Francesca De Battisti & Alfio Ferrara & Silvia Salini, 2015. "A decade of research in statistics: a topic model approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 413-433, May.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:2:d:10.1007_s11192-015-1554-1
    DOI: 10.1007/s11192-015-1554-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-015-1554-1
    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/s11192-015-1554-1?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. Alfio Ferrara & Silvia Salini, 2012. "Ten challenges in modeling bibliographic data for bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 765-785, December.
    2. Schell, Michael J., 2010. "Identifying Key Statistical Papers From 1985 to 2002 Using Citation Data for Applied Biostatisticians," The American Statistician, American Statistical Association, vol. 64(4), pages 310-317.
    3. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    4. Thomas Ryan & William Woodall, 2005. "The most-cited statistical papers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(5), pages 461-474.
    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. 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).
    2. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    3. Nadeem Shafique Butt & Ahmad Azam Malik & Muhammad Qaiser Shahbaz, 2021. "Bibliometric Analysis of Statistics Journals Indexed in Web of Science Under Emerging Source Citation Index," SAGE Open, , vol. 11(1), pages 21582440209, January.
    4. Seungsu Paek & Namhyoung Kim, 2021. "Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    5. Savin, Ivan & Ott, Ingrid & Konop, Chris, 2022. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    6. Ivan Savin & Kristina Chukavina & Andrey Pushkarev, 2023. "Topic-based classification and identification of global trends for startup companies," Small Business Economics, Springer, vol. 60(2), pages 659-689, February.
    7. Hanchen Jiang & Maoshan Qiang & Dongcheng Zhang & Qi Wen & Bingqing Xia & Nan An, 2018. "Climate Change Communication in an Online Q&A Community: A Case Study of Quora," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    8. Seongyoun Hong & Taejung Park & Jaewon Choi, 2020. "Analyzing Research Trends in University Student Experience Based on Topic Modeling," Sustainability, MDPI, vol. 12(9), pages 1-11, April.
    9. Hosang Jung & Boram Kim, 2021. "Identifying Research Topics and Trends in Asset Management for Sustainable Use: A Topic Modeling Approach," Sustainability, MDPI, vol. 13(9), pages 1-14, April.
    10. Xu, Ran & Baghaei Lakeh, Arash & Ghaffarzadegan, Navid, 2021. "Examining the characteristics of impactful research topics: A case of three decades of HIV-AIDS research," Journal of Informetrics, Elsevier, vol. 15(1).
    11. Md Nazrul Islam & Md Mofazzal Hossain & Md Shafayet Shahed Ornob, 2024. "Business research on Industry 4.0: a systematic review using topic modelling approach," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    12. Hakyeon Lee & Hanbin Seo & Youngjung Geum, 2018. "Uncovering the Topic Landscape of Product-Service System Research: from Sustainability to Value Creation," Sustainability, MDPI, vol. 10(4), pages 1-15, March.
    13. Wu, Jia-Jhou & Chang, Sue-Ting, 2020. "Exploring customer sentiment regarding online retail services: A topic-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    14. Zhou, Yusheng & Wang, Xueqin & Yuen, Kum Fai, 2021. "Sustainability disclosure for container shipping: A text-mining approach," Transport Policy, Elsevier, vol. 110(C), pages 465-477.
    15. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    16. Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
    17. Sung-Ho Shin & Oh Kyoung Kwon & Xiao Ruan & Prem Chhetri & Paul Tae-Woo Lee & Shahrooz Shahparvari, 2018. "Analyzing Sustainability Literature in Maritime Studies with Text Mining," Sustainability, MDPI, vol. 10(10), pages 1-19, September.
    18. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.
    19. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.
    20. Hongshu Chen & Xinna Song & Qianqian Jin & Ximeng Wang, 2022. "Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6637-6660, November.

    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. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    2. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
    3. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    4. Arsenyan, Jbid & Mirowska, Agata & Piepenbrink, Anke, 2023. "Close encounters with the virtual kind: Defining a human-virtual agent coexistence framework," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    5. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    6. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    7. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.
    8. Md Nazrul Islam & Md Mofazzal Hossain & Md Shafayet Shahed Ornob, 2024. "Business research on Industry 4.0: a systematic review using topic modelling approach," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    9. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Nadeem Shafique Butt & Ahmad Azam Malik & Muhammad Qaiser Shahbaz, 2021. "Bibliometric Analysis of Statistics Journals Indexed in Web of Science Under Emerging Source Citation Index," SAGE Open, , vol. 11(1), pages 21582440209, January.
    11. Daoud, Adel & Kohl, Sebastian, 2016. "How much do sociologists write about economic topics? Using big data to test some conventional views in economic sociology, 1890 to 2014," MPIfG Discussion Paper 16/7, Max Planck Institute for the Study of Societies.
    12. Cardinale, Roberto & Cardinale, Ivano & Zupic, Ivan, 2024. "The EU's vulnerability to gas price and supply shocks: The role of mismatches between policy beliefs and changing international gas markets," Energy Economics, Elsevier, vol. 131(C).
    13. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    14. Hsia-Ching Chang, 2016. "The Synergy of Scientometric Analysis and Knowledge Mapping with Topic Models: Modelling the Development Trajectories of Information Security and Cyber-Security Research," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-33, December.
    15. Holand, Øystein & Contiero, Barbara & Næss, Marius W. & Cozzi, Giulio, 2024. "“The Times They Are A-Changin' “ – research trends and perspectives of reindeer pastoralism – A review using text mining and topic modelling," Land Use Policy, Elsevier, vol. 136(C).
    16. Ozgun, Burcu & Broekel, Tom, 2021. "The geography of innovation and technology news - An empirical study of the German news media," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    17. Anita Mendiratta & Shveta Singh & Surendra Singh Yadav & Arvind Mahajan, 2023. "Bibliometric and Topic Modeling Analysis of Corporate Social Irresponsibility," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 319-339, September.
    18. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    19. Lüdering Jochen & Winker Peter, 2016. "Forward or Backward Looking? The Economic Discourse and the Observed Reality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 483-515, August.
    20. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, October.

    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:103:y:2015:i:2:d:10.1007_s11192-015-1554-1. 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.