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Measuring the Diffusion of Innovations with Paragraph Vector Topic Models

Author

Listed:
  • David Lenz

    (Justus-Liebig-University Giessen)

  • Peter Winker

    (Justus-Liebig-University Giessen)

Abstract

Measuring the diffusion of innovations from textual data sources besides patent data has not been studied extensively. However, early and accurate indicators of innovation and the recognition of trends in innovation are mandatory to successfully promote economic growth through technological progress via evidence-based policy making. In this study, we propose Paragraph Vector Topic Model (PVTM) and apply it on technology related news articles to analyze innovation related topics over time and gain insights regarding their diffusion process. PVTM represents documents in a semantic space, which has been shown to capture latent variables of the underlying documents, e.g. the latent topics. Clusters of documents in the semantic space can then be interpreted and transformed into meaningful topics by means of Gaussian mixture modeling. Using PVTM we identify innovation related topics from 170 thousand technology news articles published over a span of 20 years and gather insights about their diffusion state by measuring the topics importance in the corpus over time. Thereby, we find that PVTM diffusion indicators for certain topics are Granger causal to Google Trends indices with matching search terms. Further, our results suggest PVTM is well suited to discover latent topics in (technology related) news articles and that the diffusion of innovations could be assessed using topic importance measures derived from PVTM.

Suggested Citation

  • David Lenz & Peter Winker, 2018. "Measuring the Diffusion of Innovations with Paragraph Vector Topic Models," MAGKS Papers on Economics 201815, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:201815
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Nathan, Max & Rosso, Anna, 2022. "Innovative events: product launches, innovation and firm performance," Research Policy, Elsevier, vol. 51(1).
    3. repec:osf:socarx:t3jrq_v1 is not listed on IDEAS
    4. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    5. Ballester, Omar & Penner, Orion, 2022. "Robustness, replicability and scalability in topic modelling," Journal of Informetrics, Elsevier, vol. 16(1).
    6. Axenbeck, Janna & Breithaupt, Patrick, 2022. "Measuring the digitalisation of firms: A novel text mining approach," ZEW Discussion Papers 22-065, ZEW - Leibniz Centre for European Economic Research.
    7. Albina Latifi & David Lenz & Peter Winker, 2024. "Identification of Innovation Drivers Based on Technology-Related News Articles," MAGKS Papers on Economics 202401, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    9. Winker, Peter, 2023. "Visualizing Topic Uncertainty in Topic Modelling," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277584, Verein für Socialpolitik / German Economic Association.
    10. Janna Axenbeck & Patrick Breithaupt, 2021. "Innovation indicators based on firm websites—Which website characteristics predict firm-level innovation activity?," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    11. Maximilian Maurice Gail & Phil-Adrian Klotz, 2025. "E-book Pricing Under the Agency Model: Lessons from the UK," Journal of Industry, Competition and Trade, Springer, vol. 25(1), pages 1-39, December.
    12. 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.
    13. Axenbeck, Janna & Breithaupt, Patrick, 2019. "Web-based innovation indicators: Which firm website characteristics relate to firm-level innovation activity?," ZEW Discussion Papers 19-063, ZEW - Leibniz Centre for European Economic Research.
    14. Viktoriia Naboka-Krell, 2023. "Construction and Analysis of Uncertainty Indices based on Multilingual Text Representations," MAGKS Papers on Economics 202310, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    15. Qiang Gao & Man Jiang, 2024. "Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4043-4070, July.
    16. Dhar, Suparna & Tarafdar, Pratik & Bose, Indranil, 2022. "Understanding the evolution of an emerging technological paradigm and its impact: The case of Digital Twin," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

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    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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