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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

    as
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    More about this item

    Keywords

    Topic Model; R&D; R&I; STI; Innovation; Indicators; Text Mining; Natural Language Processing; NLP;

    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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