IDEAS home Printed from
   My bibliography  Save this paper

Measuring the Diffusion of Innovations with Paragraph Vector Topic Models


  • David Lenz

    () (Justus-Liebig-University Giessen)

  • Peter Winker

    () (Justus-Liebig-University Giessen)


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

    Download full text from publisher

    File URL:
    File Function: First 201815
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. Vegard H. Larsen & Leif Anders Thorsrud, 2015. "The Value of News," Working Papers No 6/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, March.
    4. 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.
    5. Stathoulopoulos, Kostas & Mateos-Garcia, Juan, 2017. "Mapping without a map: Exploring the UK business landscape using unsupervised learning," SocArXiv ryxdk, Center for Open Science.
    6. Bryan Kelly & Dimitris Papanikolaou & Amit Seru & Matt Taddy, 2018. "Measuring Technological Innovation over the Long Run," NBER Working Papers 25266, National Bureau of Economic Research, Inc.
    7. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 133(2), pages 801-870.
    8. Antonin Bergeaud & Yoann Potiron & Juste Raimbault, 2017. "Classifying patents based on their semantic content," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-22, April.
    9. David Chavalarias & Jean-Philippe Cointet, 2013. "Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    10. Ryohei Hisano & Didier Sornette & Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2013. "High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    11. Leah G. Nichols, 2014. "A topic model approach to measuring interdisciplinarity at the National Science Foundation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 741-754, September.
    12. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    13. Ryohei Hisano & Didier Sornette & Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2012. "High quality topic extraction from business news explains abnormal financial market volatility," Papers 1210.6321,, revised Mar 2013.
    Full references (including those not matched with items on IDEAS)

    More about this item


    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

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:mar:magkse:201815. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bernd Hayo). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.