Measuring the diffusion of innovations with paragraph vector topic models

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...

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Veröffentlicht in:PloS one Jg. 15; H. 1; S. e0226685
Hauptverfasser: Lenz, David, Winker, Peter
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 22.01.2020
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Analysis
Comprehension
Computer and Information Sciences
Data mining
Datasets
Diffusion
Diffusion of Innovation
Economic development
Economic growth
Embryology
Engineering and Technology
Humans
Indicators
Information Technology
Innovations
Machine Learning
Methods
Natural language processing
News
Physical Sciences
Research and Analysis Methods
Review Literature as Topic
Search engines
Search strategies
Semantics
Social Sciences
Studies
Support Vector Machine
Technological change
Technology
Technology application
Trends
Germany
ISSN:1932-6203, 1932-6203
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Zusammenfassung: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 to 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. In using PVTM, we identify innovation-related topics from 170, 000 technology news articles published over a span of 20 years and gather insights about their diffusion state by measuring the topic importance in the corpus over time. Our results suggest that PVTM is a credible alternative to widely used topic models for the discovery of latent topics in (technology-related) news articles. An examination of three exemplary topics shows that innovation diffusion could be assessed using topic importance measures derived from PVTM. Thereby, we find that PVTM diffusion indicators for certain topics are Granger causal to Google Trend indices with matching search terms.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0226685