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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| Vydáno v: | PloS one Ročník 15; číslo 1; s. e0226685 |
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| Médium: | Journal Article |
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Public Library of Science
22.01.2020
Public Library of Science (PLoS) |
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| 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 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Lenz, David Winker, Peter |
| AuthorAffiliation | Department of Economics, Justus-Liebig-University, Gießen, Germany University of Sao Paulo, BRAZIL |
| AuthorAffiliation_xml | – name: University of Sao Paulo, BRAZIL – name: Department of Economics, Justus-Liebig-University, Gießen, Germany |
| Author_xml | – sequence: 1 givenname: David orcidid: 0000-0002-2418-6239 surname: Lenz fullname: Lenz, David – sequence: 2 givenname: Peter surname: Winker fullname: Winker, Peter |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31967999$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0295587 crossref_primary_10_1016_j_ipm_2022_103043 crossref_primary_10_1007_s10842_025_00451_y crossref_primary_10_4018_JOEUC_314786 crossref_primary_10_1016_j_ijar_2023_02_009 crossref_primary_10_1016_j_joi_2021_101224 crossref_primary_10_1016_j_websem_2024_100820 crossref_primary_10_1016_j_eist_2022_04_001 crossref_primary_10_1016_j_rspp_2025_100240 crossref_primary_10_1007_s11192_022_04330_9 crossref_primary_10_1016_j_respol_2021_104373 crossref_primary_10_1016_j_techfore_2021_121280 crossref_primary_10_1038_s41597_025_05422_w crossref_primary_10_1371_journal_pone_0249583 crossref_primary_10_21511_ppm_22_2__2024_24 crossref_primary_10_1016_j_techfore_2022_122130 crossref_primary_10_1016_j_techfore_2022_122098 crossref_primary_10_1515_jbnst_2022_0024 crossref_primary_10_1007_s11192_024_05069_1 crossref_primary_10_1002_wics_1588 crossref_primary_10_18778_1508_2008_27_10 |
| Cites_doi | 10.1007/978-3-642-76153-9_28 10.1257/jep.28.2.3 10.1017/S0266466600009397 10.1016/j.techfore.2013.07.004 10.1111/j.2517-6161.1977.tb01600.x 10.1007/s10506-018-9222-4 10.1093/qje/qjx045 10.1016/j.jeconom.2018.11.013 10.1371/journal.pone.0176310 10.1140/epjds/s13688-017-0123-7 10.1007/978-1-4899-7488-4_196 10.23919/PICMET.2018.8481971 10.1145/2970398.2970409 10.3115/1118108.1118117 10.1007/978-1-4899-7687-1 10.1111/j.1475-4932.2012.00809.x 10.1007/s11192-014-1319-2 10.1016/j.hitech.2003.09.003 10.1007/s11698-018-0171-7 10.1006/dspr.1999.0361 10.1016/j.jbi.2016.06.001 10.1371/journal.pone.0054847 10.1109/TIT.1982.1056489 10.1371/journal.pone.0064846 10.1145/2959100.2959167 10.1016/j.jinteco.2015.12.008 10.1080/00437956.1954.11659520 10.3386/w25266 10.18653/v1/W16-1609 10.1109/TIT.1975.1055330 10.1214/aos/1176344136 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2020 Public Library of Science 2020 Lenz, Winker. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Lenz, Winker 2020 Lenz, Winker |
| Copyright_xml | – notice: COPYRIGHT 2020 Public Library of Science – notice: 2020 Lenz, Winker. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 Lenz, Winker 2020 Lenz, Winker |
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| Title | Measuring the diffusion of innovations with paragraph vector topic models |
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