- -

Are patents linked on Twitter? A case study of Google patents

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Are patents linked on Twitter? A case study of Google patents

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Orduña Malea, Enrique es_ES
dc.contributor.author Font-Julian, Cristina I. es_ES
dc.date.accessioned 2023-05-08T18:02:11Z
dc.date.available 2023-05-08T18:02:11Z
dc.date.issued 2022-11 es_ES
dc.identifier.issn 0138-9130 es_ES
dc.identifier.uri http://hdl.handle.net/10251/193218
dc.description.abstract [EN] This study attempts to analyze patents as cited/mentioned documents to better understand the interest, dissemination and engagement of these documents in social environments, laying the foundations for social media studies of patents (social Patentometrics).Particularly, this study aims to determine how patents are disseminated on Twitter by analyzing three elements: tweets linking to patents, users linking to patents, and patents linked from Twitter. To do this, all the tweets containing at least one link to a full-text patent available on Google Patents were collected and analyzed, yielding a total of 126,815 tweets (and 129,001 links) to 86,417 patents. The results evidence an increase of the number of linking tweets over the years, presumably due to the creation of a standardized patent URL ID and the integration of Google Patents and Google Scholar, which took place in 2015. The engagement achieved by these tweets is limited (80.2% of tweets did not attract likes) but increasing notably since 2018. Two super-publisher twitter bot accounts (dailypatent and uspatentbot) are responsible of 53.3% of all the linking tweets, while most accounts are sporadic users linking to patent as part of a conversation. The patents most tweeted are, by far, from United States (87.5% of all links to Google Patents), mainly due to the effect of the two super-publishers. The impact of patents in terms of the number of tweets linking to them is unrelated to their year of publication, status or number of patent citations received, while controversial and media topics might be more determinant factors. However, further research is needed to better understand the topics discussed around patents on Twitter, the users involved, and the metrics attained. Given the increasing number of linking users and linked patents, this study finds Twitter as a relevant source to measure patent-level metrics, shedding light on the impact and interest of patents by the broad public. es_ES
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Scientometrics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification BIBLIOTECONOMIA Y DOCUMENTACION es_ES
dc.title Are patents linked on Twitter? A case study of Google patents es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11192-022-04519-y es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Facultad de Bellas Artes - Facultat de Belles Arts es_ES
dc.description.bibliographicCitation Orduña Malea, E.; Font-Julian, CI. (2022). Are patents linked on Twitter? A case study of Google patents. Scientometrics. 127(11):6339-6362. https://doi.org/10.1007/s11192-022-04519-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11192-022-04519-y es_ES
dc.description.upvformatpinicio 6339 es_ES
dc.description.upvformatpfin 6362 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 127 es_ES
dc.description.issue 11 es_ES
dc.identifier.pmid 36246789 es_ES
dc.identifier.pmcid PMC9549031 es_ES
dc.relation.pasarela S\476306 es_ES
dc.contributor.funder Consejo Superior de Investigaciones Científicas es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Adie, E. (2016). The rise of altmetrics. In A. Tattersall (Ed.), Altmetrics: A practical guide for librarians, researchers and academics (pp. 67–82). Facet Publishing. es_ES
dc.description.references Costas, R., Mongeon, P., Ferreira, M. R., Honk, J., & Franssen, T. (2020). Large-scale identification and characterization of scholars on Twitter. Quantitative Science Studies, 1(2), 771–791. https://doi.org/10.1162/qss_a_00047 es_ES
dc.description.references Costas, R., de Rijcke, S., & Marres, N. (2021). “Heterogeneous couplings”: Operationalizing network perspectives to study science-society interactions through social media metrics. Journal of the Association for Information Science and Technology, 72(5), 595–610. https://doi.org/10.1002/asi.24427 es_ES
dc.description.references Delgado López-Cózar, E., Orduña-Malea, E., & Martín-Martín, A. (2019). Google Scholar as a data source for research assessment. In W. Glänzel, H. F. Moed, U. Schmod, & M. Thelwall (Eds.), Springer handbook of science and technology indicators (pp. 95–127). Springer. es_ES
dc.description.references Díaz-Faes, A. A., Bowman, T. D., & Costas, R. (2019). Towards a second generation of ‘social media metrics’: Characterizing Twitter communities of attention around science. PLoS ONE, 14(5), e0216408. https://doi.org/10.1371/journal.pone.0216408 es_ES
dc.description.references Didegah, F., Mejlgaard, N., & Sørensen, M. P. (2018). Investigating the quality of interactions and public engagement around scientific papers on twitter. Journal of Informetrics, 12(3), 960–971. https://doi.org/10.1016/j.joi.2018.08.002 es_ES
dc.description.references Fang, Z., & Costas, R. (2020). Studying the accumulation velocity of altmetric data tracked by Altmetric. com. Scientometrics, 123(2), 1077–1101. https://doi.org/10.1007/s11192-020-03405-9 es_ES
dc.description.references Fang, Z., Costas, R., Tian, W., Wang, X., & Wouters, P. (2020a). An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics. Scientometrics, 124(3), 2519–2549. https://doi.org/10.1007/s11192-020-03564-9 es_ES
dc.description.references Fang, Z., Dudek, J., & Costas, R. (2020b). The stability of twitter metrics: A study on unavailable twitter mentions of scientific publications. Journal of the Association for Information Science and Technology, 71(12), 1455–1469. https://doi.org/10.1002/asi.24344 es_ES
dc.description.references Fang, Z., Costas, R., Tian, W., Wang, X., & Wouters, P. (2021). How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications. Journal of the Association for Information Science and Technology, 72(7), 918–932. https://doi.org/10.1002/asi.24458 es_ES
dc.description.references Fang, Z., Costas, R., & Wouters, P. (2022). User engagement with scholarly tweets of scientific papers: A large-scale and cross-disciplinary analysis. Scientometrics, 127(8), 4523–4546. https://doi.org/10.1007/s11192-022-04468-6 es_ES
dc.description.references Font-Julián, C. I., Ontalba-Ruipérez, J. A., Orduña-Malea, E., & Thelwall, M. (2022). Which types of online resource support US patent claims? Journal of Informetrics, 16(1), 1–14. https://doi.org/10.1016/j.joi.2021.101247 es_ES
dc.description.references Friedrich, N., Bowman, T. D., Stock, W. G., & Haustein, S. (2015). Adapting sentiment analysis for tweets linking to scientific papers. In A. Ali Salah, Y. Tonta, A.A.A., Salah, C. Sugimoto, & U. Al (Eds.). Proceedings of the 15th International Society of Scientometrics and Informetrics Conference (pp. 107–108) https://www.issi-society.org/proceedings/issi_2015/0107.pdf es_ES
dc.description.references Graham, S., & Hegde, D. (2015). Disclosing patents’ secrets. Science, 347(6219), 236–237. https://doi.org/10.1126/science.1262080 es_ES
dc.description.references Hammarfelt, B. (2021). Linking science to technology: The “patent paper citation” and the rise of patentometrics in the 1980s. Journal of Documentation, 77(6), 1413–1429. https://doi.org/10.1108/JD-12-2020-0218 es_ES
dc.description.references Hassan, S.-U., Saleem, A., Soroya, S. H., Safder, I., Iqbal, S., Jamil, S., Bukhari, F., Aljohani, N. R., & Nawaz, R. (2021). Sentiment analysis of tweets through Altmetrics: A machine learning approach. Journal of Information Science, 47(6), 712–726. https://doi.org/10.1177/0165551520930917 es_ES
dc.description.references Haustein, S., Costas, R., & Larivière, V. (2015). Characterizing social media metrics of scholarly papers: The effect of document properties and collaboration patterns. PLoS ONE, 10(3), e0120495. https://doi.org/10.1371/journal.pone.0120495 es_ES
dc.description.references Haustein, S., Bowman, T. D., & Costas, R. (2016a). Interpreting “altmetrics”: Viewing acts on social media through the lens of citation and social theories. In C. R. Sugimoto (Ed.), Theories of informetrics and scholarly communication: A festschrift in honor of Blaise Cronin (pp. 372–405). De Gruyter. https://doi.org/10.1515/9783110308464-022 es_ES
dc.description.references Haustein, S., Bowman, T. D., Holmberg, K., Tsou, A., Sugimoto, C. R., & Larivière, V. (2016b). Tweets as impact indicators: Examining the implications of automated ‘bot’ accounts on Twitter. Journal of the Association for Information Science and Technology, 67(1), 232–238. https://doi.org/10.1002/asi.23456 es_ES
dc.description.references Holmberg, K. J. (2015). Altmetrics for information professionals: Past, present and future. Chandos Publishing. es_ES
dc.description.references Haustein, S. (2019). Scholarly twitter metrics. In W. Glänzel, F. H. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer handbook of science and technology indicators (pp. 729–760). Springer. https://doi.org/10.1007/978-3-030-02511-3_28 es_ES
dc.description.references Htoo, T. H. H., & Na, J. C. (2017). Disciplinary differences in altmetrics for social sciences. Online Information Review, 41(2), 235–251. https://doi.org/10.1108/OIR-12-2015-0386 es_ES
dc.description.references Kousha, K., & Thelwall, M. (2015). Patent citation analysis with Google. Journal of the Association for Information Science and Technology, 68(1), 48–61. https://doi.org/10.1002/asi.23608 es_ES
dc.description.references Lemley, M. A. (2008). Ignoring Patents. Michigan State Law Review, 2008(1), 19–34. es_ES
dc.description.references Marley, M. (2014). Full-text patent searching on free websites: Tools, tips and tricks. Business Information Review, 31(4), 226–236. https://doi.org/10.1177/0266382114564265 es_ES
dc.description.references Martínez, C. (2011). Patent families: When do different definitions really matter? Scientometrics, 86(1), 39–63. https://doi.org/10.1007/s11192-010-0251-3 es_ES
dc.description.references Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K. L. (2018). Academic information on twitter: A user survey. PLoS ONE, 13(5), e0197265. https://doi.org/10.1371/journal.pone.0197265 es_ES
dc.description.references Moskovkin, V. M., Shigorina, N. A., & Popov, D. (2012). The possibility of using the Google Patents search tool in patentometric analysis (based on the example of the world’s largest innovative companies). Scientific and Technical Information Processing, 39(2), 107–112. https://doi.org/10.3103/S0147688212020086 es_ES
dc.description.references Narayanankutty, A. (2019). PI3K/Akt/mTOR pathway as a therapeutic target for colorectal cancer: A review of preclinical and clinical evidence. Current Drug Targets, 20(12), 1217–1226. https://doi.org/10.2174/1389450120666190618123846 es_ES
dc.description.references Narayanankutty, A. (2022). Pharmacological potentials and nutritional values of tropical and subtropical fruits of India: Emphasis on their anticancer bioactive components. Recent Patents on Anti-Cancer Drug Discovery, 17(2), 124–135. https://doi.org/10.2174/1574892816666211130165200 es_ES
dc.description.references Noruzi, A., & Abdekhoda, M. (2014). Google Patents: The global patent search engine. Webology, 11(1). https://www.webology.org/2014/v11n1/a122.pdf es_ES
dc.description.references Ouellette, L. (2012). Do Patents Disclose Useful Information. Harvard Journal of Law & Technology, 25(2), 545–608. es_ES
dc.description.references Ouellette, L. (2017). Who reads patents? Nature Biotechnology, 35(5), 421–424. https://doi.org/10.1038/nbt.3864 es_ES
dc.description.references Orduña-Malea, E., Martín-Martín, A., & Delgado-López-Cózar, E. (2016). The next bibliometrics: ALMetrics (Author Level Metrics) and the multiple faces of author impact. Profesional De La Información, 25(3), 485–496. https://doi.org/10.3145/epi.2016.may.18 es_ES
dc.description.references Orduna-Malea, E., Thelwall, M., & Kousha, K. (2017). Web citations in patents: Evidence of technological impact? Journal of the Association for Information Science and Technology, 68(8), 1967–1974. https://doi.org/10.1002/asi.23821 es_ES
dc.description.references Ortega, J. L. (2018a). Disciplinary differences of the impact of altmetric. FEMS Microbiology Letters, 365(7), fny049. https://doi.org/10.1093/femsle/fny049 es_ES
dc.description.references Ortega, J. L. (2018b). Reliability and accuracy of altmetric providers: a comparison among Altmetric. com PlumX and crossref event data. Scientometrics, 116(3), 2123–2138. https://doi.org/10.1007/s11192-018-2838-z es_ES
dc.description.references Orduna-Malea, E., & Delgado López-Cózar, E. (2019). Demography of Altmetrics under the light of Dimensions: Locations, institutions, journals, disciplines and funding bodies in the global research framework. Journal of Altmetrics, 2(1), 1–18. https://doi.org/10.29024/joa.13 es_ES
dc.description.references Ortega, J. L. (2020). Altmetrics data providers: A metaanalysis review of the coverage of metrics and publication. Profesional De La Información, 29(1), e290107. https://doi.org/10.3145/epi.2020.ene.07 es_ES
dc.description.references Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday. https://doi.org/10.5210/fm.v15i7.2874 es_ES
dc.description.references Sayyadiharikandeh, M., Varol, O., Yang, K. C., Flammini, A., & Menczer, F. (2020). Detection of novel social bots by ensembles of specialized classifiers. In Proceedings of the 29th ACM international conference on information & knowledge management (CIKM’2’). ACM, (pp. 2725–2732) https://doi.org/10.1145/3340531.3412698 es_ES
dc.description.references Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: A review of the literature. Journal of the Association for Information Science and Technology, 68(9), 2037–2062. https://doi.org/10.1002/asi.23833 es_ES
dc.description.references Tahamtan, I., & Bornmann, L. (2020). Altmetrics and societal impact measurements: Match or mismatch? A literature review. Profesional De La Información, 29(1), e290102. https://doi.org/10.3145/epi.2020.ene.02 es_ES
dc.description.references Thelwall, M. (2015). Evaluating the comprehensiveness of Twitter Search API results A: Four step method. Cybermetrics: International Journal of Scientometrics Informetrics and Bibliometrics, 18, 1. es_ES
dc.description.references Thelwall, M., & Kousha, K. (2015a). Web indicators for research evaluation. Part 1: Citations and links to academic articles from the Web. Profesional De La Información, 24(5), 587–606. https://doi.org/10.3145/epi.2015a.sep.08 es_ES
dc.description.references Thelwall, M., & Kousha, K. (2015b). Web indicators for research evaluation. Part 2: Social media metrics. Profesional De La Informacion, 24(5), 607–620. https://doi.org/10.3145/epi.2015b.sep.09 es_ES
dc.description.references Thelwall, M. (2018). Altmetric prevalence in the social sciences, arts and humanities: Where are the online discussions? Journal of Altmetrics, 1(1), 1–12. https://doi.org/10.29024/joa.6 es_ES
dc.description.references Warren, H. R., Raison, N., & Dasgupta, P. (2017). The Rise of Altmetrics. JAMA, 317(2), 131–132. https://doi.org/10.1001/jama.2016.18346 es_ES
dc.description.references Yu, H., Xiao, T., Xu, S., & Wang, Y. (2019). Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters. Journal of Informetrics, 13(3), 841–855. https://doi.org/10.1016/j.joi.2019.08.001 es_ES
dc.description.references Zahedi, Z., Costas, R., & Wouters, P. (2014). How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications. Scientometrics, 101(2), 1491–1513. https://doi.org/10.1007/s11192-014-1264-0 es_ES
dc.description.references Zahedi, Z., Fenner, M., & Costas, R. (2015). Consistency among altmetrics data provider/aggregators: What are the challenges?. In: Altmetrics15: 5 years in, what do we know? (pp. 1–3). Amsterdam, The Netherlands. http://altmetrics.org/wp-content/uploads/2015/09/altmetrics15_paper_14.pdf es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem