Advertisement

Discovering Platform Government Research Trends Using Topic Modeling

  • Sun-Young Shin
  • Chang-Kyo SuhEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11815)

Abstract

Platform government is a key trend in the 4th industrial revolution. This paper presents an empirical analysis of 5810 articles in the Science Direct Database yielded by a search for the keyword ‘platform government’ from 1998 to 2017. Applying topic modeling to the article abstracts identified 9 key topics that were both representative and meaningful, and essentially corresponded to established sub-fields in platform government research. Measuring the variation of topic distributions over time revealed various rising research trends, such as data analytics and IoT, and a recent increasing popularity of business and governance. The identified key topics and Korean platform government projects were also compared. In conclusion, this study attempted to improve the identification, quantification, and understanding of the themes and trends in platform government over the last 20 years in order to provide a valuable tool for researchers and government agencies to make more informed decisions.

Keywords

Platform Government Topic modeling Text mining Research trends 

References

  1. 1.
    The Innovation Growth Engine. Korea Government, Seoul (2018)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).  https://doi.org/10.1145/1015330.1015439CrossRefzbMATHGoogle Scholar
  3. 3.
    Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pp. 424–433, ACM (2006).  https://doi.org/10.1145/1150402.1150450
  4. 4.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101, 5228–5235 (2004).  https://doi.org/10.1073/pnas.0307752101CrossRefGoogle Scholar
  5. 5.
    Amado, A., Cortez, P., Rita, P., Moro, S.: Research trends on big data in marketing: a text mining and topic modeling based literature analysis. Eur. Res. Manage. Bus. Econ. 24, 1–7 (2018).  https://doi.org/10.1016/j.iedeen.2017.06.002CrossRefGoogle Scholar
  6. 6.
    Mimno, D., McCallum, A.: Topic models conditioned on arbitrary features with Dirichlet-multinomial regression. UAI, pp. 401–418, arXiv preprint (2008)Google Scholar
  7. 7.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55, 77–84 (2012).  https://doi.org/10.1145/2133806.2133826CrossRefGoogle Scholar
  8. 8.
    Reich, J., Tingley, D., Leder-Luis, J., Roberts, M., Stewart, B.: Computer-assisted reading and discovery for student generated text in massive open online courses. J. Learn. Anal. 2, 156–184 (2014).  https://doi.org/10.2139/ssrn.2499725CrossRefGoogle Scholar
  9. 9.
    Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Handbook of Latent Semantic Analysis, vol. 427, pp. 424–440 (2007).  https://doi.org/10.4324/9780203936399.ch21
  10. 10.
    Gohr, A., Hinneburg, A., Schult, R., Spiliopoulou, M.: Topic evolution in a stream of documents. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 859–870. Society for Industrial and Applied Mathematics (2009).  https://doi.org/10.1137/1.9781611972795.74
  11. 11.
    Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 113–120. ACM (2006).  https://doi.org/10.1145/1143844.1143859
  12. 12.
    Jo, Y., Hopcroft, J.E., Lagoze, C.: The web of topics: discovering the topology of topic evolution in a corpus. In: Proceedings of the 20th International Conference on World Wide Web, pp. 257–266. ACM (2011).  https://doi.org/10.1145/1963405.1963444
  13. 13.
    Margaret, E., Roberts, B., Dustin, T., Christopher, L., Jetson, L., Shana, K., David, G.: Rand structural topic models for open-ended survey responses. Am. J. Polit. Sci. 1, 1–19 (2014).  https://doi.org/10.1111/ajps.12103CrossRefGoogle Scholar
  14. 14.
    Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 262–272. Association for Computational Linguistics (2012)Google Scholar
  15. 15.
    Lathrop, D., Ruma, L.: Open Government: Collaboration, Transparency, and Participation in Practice. O’Reilly Media, Inc. (2010)Google Scholar
  16. 16.
    David, S.E., Hagiu, A., Richard, S.: How Software Platforms Drive Innovation and Transform Industries. The MIT Press, Cambridge (2006)Google Scholar
  17. 17.
    Baldwin, C.Y., Woodard, C.J.: The Architecture of Platforms: A Unified View. Platforms Markets Innovation, pp. 19–44 (2009).  https://doi.org/10.2139/ssrn.1265155
  18. 18.
    Gawer, A.: Platforms, Markets and Innovation. Edward Elgar Publishing (2011).  https://doi.org/10.4337/9781849803311
  19. 19.
    Choi, B.S.: Value creation platform. Donga Business Review: Platform Leadership, 2 (2012)Google Scholar
  20. 20.
    Galloway, S.: The Four: The Hidden DNA of Amazon, Apple, Facebook, and Google. Penguin (2017)Google Scholar
  21. 21.
    Deloitte Consulting: Gov2020: A Journey into the Future of Government (2015)Google Scholar
  22. 22.
    O’Reilly, T.: Government as a Platform. Open Government: Collaboration, Transparency, and Participation in Practice. O’Reilly Media, Sebastopol (2010)Google Scholar
  23. 23.
    A Digital Government Technology Platform is Essential to Government Transformation Gartner (2018)Google Scholar
  24. 24.
    Chun, S., Stuart, W.S., Eduard, H.: Government 2.0: making connections between citizens, data and government. Inf. Polity 2, 1–9 (2010)Google Scholar
  25. 25.
    Brown, A., Fishenden, J., Thompson, M., Venters, W.: Appraising the impact and role of platform models and Government as a Platform (GaaP) in UK government public service reform: towards a Platform Assessment Framework (PAF). Gov. Inf. Q. 34, 167–182 (2017).  https://doi.org/10.1016/j.giq.2017.03.003CrossRefGoogle Scholar
  26. 26.
    Master Plan for the intelligent information society. Ministry of Science and ICT (2017)Google Scholar
  27. 27.
    Lee, G.: An Exploration of Next Generation’s eGovernment Focused on Platform Perspectives: The Possibilities and Limits in Korea. Korean Public Administration Association (2012)Google Scholar
  28. 28.
    Robinson, D.G., Yu, H., Zeller, W.P., Felten, E.W.: Government data and the invisible hand. Yale J. Law Tech. 11, 159 (2008)Google Scholar
  29. 29.
    Danneels, L., Viaene, S., Van den Bergh, J.: Open data platforms: discussing alternative knowledge epistemologies. Gov. Inf. Q. 34, 365–378 (2017).  https://doi.org/10.1016/j.giq.2017.08.007CrossRefGoogle Scholar
  30. 30.
    Shin, I.H.: e-Gov Platform Policy for Future Governments. The Korea National Informatization Strategy Committee (2012)Google Scholar
  31. 31.
    Janssen, M., Estevez, E.: Lean government and platform-based governance—doing more with less. Gov. Inf. Q. 30, 1–8 (2013).  https://doi.org/10.1016/j.giq.2012.11.003CrossRefGoogle Scholar
  32. 32.
    Wisdom LDA: https://github.com/crabyh/WisdomLDA. Accessed 10 Aug 2019
  33. 33.
    Sievert, C., Shirley, K.: LDAvis: a method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70 (2014).  https://doi.org/10.3115/v1/w14-3110
  34. 34.
    Martin Kenney, J.Z.: Choosing a future in the platform economy: the implications and consequences of digital platforms. In: Proceeding of Conference Kauffman Foundation New Entrepreneurial Growth (2015).  https://doi.org/10.4324/9781315717128-8
  35. 35.
    Lee, M.H.: Innovation policy for local government. CREN (2017)Google Scholar
  36. 36.
    Data Platform using Public Data. Ministry of the interior and safety of Korea (2018)Google Scholar
  37. 37.
    Measuring online Platforms and Cloud Computing in National Accounts. OECD (2018)Google Scholar
  38. 38.
    Panagiotopoulos, P., Bowen, F., Brooker, P.: The value of social media data: Integrating crowd capabilities in evidence-based policy. Gov. Inf. Q. 34, 601–612 (2017).  https://doi.org/10.1016/j.giq.2017.10.009CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Kyungpook National UniversityDaeguKorea

Personalised recommendations