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Trends and Changes in the Field of HCI the Last Decade from the Perspective of HCII Conference

  • André Calero ValdezEmail author
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11566)

Abstract

In order to identify trends and changes in the field of HCI, we used the full-texts of the papers of the HCII conferences from 2007 to 2017 in a text-mining approach. From a set of approx. 7500 documents we looked at word frequencies and topic modelling using latent dirichlet allocation (LDA) in order to detect changes and trends. We identified 50 topics using the LDA model. We found that the topics around social aspects, gamification and datafication play an increasing role. We find evidence for this in both LDA and word frequencies. We qualitatively asses the topic models using our own publications and find a high match of detected topics and our ground truth.

Keywords

Latent dirichlet allocation Text mining tfidf Bag-of-words model Bibliometrics 

Notes

Acknowledgements

The authors would like to thank Johannes Nakayama for his help in improving this article. Further, we would like to thank Annie Waldherr and Tim Schatto-Eckrodt for their help on improving the LDA hyperparameters. This research was supported by the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia.

References

  1. 1.
    Allaire, J., et al.: rmarkdown: Dynamic Documents for R (2018). https://CRAN.R-project.org/package=rmarkdown, r package version 1.10
  2. 2.
    Arun, R., Suresh, V., Veni Madhavan, C.E., Narasimha Murthy, M.N.: On finding the natural number of topics with latent dirichlet allocation: some observations. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS (LNAI), vol. 6118, pp. 391–402. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13657-3_43CrossRefGoogle Scholar
  3. 3.
    Benoit, K., Muhr, D., Watanabe, K.: stopwords: Multilingual Stopword Lists (2017). https://CRAN.R-project.org/package=stopwords, r package version 0.9.0
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Bouchet-Valat, M.: SnowballC: Snowball stemmers based on the C libstemmer UTF-8 library (2014). https://CRAN.R-project.org/package=SnowballC, r package version 0.5.1
  6. 6.
    Calero Valdez, A.: HCII Text-Mining, October 2018, osf.io/cfaezGoogle Scholar
  7. 7.
    Calero Valdez, A., Bruns, S., Greven, C., Schroeder, U., Ziefle, M.: What do my colleagues know? Dealing with cognitive complexity in organizations through visualizations. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2015. LNCS, vol. 9192, pp. 449–459. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20609-7_42CrossRefGoogle Scholar
  8. 8.
    Calero Valdez, A., Gebhardt, S., Kuhlen, T.W., Ziefle, M.: Measuring insight into multi-dimensional data from a combination of a scatterplot matrix and a hyperslice visualization. In: Duffy, V.G. (ed.) DHM 2017, Part II. LNCS, vol. 10287, pp. 225–236. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58466-9_21CrossRefGoogle Scholar
  9. 9.
    Calero Valdez, A., Schaar, A.K., Ziefle, M., Holzinger, A.: Enhancing interdisciplinary cooperation by social platforms. In: Yamamoto, S. (ed.) HIMI 2014, Part I. LNCS, vol. 8521, pp. 298–309. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07731-4_31CrossRefGoogle Scholar
  10. 10.
    Calero Valdez, A., Ziefle, M.: Older users’ rejection of mobile health apps a case for a stand-alone device? In: Zhou, J., Salvendy, G. (eds.) ITAP 2015, Part II. LNCS, vol. 9194, pp. 38–49. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20913-5_4CrossRefGoogle Scholar
  11. 11.
    Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. Neurocomputing 72(7–9), 1775–1781 (2009)CrossRefGoogle Scholar
  12. 12.
    Card, S.K.: The Psychology of Human-computer Interaction. CRC Press, Boca Raton (2017)Google Scholar
  13. 13.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)CrossRefGoogle Scholar
  14. 14.
    Deveaud, R., SanJuan, E., Bellot, P.: Accurate and effective latent concept modeling for ad hoc information retrieval. Doc. Numér. 17(1), 61–84 (2014)CrossRefGoogle Scholar
  15. 15.
    Garnier, S.: viridis: Default Color Maps from ‘matplotlib’ (2018). https://CRAN.R-project.org/package=viridis, r package version 0.5.1
  16. 16.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(Suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  17. 17.
    Grün, B., Hornik, K.: Topicmodels: an R package for fitting topic models. J. Stat. Softw. 40(13), 1–30 (2011).  https://doi.org/10.18637/jss.v040.i13CrossRefGoogle Scholar
  18. 18.
    Ooms, J.: pdftools: Text Extraction, Rendering and Converting of PDF Documents (2018). https://CRAN.R-project.org/package=pdftools, r package version 1.8
  19. 19.
    Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  20. 20.
    Rinker, T.W.: textstem: Tools for stemming and lemmatizing text, Buffalo, New York (2018). http://github.com/trinker/textstem, version 0.1.4
  21. 21.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  22. 22.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval (1986)Google Scholar
  23. 23.
    Silge, J., Robinson, D.: tidytext: Text mining and analysis using tidy data principles in R. JOSS 1(3) (2016).  https://doi.org/10.21105/joss.00037CrossRefGoogle Scholar
  24. 24.
    Sivic, J., Zisserman, A.: Efficient visual search of videos cast as text retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 591–606 (2009)CrossRefGoogle Scholar
  25. 25.
    Wickham, H.: ggplot2: Elegant Graphics for Data Analysis. UR. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-24277-4. http://ggplot2.orgCrossRefzbMATHGoogle Scholar
  26. 26.
    Ziefle, M., Calero Valdez, A.: Domestic robots for homecare: a technology acceptance perspective. In: Zhou, J., Salvendy, G. (eds.) ITAP 2017, Part I. LNCS, vol. 10297, pp. 57–74. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58530-7_5CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

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