Predictive power of conference-related factors on citation rates of conference papers
- 73 Downloads
This paper aims to determine the factors significantly predicting the future citation rates of conference papers. Whereas a large body of bibliometric studies has investigated the multiple factors predicting future citation rates, the attention has been paid mainly on journal articles. This study analyzes 43,463 papers from 81 conference series in the ‘Information Science’ and ‘Computer Science’ fields and examines the contributions of conference-related factors to the citation rates of the conference papers. More specifically, this paper assesses the following conference related factors as being potentially predictive factors of citation rates: longevity and names of the conference series, the number of presented papers at individual conferences, acceptance rates, the seasons of conferences, the content similarity of the presented papers at a conference, the degree of the authors’ international collaborations and the records of the best paper awards at conferences. The regression results illustrate that all of the factors were significantly predictive to the future citations of the conference papers. The factors that contributed the most to explain the citations of the conference papers include: the degree of the authors’ international collaborations at individual conferences, the records of best paper awards and the acceptance rates of individual conferences.
KeywordsCitation analysis Conferences Information science Bibliometric analysis
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (NRF-2018R1C1B6002434).
- Barbosa, S. D. J., Silveira, M. S., & Gasparini, I. (2017). What publications metadata tell us about the evolution of a scientific community: The case of the Brazilian human–computer interaction conference series. Scientometrics, 110(1), 275–300. https://doi.org/10.1007/s11192-016-2162-4.Google Scholar
- Bartneck, C., & Hu, J. (2009). Scientometric analysis of the CHI proceedings. Paper presented at the proceedings of the SIGCHI conference on human factors in computing systems, Boston, MA, USA.Google Scholar
- Bornmann, L., Marx, W., Gasparyan, A. Y., & Kitas, G. D. (2012). Diversity, value and limitations of the journal impact factor and alternative metrics. Rheumatology International, 32(7), 1861–1867.Google Scholar
- Keith, T. Z. (2014). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. London: Routledge.Google Scholar
- Scopus. (2013). Content coverage guide. Retrieved from https://files.sciverse.com/documents/pdf/ContentCoverageGuide-jan-2013.pdf.
- Shirakawa, N., Furukawa, T., Nomura, M., & Okuwada, K. (2012). Global competition and technological transition in electrical, electronic, information and communication engineering: Quantitative analysis of periodicals and conference proceedings of the IEEE. Scientometrics, 91(3), 895–910. https://doi.org/10.1007/s11192-011-0566-8.Google Scholar
- Song, M., Heo, G. E., & Kim, S. Y. (2014). Analyzing topic evolution in bioinformatics: Investigation of dynamics of the field with conference data in DBLP. Scientometrics, 101(1), 397–428.Google Scholar
- Souto, M. A. M., Warpechowski, M., & de Oliveira, J. P. M. (2007). An ontological approach for the quality assessment of computer science conferences. Berlin: Springer.Google Scholar
- Wainer, J., & Valle, E. (2013). What happens to computer science research after it is published? Tracking CS research lines. Journal of the American Society for Information Science and Technology, 64(6), 1104–1111.Google Scholar