Advertisement

On the Gender of Books: Author Gender Mixing in Book Communities

  • Doina Bucur
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

Using a book co-buying network from amazon.com of over 1 million books, we find empirically that readers who have purchased male first authors before are substantially less likely than expected to buy books by female first authors, when aggregated across the entire book market. Conversely, past buyers of female authors are slightly more likely than expected to buy other female authors. This same-gender assortativity is found to be local: certain writing genres are “coloured” preferentially by one gender. This can be attributed both to writer availability (i.e., a gender’s preferential attachment to writing for one genre), and to the buyers’ preferential attachment to the output of writers of one gender. We obtain these insights by classifying the gender of the first author for most of the books, then running statistical tests which compare the gender makeup of books co-bought with either male or female books. Structural book communities, as generated from readers’ co-buying choices, are computed, visualised in terms of gender makeup, and their writing genres are summarised to match the genre with a gender makeup.

References

  1. 1.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. (10), P10008Google Scholar
  2. 2.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174.  https://doi.org/10.1016/j.physrep.2009.11.002
  3. 3.
    Ghiasi, G., Larivire, V., Sugimoto, C.R.: On the compliance of women engineers with a gendered scientific system. PLOS ONE 10(12), 1–19 (2016).  https://doi.org/10.1371/journal.pone.0145931
  4. 4.
    Krebs, V.: Divided we stand? (2003). http://orgnet.com/leftright.html
  5. 5.
    Krebs, V.: The social life of books, visualizing communities of interest via purchase patterns on the WWW (2004). http://orgnet.com/booknet.html
  6. 6.
    Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 056117 (2009).  https://doi.org/10.1103/PhysRevE.80.056117
  7. 7.
    Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 631–636 (2006)Google Scholar
  8. 8.
    Massen, J.J.M., Bauer, L., Spurny, B., Bugnyar, T., Kret, M.E.: Sharing of science is most likely among male scientists. Sci. Rep. 7(12927) (2017).  https://doi.org/10.1038/s41598-017-13491-0
  9. 9.
    McAuley, J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015, pp. 43–52 (2015)Google Scholar
  10. 10.
    Shi, F., Shi, Y., Dokshin, F.A., Evans, J.A., Macy, M.W.: Millions of online book co-purchases reveal partisan differences in the consumption of science. Nat. Hum. Behav., 0079Google Scholar
  11. 11.
    Thelwall, M.: Book genre and author gender: Romance paranormal-romance to autobiography memoir. J. Assoc. Inf. Sci. Technol. 68(5), 1212–1223 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of TwenteEnschedeThe Netherlands

Personalised recommendations