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International Conference on Human-Computer Interaction

HCII 2020: Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis pp 215–226Cite as

Filter Bubbles and Content Diversity? An Agent-Based Modeling Approach

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12194))

Abstract

Personalisation algorithms play an important role in catering the information that is relevant to us. The best results are achieved by the algorithms when they monitor the user activity. Most of the algorithms adapt to the users’ personal preferences by filtering out the information that is irrelevant to the user. However, one of the criticisms of this process is that it is leading to informational bubbles called the filter bubbles which is a personal space of content familiar to the user, which would reinforce their confirmational biases or create informational blind spots. This phenomena however is highly debated. In this light, we propose an agent based model study, which tries to verify the implications claimed by the filter bubble theorists and also create an hypothetical environment that does not have a filter bubble and test difference in the information dispersion and opinion formation in both the environments.

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References

  1. Abbassi, Z., Mirrokni, V.S., Thakur, M.: Diversity maximization under matroid constraints. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 32–40 (2013)

    Google Scholar 

  2. Barnier, J.: rmdformats: HTML Output Formats and Templates for ‘rmarkdown’ Documents. R package version 0.3.6 (2019). https://CRAN.R-project.org/package=rmdformats

  3. Bezanson, J., et al.: Julia: a fresh approach to numerical computing. SIAM Rev. 59(1), 65–98 (2017). https://doi.org/10.1137/141000671

    Article  MathSciNet  MATH  Google Scholar 

  4. Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. 99(suppl 3), 7280–7287 (2002)

    Article  Google Scholar 

  5. Valdez, A.C.: rmdtemplates: rmdtemplates - an opinionated collection of R markdown templates. R package version 0.3.0.0 (2019). https://github.com/statisticsforsocialscience/%20rmd_templates

  6. Valdez, A.C., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 123–126 (2016)

    Google Scholar 

  7. Ebenhoh, E.: Agent-based modeling with boundedly rational agents. In: Handbook of Research on Nature-Inspired Computing for Economics and Management, pp. 225–245. IGI Global (2007)

    Google Scholar 

  8. Flaxman, S., Goel, S., Rao, J.M.: Filter bubbles, echo chambers, and online news consumption. Pub. Opin. Q. 80(S1), 298–320 (2016)

    Article  Google Scholar 

  9. He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)

    Article  Google Scholar 

  10. Moeller, J., Helberger, N., et al.: Beyond the filter bubble: concepts, myths, evidence and issues for future debates (2018)

    Google Scholar 

  11. Nagulendra, S., Vassileva, J.: Understanding and controlling the filter bubble through interactive visualization: a user study. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 107–115 (2014)

    Google Scholar 

  12. Nguyen, T.T., et al.: Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 677–686 (2014)

    Google Scholar 

  13. Pariser, E.: The Filter Bubble: What the Internet is Hiding From You. Penguin, London (2011)

    Google Scholar 

  14. Fairbanks, J., Bromberger, S., other contributors: JuliaGraphs/LightGraphs.jl: an optimized graphs package for the Julia programming language (2017). https://doi.org/10.5281/zenodo.889971

  15. Simon, H.A.: Models of Bounded Rationality: Empirically Grounded Economic Reason, vol. 3. MIT Press, Cambridge (1997)

    Book  Google Scholar 

  16. Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44593-5_25

    Chapter  Google Scholar 

  17. Tintarev, N.: Presenting diversity aware recommendations: making challenging news acceptable (2017)

    Google Scholar 

  18. Tintarev, N., Dennis, M., Masthoff, J.: Adapting recommendation diversity to openness to experience: a study of human behaviour. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 190–202. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38844-6_16

    Chapter  Google Scholar 

  19. Tintarev, N., et al.: Same, same, but different: algorithmic diversification of viewpoints in news. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 7–13 (2018)

    Google Scholar 

  20. Calero Valdez, A., Ziefle, M.: Human factors in the age of algorithms. understanding the human-in-the-loop using agent-based modeling. In: Meiselwitz, G. (ed.) SCSM 2018. LNCS, vol. 10914, pp. 357–371. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91485-5_27

    Chapter  Google Scholar 

  21. Wickham, H.: tidyverse: easily install and load the ‘Tidyverse’. R package version 1.3.0 (20190. https://CRAN.R-project.org/package=tidyverse

  22. Wickham, H., Seidel, D.: Scales: scale functions for visualization. R package version 1.1.0 (2019). https://CRAN.R-project.org/package=scales

  23. Xie, Y.: knitr: a general-purpose package for dynamic report generation in R. R package version 1.26 (2019). https://CRAN.Rproject.org/package=knitr

  24. Yook, S.-H., Jeong, H., Barabási, A.-L.: Modeling the Internet’s large-scale topology. Proc. Natl. Acad. Sci. 99(21), 13382–13386 (2002)

    Article  Google Scholar 

  25. Zhu, H.: kableExtra: construct complex table with ‘kable’ and pipe syntax. R package version 1.1.0 (2019). https://CRAN.R-project.org/package=kableExtra

  26. Ziegler, C.-N., et al.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32 (2005)

    Google Scholar 

  27. Borgesius, F.Z., et al.: Should we worry about filter bubbles? Internet Policy Rev. J. Internet Regul. 5(1) (2016)

    Google Scholar 

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Acknowledgements

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. We would further like to thank the authors of the packages we have used. We used the following packages to create this document: knitr [23], tidyverse [21], rmdformats [2], kableExtra [25], scales [22], psych [R-psych], rmdtemplates [5].

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Authors and Affiliations

  1. RWTH Aachen University, Campus Boulevard 57, 52076, Aachen, Germany

    Poornima Belavadi, Laura Burbach, Patrick Halbach, Johannes Nakayama, Nils Plettenberg, Martina Ziefle & André Calero Valdez

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  1. Poornima Belavadi
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  2. Laura Burbach
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  3. Patrick Halbach
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  4. Johannes Nakayama
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  5. Nils Plettenberg
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  6. Martina Ziefle
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  7. André Calero Valdez
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Correspondence to Poornima Belavadi .

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Editors and Affiliations

  1. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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Belavadi, P. et al. (2020). Filter Bubbles and Content Diversity? An Agent-Based Modeling Approach. In: Meiselwitz, G. (eds) Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science(), vol 12194. Springer, Cham. https://doi.org/10.1007/978-3-030-49570-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-49570-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49569-5

  • Online ISBN: 978-3-030-49570-1

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