Skip to main content
SpringerLink
Log in

Exploring Bias and Information Bubbles in YouTube’s Video Recommendation Networks

  • Conference paper
  • First Online:
Complex Networks & Their Applications X (COMPLEX NETWORKS 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1073))

Included in the following conference series:

Abstract

This study audits the structural and emergent properties of YouTube’s video recommendations, with an emphasis on the black-box evaluation of recommender bias, confinement, and formation of information bubbles in the form of content communities. Adopting complex networks and graphical probabilistic approaches, the results of our analysis of 6,068,057 video recommendations made by the YouTube algorithm reveals strong indicators of recommendation bias leading to the formation of closely-knit and confined content communities. Also, our qualitative and quantitative exploration of the prominent content and discourse in each community further uncovered the formation of narrative-specific clusters made by the recommender system we examined.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys 2017) (2020)

    Google Scholar 

  2. Imana, B., Korolova, A., Heidemann, J.: Auditing for discrimination in algorithms delivering job ads. In: Proceedings of the Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. ACM (2021). https://doi.org/10.1145/3442381.3450077

  3. Juneja, P., Mitra, T.: Auditing e-commerce platforms for algorithmically curated vaccine misinformation. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021). Association for Computing Machinery (2021). https://doi.org/10.1145/3411764.3445250

  4. Papakyriakopoulos, O., Serrano, J.C.M., Hegelich, S.: Political communication on social media: a tale of hyperactive users and bias in recommender systems. Online Soc. Netw. Media 15, 100058 (2020)

    Article  Google Scholar 

  5. Ribeiro, M.H., Ottoni, R., West, R., Almeida, V.A., Meira Jr, W.: Auditing radicalization pathways on YouTube. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 131–141 (2020)

    Google Scholar 

  6. Tufekci, Z.: YouTube, the great radicalizer. The New York Times, 10 May 2018

    Google Scholar 

  7. Ledwich, M., Zaitsev, A.: Algorithmic extremism: examining YouTube’s rabbit hole of radicalization. First Monday (2020)

    Google Scholar 

  8. Hosseinmardi, H., Ghasemian, A., Clauset, A., Mobius, M., Rothschild, D. M., Watts, D.J.: Examining the consumption of radical content on YouTube. Proc. Natl. Acad. Sci. 118(32) (2021)

    Google Scholar 

  9. Le Merrer, E., Trédan, G.: The topological face of recommendation. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds.) Complex Networks & Their Applications VI, vol. 689, pp. 897–908. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72150-7_72

    Chapter  Google Scholar 

  10. Kirdemir, B., Kready, J., Mead, E., Hussain, M.N., Agarwal, N.: Examining video recommendation bias on YouTube. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds.) Advances in Bias and Fairness in Information Retrieval. CCIS, vol. 1418, pp. 106–116. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78818-6_10

    Chapter  Google Scholar 

  11. Kirdemir, B., Kready, J., Mead, E., Hussain, M.N., Agarwal, N., Adjeroh, D.: Assessing bias in YouTube’s video recommendation algorithm in a cross-lingual and cross-topical context. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) Social, Cultural, and Behavioral Modeling. LNCS, vol. 12720, pp. 71–80. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_7

    Chapter  Google Scholar 

  12. Cano, P., Celma, O., Koppenberger, M., Buldu, J.M.: Topology of music recommendation networks. Chaos: Interdisc. J. Nonlinear Sci. 16(1), 013107 (2006)

    Article  MATH  Google Scholar 

  13. Boratto, L., Marras, M., Faralli, S., Stilo, G.: International workshop on algorithmic bias in search and recommendation (Bias 2020). In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 637–640. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_84

    Chapter  Google Scholar 

  14. Verma, S., Gao, R., Shah, C.: Facets of fairness in search and recommendation. In: Borratto, L., Faralli, S., Marras, M., Stilo, G. (eds.) Bias and Social Aspects in Search and Recommendation, vol. 1245, pp. 1–11. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52485-2_1

    Chapter  Google Scholar 

  15. Rosen, S.: The economics of superstars. Am. Econ. Rev. 71(5), 845–858 (1981)

    Google Scholar 

  16. Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., Diaz, F.: Towards a fair marketplace: counterfactual evaluation of the trade-off between relevance, fairness and satisfaction in recommendation systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 2243–2251 (2018)

    Google Scholar 

  17. Boratto, L., Fenu, G., Marras, M.: The effect of algorithmic bias on recommender systems for massive open online courses. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) Advances in Information Retrieval, vol. 11437, pp. 457–472. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15712-8_30

    Chapter  Google Scholar 

  18. Olteanu, A., Castillo, C., Diaz, F., Kıcıman, E.: Social data: biases, methodological pitfalls, and ethical boundaries. Front. Big Data 2, 13 (2019)

    Article  Google Scholar 

  19. Bartley, N., Abeliuk, A., Ferrara, E., Lerman, K.: Auditing algorithmic bias on Twitter. In: 13th ACM Web Science Conference 2021, pp. 65–73 (2021)

    Google Scholar 

  20. Faddoul, M., Chaslot, G., Farid, H.: A longitudinal analysis of YouTube’s promotion of conspiracy videos. arXiv preprint https://arxiv.org/abs/2003.03318 (2020)

  21. Hussein, E., Juneja, P., Mitra, T.: Measuring misinformation in video search platforms: an audit study on YouTube. Proc. ACM Hum.-Comput. Interact. 4(CSCW1), 1–27 (2020)

    Article  Google Scholar 

  22. Roth, C., Mazières, A., Menezes, T.: Tubes and bubbles topological confinement of YouTube recommendations. PloS one 15(4), e0231703 (2020)

    Article  Google Scholar 

  23. Mirza, B.J., Keller, B.J., Ramakrishnan, N.: Studying recommendation algorithms by graph analysis. J. Intell. Inf. Syst. 20(2), 131–160 (2003). https://doi.org/10.1023/A:1021819901281

    Article  Google Scholar 

  24. Leskovec, J., Singh, A., Kleinberg, J.: Patterns of influence in a recommendation network. In: Ng, W.K., Kitsuregawa, M., Li, J., Chang, K. (eds.) Advances in Knowledge Discovery and Data Mining, vol. 3918, pp. 380–389. Springer, Heidelberg (2006). https://doi.org/10.1007/11731139_44

    Chapter  Google Scholar 

  25. Marcoux, T., Agarwal, N., Erol, R., Obadimu, A., Hussain, M.N.: Analyzing cyber influence campaigns on YouTube using YouTubeTracker. In: Çakırtaş, M., Ozdemir, M.K. (eds.) Big Data and Social Media Analytics, pp. 101–111. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67044-3_5

  26. Kready, J., Shimray, S.A., Hussain, M.N., Agarwal, N.: YouTube data collection using parallel processing. In: 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1119–1122. IEEE (2020)

    Google Scholar 

  27. Google Developers (2020). YouTube Data API, Google, https://developers.google.com/youtube/v3

  28. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Stanford InfoLab (1999)

    Google Scholar 

  29. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  MATH  Google Scholar 

  30. Lambiotte, R., Delvenne, J.C., Barahona, M.: Laplacian dynamics and multiscale modular structure in networks. arXiv preprint https://arxiv.org/abs/0812.1770 (2008)

  31. Malvestio, I., Cardillo, A., Masuda, N.: Interplay between k-core and community structure in complex networks. Nat. Sci. Rep. 10(1), 1–12 (2020)

    Google Scholar 

  32. Erdős, P., Hajnal, A.: On chromatic number of graphs and set-systems. Acta Math. Acad. Sci. Hung. 17(1–2), 61–99 (1966). https://doi.org/10.1007/BF02020444

    Article  MathSciNet  MATH  Google Scholar 

  33. Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)

    Article  MathSciNet  Google Scholar 

  34. Clauset, A., Shalizi, C.R., Newman, M.E.: Power law distributions in empirical data. SIAM Rev. 51(4), 661–703 (2009)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This research is funded in part by the U.S. National Science Foundation (OIA- 1946391, OIA-1920920, IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2675, N00014-17-1-2605, N68335-19-C-0359, N00014-19-1-2336, N68335-20-C-0540, N00014-21-1-2121), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-17-S-0002, W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q- 17-C-0059), Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP) (award number: 2020-106-094). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.

Author information

Authors and Affiliations

  1. Cosmos Research Center, U.A. – Little Rock, Little Rock, AR, USA

    Baris Kirdemir & Nitin Agarwal

Authors
  1. Baris Kirdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nitin Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas Universidad Politécnica de Madrid, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa Maria Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, University of Burgundy, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Compl, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Complex Networks and Systems Research School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kirdemir, B., Agarwal, N. (2022). Exploring Bias and Information Bubbles in YouTube’s Video Recommendation Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93413-2_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93412-5

  • Online ISBN: 978-3-030-93413-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation