Abstract
In recent years, a growing number of journalistic and scholarly publications have paid particular attention to the broad societal impact of YouTube’s video recommendation system. Significantly, the YouTube algorithm’s alleged contributions to the formation of echo-chambers, filter-bubbles, polarization, radicalization, disinformation, and malicious use of information are among the top concerns. On top of the given issues, potential biases of the recommendation system in favor of a small number of videos, content producers, or channels would further exacerbate the magnitude of the problem, especially if a systematic understanding of the inherent nature and characteristics of the algorithm is lacking. In this study, we investigate the structure of recommendation networks and probabilistic distributions of the node-centric influence of recommended videos. Adopting a stochastic approach, we observe PageRank distributions over a diverse set of recommendation graphs we collected and built based on eight different real-world scenarios. In total, we analyzed 803,210 recommendations made by YouTube’s recommendation algorithm, based on specific search queries and seed datasets from previous studies. As a result, we demonstrate the existence of a structural, systemic, and inherent tendency to impose bias by YouTube’s video recommendation system in favor of a tiny fraction of videos in each scenario. We believe that this work sets the stage for further research in creating predictive modeling techniques that reduce bias in video recommendation systems and make algorithms fairer. The implementation of such attempts aims to reduce their potential harmful social and ethical impacts and increase public trust in these social systems.
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Notes
- 1.
YouTube Tracker (2020), https://vtracker.host.ualr.edu, COSMOS, UALR.
References
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), pp 42–46 (2020)
Alstott, J., Bullmore, E., Plenz, D.: Power-law: a Python package for analysis of heavy-tailed distributions. PloS One 9(1), e85777 (2014)
Bellogín, A., Castells, P., Cantador, I.: Statistical biases in information retrieval metrics for recommender systems. Inf. Retriev. J. 20(6), 606–634 (2017). https://doi.org/10.1007/s10791-017-9312-z
Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2019), pp. 2212–2220 (2019)
Boratto, L., Marras, M., Faralli, S., and Stilo, G.: International workshop on algorithmic bias in search and recommendation (Bias 2020). In: Jose, J. et al. (eds.) Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science. Springer (2020)
Buntain, C., Bonneau, R., Nagler, J., Tucker, J.A.: YouTube Recommendations and Effects on Sharing Across Online Social Platforms (2020). arXiv preprint arXiv:2003.00970
Clauset, A., Shalizi, C.R., Newman, M.E.: Power law distributions in empirical data. SIAM Rev. 51(4), 661–703 (2009)
Davidson, J., et al.: The YouTube video recommendation system. In: Proceedings of the fourth ACM Conference on Recommender systems, pp. 293–296, September 2010
Faddoul, M., Chaslot, G., Farid, H.: A Longitudinal Analysis of YouTube’s Promotion of Conspiracy Videos (2020). arXiv preprint arXiv:2003.03318
Galeano, K., Galeano, L., Mead, E., Spann, B., Kready, J., Agarwal, N.: The role of YouTube during the 2019 Canadian federal election: a multi-method analysis of online discourse and information actors, Fall 2020, no. 2, pp. 1–22. Queen’s University, Canada (2020). Journal of Future Conflict
Google Developers: YouTube Data API, Google (2020). https://developers.google.com/youtube/v3
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)
Le Merrer, E., Trédan, G.: The topological face of recommendation. In: International Conference on Complex Networks and their Applications, pp. 897–908. Springer, Cham (2017).https://doi.org/10.1007/978-3-319-72150-7_72
Ledwich, M., Zaitsev, A.: Algorithmic extremism: Examining YouTube’s rabbit hole of radicalization. First Monday (2020)
Marcoux, T., Agarwal, N., Erol, R., Obadimu, A., Hussain, M.: Analyzing Cyber Influence Campaigns on YouTube using YouTubeTracker. Lecture Notes in Social Networks, Springer. Forthcoming (2018)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford InfoLab (1999)
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)
Roose, K.: The making of a YouTube Radical. The New York Times (2019)
Roth, C., Mazières, A., Menezes, T.: Tubes and bubbles topological confinement of YouTube recommendations. PloS One 15(4), e0231703 (2020)
Tufekci, Z.: YouTube, the Great Radicalizer. The New York Times, vol. 10, p. 2018 (2018)
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, First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings. Communications in Computer and Information Science, vol. 1245, pp. 1–11 (2020). https://doi.org/10.1007/978-3-030-52485-2_1
Wakabayashi, D.: YouTube Moves to Make Conspiracy Videos Harder to Find. The New York Times, 25 Jan 2019. https://www.nytimes.com/2019/01/25/technology/youtube-conspiracy-theory-videos.html
Zhou, R., Khemmarat, S., Gao, L.: The impact of YouTube recommendation system on video views. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 404–410, November 2010
Zhou, R., Khemmarat, S., Gao, L., Wan, J., Zhang, J.: How YouTube videos are discovered and its impact on video views. Multimed. Tools Appl. 75(10), 6035–6058 (2016). https://doi.org/10.1007/s11042-015-3206-0
Acknowledgements
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, and 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. The researchers also thank MaryEtta Morris for helping with proofreading and improving the paper.
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Kirdemir, B., Kready, J., Mead, E., Hussain, M.N., Agarwal, N. (2021). Examining Video Recommendation Bias on YouTube. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2021. Communications in Computer and Information Science, vol 1418. Springer, Cham. https://doi.org/10.1007/978-3-030-78818-6_10
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