Skip to main content
Springer Nature Link
Log in

Is YouTube Still a Radicalizer? An Exploratory Study on Autoplay and Recommendation

  • Conference paper
  • First Online:
Disinformation in Open Online Media (MISDOOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12887))

Abstract

This work investigates the functioning of YouTube’s recommendation system with focus on the autoplay function. The autoplay function was often referred to as “radicalizer” in the past, as it was considered to lead towards more extremist content. By an automated data collection through browser remote control, we simulate different usage scenarios (allowing and disallowing autoplay) with personalized accounts as well as with anonymous users. This leads to multiple recommendation paths, which are analyzed. The presented analyses suggest that while YouTube continues to rely on familiar mechanisms for capturing users’ attention, ongoing public criticism with respect to the recommendation system has seemingly led to changes in YouTube’s algorithm parameterization and to more cautious recommendations.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The cosine similarity [25] is computed as angle between two vectors, which represent the frequency distribution of video tags in the compared categories. A value of 0 denotes maximum dissimilarity, while a value of 1 denotes equality.

  2. 2.

    https://blog.youtube/news-and-events/more-information-faster-removals-more/.

  3. 3.

    https://transparencyreport.google.com/youtube-policy/removals?hl=en.

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  2. Aggarwal, C.C.: Recommender Systems: The Textbook. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3

    Book  Google Scholar 

  3. Allgaier, J.: Science and environmental communication on YouTube: strategically distorted communications in online videos on climate change and climate engineering. Front. Commun. 4, 36 (2019). https://doi.org/10.3389/fcomm.2019.00036

    Article  Google Scholar 

  4. Araujo, T., Helberger, N., Kruikemeier, S., de Vreese, C.H.: In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI Soc. 35(3), 611–623 (2020). https://doi.org/10.1007/s00146-019-00931-w

    Article  Google Scholar 

  5. Brinkmann, M.: Google tests new video autoplay feature on YouTube (2015). https://www.ghacks.net/2015/01/28/google-tests-new-video-autoplay-feature-on-youtube/. Accessed 29 Mar 2021

  6. Brynjolfsson, E., Oh, J.: The attention economy: measuring the value of free digital services on the internet. In: ICIS 2012 Proceedings (2012)

    Google Scholar 

  7. Burke, R., Felfernig, A., Göker, M.H.: Recommender systems: an overview. AI Mag. 32(3), 13–18 (2011). https://doi.org/10.1609/aimag.v32i3.2361

    Article  Google Scholar 

  8. Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.Y., Moon, S.: I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC 2007, New York, NY, USA, pp. 1–14. Association for Computing Machinery (2007). https://doi.org/10.1145/1298306.1298309

  9. Coombs, C., et al.: What is it about humanity that we can’t give away to intelligent machines? A European perspective. Int J. Inf. Manag. 58 (2021). https://doi.org/10.1016/j.ijinfomgt.2021.102311

  10. Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, New York, NY, USA, pp. 191–198. Association for Computing Machinery (2016). https://doi.org/10.1145/2959100.2959190

  11. Eyal, N., Hoover, R.: Hooked - How to Build Habit-Forming Products. Penguin Publishing Group, New York (2014)

    Google Scholar 

  12. Goldhaber, M.H.: The attention economy and the Net. First Monday (1997). https://doi.org/10.5210/fm.v2i4.519

    Article  Google Scholar 

  13. Hannak, A., et al.: Measuring personalization of web search. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, New York, NY, USA, pp. 527–538. Association for Computing Machinery (2013). https://doi.org/10.1145/2488388.2488435

  14. Hern, A.: YouTube to manually review popular videos before placing ads, January 2018. http://www.theguardian.com/technology/2018/jan/17/youtube-google-manually-review-top-videos-before-placing-ads-scandal-logan-paul

  15. Heuer, H.: Users & machine learning-based curation systems. Ph.D. thesis, University of Bremen, Bremen, July 2020

    Google Scholar 

  16. 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 

  17. Lewis, P.: ‘Fiction is outperforming reality’: how YouTube’s algorithm distorts truth, February 2018. http://www.theguardian.com/technology/2018/feb/02/how-youtubes-algorithm-distorts-truth

  18. Lewis, P., McCormick, E.: How an ex-YouTube insider investigated its secret algorithm, February 2018. http://www.theguardian.com/technology/2018/feb/02/youtube-algorithm-election-clinton-trump-guillaume-chaslot

  19. Maack, M.: ‘YouTube recommendations are toxic’, says dev who worked on the algorithm (2019). https://thenextweb.com/google/2019/06/14/youtube-recommendations-toxic-algorithm-google-ai/. Accessed 26 Mar 2021

  20. Meyerson, E.: YouTube now: why we focus on watch time (2012). https://blog.youtube/news-and-events/youtube-now-why-we-focus-on-watch-time/

  21. Newton, C.: YouTube says it will recommend fewer videos about conspiracy theories, January 2019. https://www.theverge.com/2019/1/25/18197301/youtube-algorithm-conspiracy-theories-misinformation

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

    Google Scholar 

  23. Pasquale, F.: The Black Box Society. Harvard University Press, Cambridge (2015)

    Book  Google Scholar 

  24. Rieder, B., Matamoros-Fernández, A., Coromina, Ò.: From ranking algorithms to ‘ranking cultures’ investigating the modulation of visibility in YouTube search results. Convergence 24(1), 50–68 (2018)

    Article  Google Scholar 

  25. Singhal, A.: Modern information retrieval: a brief overview. IEEE Data Eng. Bull. 24(4), 35–43 (2001)

    Google Scholar 

  26. Solsman, J.E.: YouTube’s AI is the puppet master over most of what you watch (2018). https://www.cnet.com/news/youtube-ces-2018-neal-mohan/. Accessed 8 Mar 2021

  27. Stöcker, C.: How facebook and google accidentally created a perfect ecosystem for targeted disinformation. In: Grimme, C., Preuss, M., Takes, F.W., Waldherr, A. (eds.) MISDOOM 2019. LNCS, vol. 12021, pp. 129–149. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39627-5_11

    Chapter  Google Scholar 

  28. Stöcker, C., Preuss, M.: Riding the wave of misclassification: how we end up with extreme YouTube content. In: Meiselwitz, G. (ed.) HCII 2020. LNCS, vol. 12194, pp. 359–375. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49570-1_25

    Chapter  Google Scholar 

  29. Tankovska, H.: Most popular social networks worldwide as of January 2021, ranked by number of active users (2021). https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/. Accessed 24 Mar 2021

  30. Tankovska, H.: Most popular YouTube videos based on total global views as of February 2021 (2021). https://www.statista.com/statistics/249396/top-youtube-videos-views/. Accessed 30 Mar 2021

  31. Tufekci, Z.: YouTube, the Great Radicalizer. The New York Times, March 2018. https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html

  32. Waterson, J.: YouTube bans videos promoting Nazi ideology, June 2019. http://www.theguardian.com/technology/2019/jun/05/youtube-bans-videos-promoting-nazi-ideology

  33. Williams, J.: Stand Out of Our Light: Freedom and Resistance in the Attention Economy. Cambridge University Press, Cambridge (2018). https://doi.org/10.1017/9781108453004

    Book  Google Scholar 

  34. Zhang, Y., Goh, K.H.: Attracting versus sustaining attention in the information economy. In: Cho, W., Fan, M., Shaw, M.J., Yoo, B., Zhang, H. (eds.) WEB 2017. LNBIP, vol. 328, pp. 1–14. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99936-4_1

    Chapter  Google Scholar 

  35. 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, IMC 2010, New York, NY, USA, pp. 404–410. Association for Computing Machinery (2010). https://doi.org/10.1145/1879141.1879193

  36. Zimmermann, D., et al.: Influencers on YouTube: a quantitative study on young people’s use and perception of videos about political and societal topics. Curr. Psychol. (3), 1–17 (2020). https://doi.org/10.1007/s12144-020-01164-7

  37. Zink, M., Suh, K., Gu, Y., Kurose, J.: Characteristics of YouTube network traffic at a campus network - measurements, models, and implications. Comput. Netw. Int. J. Comput. Telecommun. Netw. 53(4), 501–514 (2009). https://doi.org/10.1016/j.comnet.2008.09.022

    Article  Google Scholar 

Download references

Acknowlegments

Both authors appreciate the support of the European Research Center for Information Systems (ERCIS).

Author information

Authors and Affiliations

  1. Department of Information Systems, University of Münster, Leonardo-Campus 3, 48149, Münster, Germany

    Simon Markmann & Christian Grimme

Authors
  1. Simon Markmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Grimme
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Grimme .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Jonathan Bright

  2. Utrecht University, Utrecht, The Netherlands

    Anastasia Giachanou

  3. University of Leeds, Leeds, UK

    Viktoria Spaiser

  4. Boise State University, Boise, ID, USA

    Francesca Spezzano

  5. University of Oxford, Oxford, UK

    Anna George

  6. University of Oxford, Oxford, UK

    Alexandra Pavliuc

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Markmann, S., Grimme, C. (2021). Is YouTube Still a Radicalizer? An Exploratory Study on Autoplay and Recommendation. In: Bright, J., Giachanou, A., Spaiser, V., Spezzano, F., George, A., Pavliuc, A. (eds) Disinformation in Open Online Media. MISDOOM 2021. Lecture Notes in Computer Science(), vol 12887. Springer, Cham. https://doi.org/10.1007/978-3-030-87031-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87031-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87030-0

  • Online ISBN: 978-3-030-87031-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics