318
Views
0
CrossRef citations to date
0
Altmetric
Articles

The filter bubble generated by artificial intelligence algorithms and the network dynamics of collective polarization on YouTube: the case of South Korea

&
Pages 195-212 | Received 01 Feb 2023, Accepted 29 Jan 2024, Published online: 12 Feb 2024

References

  • Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160
  • Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. http://doi.org/10.1177/0956797615594620
  • Barnett, G. A., Chung, J. C., & Park, H. W. (2011). Uncovering transnational hyperlink patterns and web-mediated contents: A new approach based on cracking.com domain. Social Science Computer Review, 29(3), 369–384. https://doi.org/10.1177/0894439310382519
  • Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. In Proceedings of the International AAAI Conference on Web and Social Media 3 (pp. 361–362). Association for the Advancement of Artificial Intelligence.
  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis [Computer software]. Analytic Technologies.
  • Boutyline, A., & Willer, R. (2017). The social structure of political echo chambers: Variation in ideological homophily in online networks. Political Psychology, 38(3), 551–569. https://doi.org/10.1111/pops.12337
  • Cho, K. W., & Park, D. (2023). Emergency management policy issues during and after COVID-19: Focusing on South Korea. Journal of Contemporary Eastern Asia, 22(1), 49–81.
  • Danowski, J. A., & Park, H. W. (2020). East Asian communication technology use and cultural values. Journal of Contemporary Eastern Asia, 19(1), 43–58. https://doi.org/10.17477/jcea.2020.19.1.043.
  • De-Aguilera-Moyano, M., Castro-Higueras, A., & Pérez-Rufí, J. P. (2019). Between broadcast yourself and broadcast whatever: YouTube’s homepage as a synthesis of its business strategy. El Profesional de la Información, 28(2), 1–13. https://doi.org/10.3145/epi.2019.mar.06
  • Ekström, A. G., Niehorster, D. C., & Olsson, E. J. (2022). Self-imposed filter bubbles: Selective attention and exposure in online search. Computers in Human Behavior Reports, 7, 100226. https://doi.org/10.1016/j.chbr.2022.100226
  • Enjolras, B. (2023). Does relational polarization entail ideological polarization? The case of the 2017 Norwegian election campaign on Twitter. International Journal of Communication, 17, 2394–2421. https://ijoc.org/index.php/ijoc/article/view/20575
  • Garrett, R. K. (2009). Echo chambers online? Politically motivated selective exposure among Internet news users. Journal of Computer-Mediated Communication, 14(2), 265–285. https://doi.org/10.1111/j.1083-6101.2009.01440.x
  • Garrett, R. K., Long, J. A., & Jeong, M. S. (2019). From partisan media to misperception: Affective polarization as mediator. Journal of Communication, 69(5), 490–512. https://doi.org/10.1093/joc/jqz028
  • González-Bailón, S., Lazer, D., Barberá, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A. M., Iyengar, S., Kim, Y. M., Malhotra, N., Moehler, D., Nyhan, B., Pan, J., Velasco Rivera, C., Settle, J., Thorson, E., … Tucker, J. A. (2023). Asymmetric ideological segregation in exposure to political news on Facebook. Science, 381(6656), 392–398. https://doi.org/10.1126/science.ade7138
  • Goodrow, C. (2021, September 15). On YouTube’s recommendation system. YouTube Official Blog. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/
  • Guess, A. M. (2021). (Almost) everything in moderation: New evidence on Americans’ online media diets. American Journal of Political Science, 65(4), 1007–1022. https://doi.org/10.1111/ajps.12589
  • Haim, M., Graefe, A., & Brosius, H. (2018). Burst of the filter bubble? Digital Journalism, 6(3), 330–343. https://doi.org/10.1080/21670811.2017.1338145
  • Hansen, D., Shneiderman, B., & Smith, M. A. (2010). Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann.
  • Khan, G. F., Yoon, H. Y., & Park, H. W. (2014). Social media communication strategies of government agencies: Twitter use in Korea and the USA. Asian Journal of Communication, 24(1), 60–78. https://doi.org/10.1080/01292986.2013.851723
  • Kim, Y. (2015). Does disagreement mitigate polarization? How selective exposure and disagreement affect political polarization. Journalism & Mass Communication Quarterly, 92(4), 915–937. https://doi.org/10.1177/1077699015596328
  • Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding echo chambers and filter bubbles: The impact of social media on diversification and partisan shifts in news consumption. MIS Quarterly, 44(4), 1619–1649. https://doi.org/10.25300/MISQ/2020/16371
  • Ledwich, M., & Zaitsev, A. (2020). Algorithmic extremism: Examining YouTube’s rabbit hole of radicalization. First Monday, 25(3). https://doi.org/10.5210/fm.v25i3.10419
  • Levy, R. (2021). Social media, news consumption, and polarization: Evidence from a field experiment. American Economic Review, 111(3), 831–870. https://doi.org/10.1257/aer.20191777
  • Lewandowsky, S., Ecker, U. K., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition, 6(4), 353–369. https://doi.org/10.1016/j.jarmac.2017.07.008
  • Lewis, R. (2018). Alternative influence: Broadcasting the reactionary right on YouTube. Data & Society. https://datasociety.net/wp-content/uploads/2018/09/DS_Alternative_Influence.pdf
  • Li, H. (2019). Special section introduction: Artificial intelligence and advertising. Journal of Advertising, 48(4), 333–337. https://doi.org/10.1080/00913367.2019.1654947
  • Lim, Y. S., Barnett, G. A., & Kim, J. H. (2008). The structure of international aid flows and global news media. Journal of International Communication, 14(2), 117–142. https://doi.org/10.1080/13216597.2008.9674736
  • Mehrabian, A. (1981). Silent messages: Implicit communication of emotions and attitudes. Wadsworth.
  • Min, Y., Jiang, T., Jin, C., Li, Q., & Jin, X. (2019). Endogenetic structure of filter bubble in social networks. Royal Society Open Science, 6(11), 190868. https://doi.org/10.1098/rsos.190868
  • Möller, J. (2021). Filter bubbles and digital echo chambers 1. In H. Tumber & S. Waisbord (Eds.), The Routledge companion to media disinformation and populism (pp. 92–100). Routledge.
  • Newman, N., Fletcher, R., Kalogeropoulos, A., & Nielsen, R. K. (2019). Digital news report 2019. Reuters Institute.
  • O’Callaghan, D., Greene, D., Conway, M., Carthy, J., & Cunningham, P. (2015). Down the (white) rabbit hole: The extreme right and online recommender systems. Social Science Computer Review, 33(4), 459–478. https://doi.org/10.1177/0894439314555329
  • Oh, S. W., & Song, H. Y. (2019). YouTube algorithm and journalism. Korea Press Foundation.
  • Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin Books.
  • Park, H. W. (2021). Use of North Korea-related YouTube videos in South Korea: A case study of VideoMug. Društvena istraživanja-Časopis za opća društvena pitanja, 30(4), 721–740.
  • Park, H. W., & Chung, S. W. (2023). Fake stock information on YouTube during presidential election candidate races. International Journal of Contents, 19(1), 11–22. https://doi.org/10.5392/IJoC.2023.19.1.011
  • Park, H. W., & Thelwall, M. (2008). Developing network indicators for ideological landscapes from the political blogosphere in South Korea. Journal of Computer-Mediated Communication, 13(4), 856–879. https://doi.org/10.1111/j.1083-6101.2008.00422.x
  • Park, S., Bier, L., & Park, H. W. (2021). The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis. Profesional de la Información, 29(5), 1–14.
  • Park, S., Lim, Y. S., & Park, H. W. (2015). Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement. Technological Forecasting and Social Change, 95, 208–217. https://doi.org/10.1016/j.techfore.2015.02.003
  • Park, S., Park, J., Lim, Y. S., & Park, H. W. (2016). Expanding the presidential debate by tweeting: The 2012 presidential election debate in South Korea. Telematics and Informatics, 33(2), 557–569. https://doi.org/10.1016/j.tele.2015.08.004
  • Pinsof, D., & Haselton, M. (2016). The political divide over same-sex marriage: Mating strategies in conflict? Psychological Science, 27(4), 435–442. https://doi.org/10.1177/0956797615621719
  • Rader, E., & Gray, R. (2015). Understanding user believes about algorithmic curation in the Facebook news feed. In B. Begole & J. Kim (Eds.), Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 173–182). ACM.
  • Rieder, B. (2015). YouTube data tools (Version 1.23) [Computer software]. Digital Methods. https://tools.digitalmethods.net/netvizz/youtube/
  • Rieder, B., Fernandez, A. M., & Coromina, O. (2018). From ranking algorithms to ‘ranking cultures’: Investigating the modulation of visibility in YouTube search results. Convergence: The International Journal of Research Into New Media Technologies, 24(1), 50–68. https://doi.org/10.1177/1354856517736982
  • Roose, K. (2020, October 24). How the epoch times created a giant influence machine. The New York Times. https://www.nytimes.com/2020/10/24/technology/epoch-times-influence-falun-gong.html
  • Ruiz, J., Featherstone, J. D., & Barnett, G. A. (2021). Identifying vaccine hesitant communities on Twitter and their geolocations: A network approach. In Proceedings of the 54th HICSS (pp. 3964–3969). University of Hawai’i Press. http://hdl.handle.net/10125/71096
  • Shneiderman, B., & Dunne, C. (2013). Interactive network exploration to derive insights: Filtering, clustering, grouping, and simplification. In Graph Drawing: 20th International Symposium, GD 2012, Redmond, WA, USA, September 19-21, 2012, Revised Selected Papers 20 (pp. 2–18). Springer.
  • Spohr, D. (2017). Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Business Information Review, 34(3), 150–160. https://doi.org/10.1177/0266382117722446
  • Sunstein, C. R. (2001). Republic.com. Princeton University Press.
  • Sunstein, C. R., Reisch, L. A., & Rauber, J. (2018). A worldwide consensus on nudging? Not quite, but almost. Regulation & Governance, 12(1), 3–22. https://doi.org/10.1111/rego.12161
  • The JoongAng. (2020, December 3). Choo Mi-ae was wrong 38% vs Yoon Suk-yeol was wrong 18% [4-company public opinion survey]. The JoongAng. https://www.joongang.co.kr/article/23936664#home
  • Thelwall, M. (2017). Web indicators for research evaluation: A practical guide. Morgan & Claypool.
  • The YouTube Team. (2019a, January 25). Continuing our work to improve recommendations on YouTube. YouTube Official Blog. https://blog youtube/news-and-events/continuing-our-work-to- improve
  • The YouTube Team. (2019b, December 3). The four Rs of responsibility, part 2: Raising authoritative content and reducing borderline content and harmful misinformation. YouTube Official Blog. https://blog.youtube/inside-youtube/the-four-rs-of-responsibility-raise-and-reduce
  • Thorson, K. (2020). Attracting the news: Algorithms, platforms, and reframing incidental exposure. Journalism, 21(8), 1067–1082. https://doi.org/10.1177/1464884920915352
  • Thorson, K., & Wells, C. (2016). Curated flows: A framework for mapping media exposure in the digital age. Communication Theory, 26(3), 309–328. https://doi.org/10.1111/comt.12087
  • Xu, W. X., Park, J., & Park, H. W. (2017). Longitudinal dynamics of the cultural diffusion of Kpop on YouTube. Quality & Quantity, 51(4), 1859–1875. https://doi.org/10.1007/s11135-016-0371-9
  • Yang, T., Majó-Vázquez, S., Nielsen, R. K., & González-Bailón, S. (2020). Exposure to news grows less fragmented with an increase in mobile access. Proceedings of the National Academy of Sciences, 117(46), 28678–28683. https://doi.org/10.1073/pnas.2006089117

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.