The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems
Open accessAbstract
In addition to hosting user-generated video content, YouTube provides recommendation services, where sets of related and recommended videos are presented to users, based on factors such as co-visitation count and prior viewing history. This article is specifically concerned with extreme right (ER) video content, portions of which contravene hate laws and are thus illegal in certain countries, which are recommended by YouTube to some users. We develop a categorization of this content based on various schema found in a selection of academic literature on the ER, which is then used to demonstrate the political articulations of YouTube’s recommender system, particularly the narrowing of the range of content to which users are exposed and the potential impacts of this. For this purpose, we use two data sets of English and German language ER YouTube channels, along with channels suggested by YouTube’s related video service. A process is observable whereby users accessing an ER YouTube video are likely to be recommended further ER content, leading to immersion in an ideological bubble in just a few short clicks. The evidence presented in this article supports a shift of the almost exclusive focus on users as content creators and protagonists in extremist cyberspaces to also consider online platform providers as important actors in these same spaces.
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Citations
Cites
Populist Radical Right Parties in Europe
Mudde, C. (2007) · doi:10.1017/cbo9780511492037 · Cited by 4,694
Gillespie, T. (2010) · New Media & Society · doi:10.1177/1461444809342738 · Cited by 2,522 · cited 2×
“…work to analyze the extent to which a viewer may be exposed to such content and thereby highlighting ‘‘Whether these interventions are strategic or incidental, harmful or benign, they are deliberate choices that end up shaping the contours of public discourse online’’ (Gillespie, 2010, p. 358).”
BackgroundGillespie, T. (2014) · Media Technologies · doi:10.7551/mitpress/9042.003.0013 · Cited by 1,016
Cited by
Auditing radicalization pathways on YouTube
Ribeiro, M. H., Ottoni, R., West, R., Almeida, V. A. F., & Meira, W. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372879 · Cited by 580
Ethical and social risks of harm from Language Models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04359 · Cited by 477
Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018) · Information, Communication & Society · doi:10.1080/1369118x.2018.1444076 · Cited by 387
Recommender systems and the amplification of extremist content
Whittaker, J., Looney, S., Reed, A., & Votta, F. (2021) · Internet Policy Review · doi:10.14763/2021.2.1565 · Cited by 135
Kaiser, J., & Rauchfleisch, A. (2020) · Social Media + Society · doi:10.1177/2056305120969914 · Cited by 74
Metadata
- Authors
- O’Callaghan, D., Greene, D., Conway, M., Carthy, J., & Cunningham, P.
- Venue
- Social Science Computer Review
- Publisher
- SAGE Publications
- Published
- 2014-10-16
- Volume
- 33
- Issue
- 4
- Pages
- 459-478
- ISSN
- 0894-4393 (ISSN-L)
- 1552-8286
- OpenAlex
- W2127109099
- Cited by
- 205
- References
- 3
- FWCI
- 10.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
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