Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems

By Derek O'Callaghan, Derek Greene, Maura Conway, Joe Carthy, Padraig Cunningham. 2015

172 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Political Science and International Relations, Sociology and Political Science, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Iason Gabriel
2. Po-Sen Huang
3. Jonathan Uesato
4. William S. Isaac
5. John F. J. Mellor
6. Borja Balle
7. Geoffrey Irving
8. Myra Cheng

In Political Science and International Relations

1. Iason Gabriel
2. Myra Cheng

In Sociology and Political Science

1. William S. Isaac
2. Iason Gabriel
3. Borja Balle
4. Myra Cheng
5. Lisa Anne Hendricks
6. T. Stepleton
7. Atoosa Kasirzadeh
8. Abeba Birhane

In AI Ethics, Fairness & Society

1. William S. Isaac
2. Iason Gabriel
3. Borja Balle
4. T. Stepleton
5. Atoosa Kasirzadeh
6. Abeba Birhane

Citations in the corpus

• Ethical and social risks of harm from Language Models (2021, background): “…switch to a human once the victim 11Some recommender systems have been found to respond to certain user behaviour by recommending more and more extreme viewpoints to increase engagement ((O’Callaghan et al., 2015; Yesilada and Lewandowsky, 2021); for counterexamples view (Möller et al., 2018)).”