Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization

Ledwich, Mark; Zaitsev, Anna

doi:10.48550/arxiv.1912.11211

Cited by 15 publications

(27 citation statements)

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“…Meanwhile, their analysis of user comments suggests that users do migrate from milder to more extreme content over time. This is supported by Ledwich & Zaitsev [45], who find that YouTube's recommendation system actively discourages users from visiting extremist content. Their analysis suggests that YouTube directs traffic towards the two largest mainstream groups -the Partisan Right and the Partisan Left -away from more niche content they labeled Conspiracy, White Identitarian, and Anti-Social Justice Warrior.…”

Section: Youtube Recommendation and Topic Driftmentioning

confidence: 85%

Auditing the Biases Enacted by YouTube for Political Topics in Germany

Heuer,

Hoch,

Breiter

et al. 2021

Preprint

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With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We present audits as an important tool to enforce such laws and to ensure that a system operates in the public's interest. To examine whether YouTube is enacting certain biases, we collected video recommendations about political topics by following chains of ten recommendations per video. Our findings suggest that YouTube's recommendation system is enacting important biases. We find that YouTube is recommending increasingly popular but topically unrelated videos. The sadness evoked by the recommended videos decreases while the happiness increases. We discuss the strong popularity bias we identified and analyze the link between the popularity of content and emotions. We also discuss how audits empower researchers and civic hackers to monitor complex machine learning (ML)-based systems like YouTube's recommendation system.

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Section: Youtube Recommendation and Topic Driftmentioning

confidence: 85%

Auditing the Biases Enacted by YouTube for Political Topics in Germany

Heuer,

Hoch,

Breiter

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Overall, 14 studies implicated the YouTube recommender system in facilitating problematic content pathways ( Alfano et al, 2020 ; AVAAZ Report, 2020 ; Chen et al, 2021 ; Hussein et al, 2020; Matamoros-Fernández, 2017; O’Callaghan et al, 2013 ; O’Callaghan et al, 2015 ; Papadamou et al, 2020 ; Papadamou et al, 2020 ; Röchert, Weitzel, & Ross, 2020 ; Schmitt et al, 2018 ; Song & Gruzd, 2017 ; Spinelli & Crovella, 2020). Two studies did not implicate the YouTube recommender system in facilitating problematic content pathways ( Hosseinmardi et al, 2020 ; Ledwich & Zaitsev, 2019 ). Finally, seven studies produced mixed results ( Abul-Fottouh, Song, & Gruzd, 2020 ; Faddoul, Chaslot, & Farid, 2020 ; Kaiser & Rauchfleisch, 2019 ; Kaushal et al, 2016 ; Papadamou et al, 2020 ; Ribeiro et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…Those studies are thus described in other sub-sections. For example, Ledwich and Zaitsev (2019) included conspiratorial content as a proxy of radicalising content, so this study is discussed in the radicalising content section.…”

Section: Resultsmentioning

confidence: 99%

Systematic review: YouTube recommendations and problematic content

Yesilada

Lewandowsky

2022

Internet Policy Review

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“…The selection of good models, we claim, however, benefits from inclusion of psychological grounding and extensive testing. content is more diverse than natural consumption [47], and that real systems do not exhibit the strong extremism or polarization effects implied by theoretical models [28,39,40,43,57]. The study [5] points out that recommendation systems might lead to undesired changes in preferences, and proposes to design for safe preference shifts, which are preference trajectories that are deemed "desirable".…”

Section: Related Workmentioning

confidence: 99%

Towards Psychologically-Grounded Dynamic Preference Models

Curmei

Haupt

Recht

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.

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Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization

Cited by 15 publications

References 0 publications

Auditing the Biases Enacted by YouTube for Political Topics in Germany

Auditing the Biases Enacted by YouTube for Political Topics in Germany

Systematic review: YouTube recommendations and problematic content

Towards Psychologically-Grounded Dynamic Preference Models

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