Science by YouTube: an Analysis of YouTube’s Recommendations on the Climate Change Issue

Dündar, Pınar; Ranaivoson, Heritiana Renaud

doi:10.15847/obsobs16320222061

(OBS*)

2022

DOI: 10.15847/obsobs16320222061

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Science by YouTube: an Analysis of YouTube’s Recommendations on the Climate Change Issue

Pınar Dündar

Heritiana Renaud Ranaivoson

Abstract: Citizens increasingly use online platforms, and in particular YouTube, to get informed. This paper addresses the role of YouTube’s recommendation system in science communication to assess whether YouTube's recommendation system is creating filter bubbles on the climate change issue. It, therefore, contributes to the growing literature that finds that such filter bubbles actually do not exist. The paper shows that users are likely to get content with similar opinions, at least for a few recommended videos in a … Show more

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2023

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Cited by 2 publications

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Simion and Kelp on trustworthy AI

Carter

2023

AJPH

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Simion and Kelp offer a prima facie very promising account of trustworthy AI. One benefit of the account is that it elegantly explains trustworthiness in the case of cancer diagnostic AIs, which involve the acquisition by the AI of a representational etiological function. In this brief note, I offer some reasons to think that their account cannot be extended — at least not straightforwardly — beyond such cases (i.e., to cases of AIs with non-representational etiological functions) without incurring the unwanted cost of overpredicting untrustworthiness.

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Simion and Kelp on trustworthy AI

Carter

2023

AJPH

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Platforms and Exposure Diversity: Towards a Framework to Assess Policies to Promote Exposure Diversity

Ranaivoson,

Domazetovikj

2023

MaC

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The fragmentation of consumption and algorithms’ increasing impact on how content is recommended and displayed makes it even more important to analyse and promote exposure diversity, i.e., the extent to which audiences are exposed to, discover, and engage with diverse content. Although there is a growing literature addressing how to define media diversity in the context of the challenges posed by platformisation, this article translates the normative dimensions into a framework for operationalising exposure diversity into a tangible policy goal, taking into account datafication and its consequences in terms of increasing data requirements towards platforms. The main objective of this study is to analyse initiatives to assess exposure diversity in the platform era and to discuss how such assessment could be improved, particularly for policy initiatives. This involves addressing several challenges of existing approaches for the assessment of exposure diversity related to defining an appropriate frame of reference, determining the degree of diversity required, dealing with data transparency issues, and promoting user autonomy. To achieve this, we propose a framework for analysing initiatives aimed at assessing and promoting exposure to media diversity. Our framework is composed of four key features: measures (type of initiative), metrics (quantifying exposure diversity), data collection methods, and data requirements. We apply this framework to a set of 13 initiatives and find that policy initiatives can benefit from adopting metrics based on distances and experimenting with data collection methods.

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Science by YouTube: an Analysis of YouTube’s Recommendations on the Climate Change Issue

Cited by 2 publications

References 21 publications

Simion and Kelp on trustworthy AI

Simion and Kelp on trustworthy AI

Platforms and Exposure Diversity: Towards a Framework to Assess Policies to Promote Exposure Diversity

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