2020
DOI: 10.1016/j.ijmedinf.2020.104175
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Examining algorithmic biases in YouTube’s recommendations of vaccine videos

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Cited by 43 publications
(42 citation statements)
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“…For example, YouTube’s search algorithm has historically recommended videos that attracted the most views or clicks. However, the recent heightened concerns regarding harmful misinformation on YouTube has prompted algorithm changes, which have reportedly reduced consumption of borderline content by 70% (24). Further potential action through increasing the ranking and visibility of health content from reputable scientific sources such as universities, hospitals and health charities would improve consumer exposure to high-quality and reliable health information.…”
Section: Discussionmentioning
confidence: 99%
“…For example, YouTube’s search algorithm has historically recommended videos that attracted the most views or clicks. However, the recent heightened concerns regarding harmful misinformation on YouTube has prompted algorithm changes, which have reportedly reduced consumption of borderline content by 70% (24). Further potential action through increasing the ranking and visibility of health content from reputable scientific sources such as universities, hospitals and health charities would improve consumer exposure to high-quality and reliable health information.…”
Section: Discussionmentioning
confidence: 99%
“…Concerns have also been raised by health practitioners and commentators related to miscommunication of risk and subsequent spread of misinformation (Wise, 2021), which is contributing to the rise of anti-vax movements. Research suggests that once people start engaging with anti-vax content, they are likely to be directed towards more of the same, which propagates the spread of misinformation and anti-vax sentiment within such communities (Abul-Fottouh et al., 2020).…”
Section: Covid-19 Vaccine Rolloutmentioning
confidence: 99%
“…The second issue is connected to the recommendation algorithm itself. Due to its proprietary nature, the underlying incentive structures remain opaque [39]. Given the priming possibilities, there is a clear need for increased algorithmic transparency.…”
Section: Hyper-personalisation and Platform Designmentioning
confidence: 99%