A new paper has been making the rounds with the intriguing claim that YouTube has a *de-radicalizing* influence. https://arxiv.org/abs/1912.11211 Having read the paper, I wanted to call it wrong, but that would give the paper too much credit, because it is not even wrong. Let me explain.
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Let’s not forget: the peddlers of extreme content adversarially navigate YouTube’s algorithm, optimizing the clickbaitiness of their video thumbnails and titles, while reputable sources attempt to maintain some semblance of impartiality. (None of this is modeled in the paper.)
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After tussling with these complexities, my students and I ended up with nothing publishable because we realized that there’s no good way for external researchers to quantitatively study radicalization. I think YouTube can study it internally, but only in a very limited way.
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If you’re wondering how such a widely discussed problem has attracted so little scientific study before this paper, that’s exactly why. Many have tried, but chose to say nothing rather than publish meaningless results, leaving the field open for authors with lower standards.
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In our data-driven world, the claim that we don’t have a good way to study something quantitatively may sound shocking. The reality even worse — in many cases we don’t even have the vocabulary to ask meaningful quantitative *questions* about complex socio-technical systems.
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Consider the paper’s definition of radicalization: "YouTube’s algorithm [exposes users] to more extreme content than they would otherwise." Savvy readers are probably screaming: There is no "otherwise"! There is no YouTube without the algorithm! There is no neutral!
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That’s the note on which I’d like to end: a plea to consider that the available quantitative methods can’t answer everything. And I want to thank the journalists who’ve been doing the next best thing — telling the stories of people led down a rabbit hole by YouTube’s algorithm.
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Totally right, this isnt broadcast TV, viewers now mutate content with their comments. Comments are now permanently apart of the content for others to consume. The context of viewing shifts and its unclear if the original content is always the strongest signal with the viewer.
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This is a great -- thank you for this
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I briefly cover the concept of recommendation systems, resulting in insular segregation of groups leading to potential radicalisation, when teaching Data Mining. Any recommendations for key papers (including your own) to throw to my students?
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