Another new study indicates algorithms aren't driving polarization. The evidence is preliminary, but part of a growing trend of studies that don't find evidence of the much fabled extremist rabbit hole.https://www.sciencedirect.com/science/article/abs/pii/S0747563220303733 …
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I share the frustration the data we need is locked up in the big companies, and nobody is funding the kind of longitudinal design we need. But the keys aren't under the light. We can try, like this study, but the world isn't separated neatly for our surveys to work like this.
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I agree on data access, but would also point out that most of the evidence in support of the algorithmic hypothesis is highly anecdotal and non-causal. The best analysis I've yet seen is
@BrendanNyhan's recent work w/web tracking data that also finds little support in my reading. - Show replies
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The data are not ideal, and we could not agree more that longitudinal tracking data analyzed by independent researchers is needed to really test this question.
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However to your point re Fox, they are included in the algo. category when that info is via SMS or news aggs. I suspect selection effects that limit impact on polarization. That type of info via algos is probably likely to pull on the tails ala Levendusky but needs long. data.
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Right, though the problem is they've also reoriented their TV programming to compete with their audience pulled by Breitbart on Facebook—where recs/ranking play a big role. (One producer put it as: "gotta compete with the crazy). It makes it very hard to measure, for sure.
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i think there's a diff between being in the algorithm-infused 'public sphere' and algorithms hands-off choosing the content you serve. A lot of the discussion is around the latter
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you actually can | longitudinal studies have successfully predicted everything from the comparative impacts of nature vs. technology on child development to the power of storytelling through media to bridge cultural divides .. as with all research, design + citation matters
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Part of a growing trend of studies with catchy contrarian conclusions whose design cannot answer the questions raised.
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