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zeynep's profile
zeynep tufekci
zeynep tufekci
zeynep tufekci
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@zeynep

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zeynep tufekciVerified account

@zeynep

Complex systems, wicked problems. Society, technology, science and more. @UNC professor. @NYTimes columnist. My newsletter is @insight: http://www.theinsight.org 

floating in a most peculiar way
theinsight.org
Joined August 2009

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    1. Nick Seaver‏ @npseaver 7 Nov 2018

      The implicit takeaway here is that, counter to arguments that folks like @zeynep have made, the recommender biases to the popular, not to the fringe. But this research doesn't show that!

      1 reply 0 retweets 3 likes
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    2. Nick Seaver‏ @npseaver 7 Nov 2018

      What it shows is that, for a brand new user, who makes five interactions with the service, the recommended videos become progressively more popular (and, for some reason, longer). What assumptions are in here?

      1 reply 0 retweets 7 likes
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    3. Nick Seaver‏ @npseaver 7 Nov 2018

      Primarily, that the YouTube API recommendations are the same as the up-next auto-play recommendations. This is a bad assumption, as the authors note, because YouTube recommendations are *personalized*—the recommendations aren't just contingent on the video, but on your history!

      1 reply 0 retweets 5 likes
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    4. Nick Seaver‏ @npseaver 7 Nov 2018

      They try to get out of this issue by suggesting that the API recommendations represents a "baseline viewer," like if you always used incognito mode. The implication is that personalization would just skew you away from that baseline a bit—that the baseline is a sort of average.

      1 reply 0 retweets 2 likes
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    5. Nick Seaver‏ @npseaver 7 Nov 2018

      But that's not how recommender systems work! If you're a brand new user, you pose the "cold start problem"—without data, data-driven recommendation doesn't work. So, if you're a recommender system, you cope by trying to find stuff this mystery person is likely to like.

      1 reply 0 retweets 4 likes
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    6. Nick Seaver‏ @npseaver 7 Nov 2018

      What is a random person likely to like? Popular shit. (That's what it means to be popular!) So, a tendency to popularity is *exactly* what you'd expect a recommender to have, given a mystery user with no history. But if you are logged in or have an IP address, this ain't you.

      1 reply 1 retweet 14 likes
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    7. Nick Seaver‏ @npseaver 7 Nov 2018

      (I think the survey work is more reliable, and the bias toward *length* is a weird and interesting finding, probably the result of length correlating with something else. But experimental studies of algorithms often have exactly this kind of problem. Your bots aren't real users!)

      1 reply 0 retweets 3 likes
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    8. Nick Seaver‏ @npseaver 7 Nov 2018

      For more rain on your parade of interface experiments on recommender systems, check out this old piece of mine: https://nick-seaver.squarespace.com/s/seaverMiT8.pdf … (and, uh, #cscw2018, I guess?)

      2 replies 1 retweet 16 likes
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    9. zeynep tufekci‏Verified account @zeynep 7 Nov 2018
      Replying to @npseaver

      It's a hard space to research without access to the data only the companies have and are keeping opaque! What you say is true, but also that "popular stuff" and "more extreme/hateful" aren't in contradiction. As they note, recommended stuff get more popular over time..

      1 reply 0 retweets 2 likes
    10. Nick Seaver‏ @npseaver 7 Nov 2018
      Replying to @zeynep

      Yeah, but the main issue here is that the "baseline viewer" they construct is not normal, but in fact unusual in a very specific way that usually invokes atypical responses from recommender systems.

      1 reply 0 retweets 0 likes
      zeynep tufekci‏Verified account @zeynep 7 Nov 2018
      Replying to @npseaver

      Yes, cannot start from zero (or stay close there) to understand how it behaves in the wild.. On the other hand, it is tough and I'm glad there is these efforts to try to get at it.

      3:16 PM - 7 Nov 2018
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      • Mace ‘it's turtles all the way up’ Ojala Nick Seaver
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