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Again, though, my point is that the NYT didn't just sell advertising based on content type. That wouldn't be new. "Project Feels" was an ML initiative to study how readers felt after reacting to stories, and create an ad-targeting tool based on that. It's very specifically about offering advertisers the chance to choose stories based on predicted reader demographics, behaviors, and emotional response, instead of simply targeting stories by category or topic.

To decide whether to air your commercial alongside a drama or a comedy, all you need to do is watch the show (and perhaps collect viewer demographics). To decide which section of the print NYT to advertise in, all you need to do is read it. But Project Feels was only possible by studying the behaviors and emotional responses of readers. It didn't try to alter those emotions in specific ways, so it's not equivalent to Facebook's project, but it's also not the same as content-based targeting.




The NYT is replacing a human reader determining a story's emotional value with an ML program determining a story's emotional value.

You're implying that the NYT's ML software is capable of finding some kind of secret, subliminal emotional traits that wouldn't be detectable to a human reader. I don't find this believable at all.


I'm not. If the NYT had allowed advertisers to handpick stories to appear alongside, I wouldn't consider this substantially different.

What I'm interested in is the switch from choosing to appear alongside a category or keywords (content targeting) to appearing alongside a type of story, with its more specific impact. The impact of ML isn't better-than-human parsing, it's almost certainly worse-than-human. It's just a question of adding story-level targeting which wasn't previously available, along with access to user studies that go beyond demographics and engagement to self-reported emotions.




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