Comment by o10449366 | Hacker News Reader

Comment by o10449366 | Hacker News Reader

Parent Full thread

"But today’s technology works at a far higher level. Consider an example involving Facebook. In 2017, the newspaper The Australian published an article, based on a leaked document from Facebook, revealing that the company had told advertisers that it could predict when younger users, including teenagers, were feeling “insecure,” “worthless” or otherwise in need of a “confidence boost.” Facebook was apparently able to draw these inferences by monitoring photos, posts and other social media data."

Ironically, that's exactly what the NYT has started doing recently![0] I think they've realized just how lucrative selling these interferential insights can be; regardless of how accurate these metrics are, advertisers eat them up. How long until NYT writers start receiving pressure from upper management to produce content that maximizes emotional output from their readers? It's possible that proper restraint and separation of departments can be maintained, but I think history has shown that when profit motives are involved, greed often usurps ethics.

[0] https://investors.nytco.com/press/press-releases/press-relea...

> [Facebook] had told advertisers that it could predict when younger users, including teenagers, were feeling “insecure,” “worthless” or otherwise in need of a “confidence boost.”

> Ironically, that's exactly what the NYT has started doing recently!

No, that's not what the NYT is doing at all, and it's disinformation to say that it is. Facebook was inferring the emotional state of its users, the NYT was inferring the emotional content of its article content [1]. Those are very, very different things.

[1] From your link: "The result was an artificial intelligence model that predicts emotional response to any content The Times publishes... Perspective targeting allows advertisers to target their media against content predicted to evoke reader sentiments like self-confidence or adventurousness." (emphasis mine)

> Based on the machine learning powering Project Feels, nytDEMO has launched perspective targeting as a new ad product. Perspective targeting allows advertisers to target their media against content predicted to evoke reader sentiments like self-confidence or adventurousness.

> nytDEMO will also soon launch Readerscope, an AI-driven data insights tool that summarizes what The Times audience is reading using anonymized data to visualize who is interested in which topics and where they are.

> Readerscope can be used as a content strategy tool to develop creative ideas for branded content or campaigns by searching a brand’s target audience segment (e.g., millennial women) to understand what they're reading, either as topics or as representative articles exemplifying those topics. It can also help brands find the right audience or geography for a certain message by searching a topic (e.g., human rights, philanthropy or travel ) and seeing which audience segments over-index for interest about that subject. Topics are algorithmically learned from The New York Times article archive using state of the art natural language processing, and all of the reader segments are targetable with media on NYTimes.com.

> content predicted to evoke reader sentiments like self-confidence or adventurousness

I'm not convinced that is different from Facebook's model - it's just less powerful.

Advertisers have always tried to place their ads alongside content that favors their brand identity, sure, but that's about the style of the content. That looks like Burton sponsoring top-tier snowboarders or Chanel advertising in Vogue. Watching Olympic snowboarders might make some people feel adventurous, but Burton's placement is also aspirational and cultural, a way of simply forming an association between the brand and high performance.

The NYT model didn't just put content alongside stories about adventure, it put content alongside stories expected to evoke adventurous sentiments. If a story about a daring Arctic expedition makes you feel relieved to be comfy at home, it could still associate a brand with adventure, but it's outside the sentiment target. That is inferring the emotional state of users, rather than of content. The main difference is that the NYT was making session-level judgements, rather than long-term ones. I find that much less objectionable (even if it's only out of a lack of data), but it's still in the category of mind-state targeting rather than content alignment.

Advertising based on the type of content is nearly as old as advertising. Some TV commercials air during dramas, others during comedies, others during sporting events. If you want to get more specific, you can elect to have your ad air during a certain show.

The only difference between this and what the NYT is doing is the former requires slightly more research on the part of the advertiser, to learn what the show or article is about.

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.

Surprised the author didn't reference the time when Facebook set out to intentionally manipulate hundreds of thousands of people by playing with their emotions [1]. Facebook made like 200,000 people start to feel sad on purpose and bragged about their abilities in a "scientific paper". That's in quotes because their lawyers say that you give your "informed consent" to be psychologically experimented on when you sign up for Facebook.

Facebook does in fact decide to basically torture people into a depression with its power. Facebook is an evil entity and their intentional emotional torture exerted onto its users far surpasses the kind of thing you're quoting NYT for. Not excusing NYT here but Facebook is next-level-evil when it comes to psychological experimentation on unwilling and non-consenting users.

[1] Lots of articles on this, here's a random one from Google searching: https://www.theatlantic.com/technology/archive/2014/06/every...

(edited for accuracy, millions->thousands)

Well said. Remember how whimsical and far-fetched Monsters Inc. was with the corporations feeding off of childrens' fear..? Shiver

Reply on news.ycombinator.com