Somewhat silly we’re still relying on big companies to collect this data when they then gate it. The timing is right with LLMs that can make maintaining and rating reviews less burdensome on human labor (“moderation” broadly speaking). It’s just text blobs and possibly images linked to people and places.
The traditional restaurant reviewer filled this role much better. His role as a trusted arbiter was closer to what people want from a site like Yelp, but it’s very labor intensive and obviously doesn’t scale.
Only ways I could see it working at scale are Wikipedia model, which is labor intensive for a large number of people, or some kind of web of trust / reputation system, which is complicated and attackable.
High quality people want to be helpful, they just need a platform where curation and distribution takes place. See: here. I’m just tired of seeing startups and other companies build and kill these knowledge and community based third spaces, and the intangible value inherent to them.
(customer identity is a component of my work in fintech as an infosec lead, I get paid to squash bots and fraud)
But it would quickly become a victim of its own success as people start using it and the database gets big enough to be useful. bots and spammers would swarm the platform to try to game it. That would require a constant cat and mouse game that would not be fun. Without significant ongoing attention, the site is going to become worthless. In order for a site like this that is labor intensive and not fun to exist, it would need to be monetized at least to the extent it can pay salaries. That's the point at which I don't see any realistic solution. In order to maintain neutrality, it would need to be supported by users, meaning users would need to pay. I don't see very many people paying for something like this. And even if some would, there would have to be some benefit or reason for paid users to pay, and that seems like it would open the door for more cat and mouse games and abuse.
Just a hard problem I think.
like, imagine a vegan, a devout hindu, a muslim, a carnivore, a neighborhood kid, and a foodie editing an article about a texas steakhouse that is known for its barbecued pork. whatever text any of these people finds unfairly biased is going to get deleted from the article, and what's left is what they can agree on
this process produces astoundingly reliable articles on contentious topics like the armenian genocide, but the results are predictably of poor literary quality, and generally not very useful for things like guessing which restaurant you would prefer to eat at
Google seems to have done a good job with review spam on Google Maps, better than their job with SEO spam and malicious search results, and deep+diverse datasets are effectively Alphabet's (not just Google's) entire business.
Yelp would have a very hard time getting to know as much about review submitters, an open data platform would have a hard time gathering even what Yelp already collects.
Zagat worked pretty well for "foodies" back in a day when raters had to physically mail in hardcopy questionnaires and people had to buy guidebooks in stores. I'm not sure how you duplicate that today absent physical pay-walls on both sides which I can't really see working.
I do think the various rating sites--Yelp, TripAdvisor, Google--"work" in the sense that they're probably better than picking at random. And you can't really call the local newspaper's food critic on the phone (which is what my dad used to do). So I'm not sure what the alternative really is. Yes, there's some word of mouth but that's pretty random.
The results clearly indicate it is.
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