3 ms·

I trust Yelp/Urbanspoon reviews the same way I trust Amazon reviews: By reading them and looking for clues that the reviewer is real and has astes and concerns

I trust Yelp/Urbanspoon reviews the same way I trust Amazon reviews: By reading them and looking for clues that the reviewer is real and has astes and concerns similar to mine. I gloss over uncritical reviews, whether they're real or not; a review that says "everything was great but the fries" I'll put stock in. It's possible that astroturfers could start taking that into account, but it's hard to imagine. The kind of people who stuff Yelp reviews generally have personality quirks that would prevent them from allowing any negative feedback.

joezydeco 15y ago
Yeah, Viewpoints Network tried the approach of "reviewers similar to me" and nobody really cared. Now they're just another opinion engine.
dcosson 15y ago
I would guess this is what a lot of people do and it's certainly what keeps me from giving up entirely on Yelp/Urbanspoon. But the downside is that ranking results by average review becomes useless; you have to dig pretty deep to find the place with an average of 1.5 stars that has a couple of thoughtful reviews indicating it's somewhere you might like. (i.e. you have to get your reviews in linear rather than constant time :) Companies like Hunch are addressing the problem from the social-networking standpoint that the author suggests (their Local app adds a Facebook friend layer on top of scraped Yelp reviews), but it seems most people are not yet dissatisfied enough with Yelp to consider getting themselves invested in yet another social networking service.
r00fus 15y ago
Like you I also check the quality of the reviews. However, I do cull my list initially based on summarized data. What this ultimately means is that when looking at a map or listing with sites and summarized data you can't trust the summarized results which may have been gamed/poisoned. Also poisoning the summary is very powerful in controlling which users even notice the property (e.g. 25 reviews 3.2 stars - might be worth looking at, 15 reviews 2 star = probably avoid... the difference is 10 bogus 5 star reviews, not hard) black-hat SEO works
pasbesoin 15y ago
I ended up with an excellent recommendation for a Chinese restaurant in Milwaukee, by reading past the lame Milwaukee Journal survey article to the comments. One commenter provided enough context, detail, and personal investment -- in the topic, but not the restaurant -- that I felt confident taking the recommendation. And he was right. This still seems to be the only way to get a good recommendation out of the web. It puts us in touch, but we still have to pay attention and filter and aggregate manually.

joezydeco 15y ago

Yeah, Viewpoints Network tried the approach of "reviewers similar to me" and nobody really cared. Now they're just another opinion engine.

dcosson 15y ago

I would guess this is what a lot of people do and it's certainly what keeps me from giving up entirely on Yelp/Urbanspoon. But the downside is that ranking results by average review becomes useless; you have to dig pretty deep to find the place with an average of 1.5 stars that has a couple of thoughtful reviews indicating it's somewhere you might like. (i.e. you have to get your reviews in linear rather than constant time :) Companies like Hunch are addressing the problem from the social-networking standpoint that the author suggests (their Local app adds a Facebook friend layer on top of scraped Yelp reviews), but it seems most people are not yet dissatisfied enough with Yelp to consider getting themselves invested in yet another social networking service.

r00fus 15y ago

Like you I also check the quality of the reviews. However, I do cull my list initially based on summarized data. What this ultimately means is that when looking at a map or listing with sites and summarized data you can't trust the summarized results which may have been gamed/poisoned. Also poisoning the summary is very powerful in controlling which users even notice the property (e.g. 25 reviews 3.2 stars - might be worth looking at, 15 reviews 2 star = probably avoid... the difference is 10 bogus 5 star reviews, not hard) black-hat SEO works

pasbesoin 15y ago

I ended up with an excellent recommendation for a Chinese restaurant in Milwaukee, by reading past the lame Milwaukee Journal survey article to the comments. One commenter provided enough context, detail, and personal investment -- in the topic, but not the restaurant -- that I felt confident taking the recommendation. And he was right. This still seems to be the only way to get a good recommendation out of the web. It puts us in touch, but we still have to pay attention and filter and aggregate manually.