Just recently, I was in Ubud, Bali. A week or so of yoga, sightseeing and rejuvenation… towards the end I had a couple of spare days and decided to buy a “nicer” hotel. Now everyone has their own preferred platform(s) for me it was booking.com. I’d used it before for business and had a business account set up. There's something about it that seems a little bit more “legitimate to me”. Maybe it's their appeal to business travellers.. anyway.
Ubud is amazing btw... 😎🌋🗿
On my selected dates in Ubud there were 1,320 properties found… which one to choose? It's a similar task we are faced with all the time on the internet: which similar product or services should I choose? Which “value model” should I construct in my mind?
I'll pick the er... Blue one??
For me the formula went like this:
Accomodation Ranking = Price x Location (not in the center, not too far away) x Views x Photos x Reviews
So armed with this formula and 45mins by the pool - I found what looked a perfect fit... and just look at those wonderful reviews averaging at 4.7! (See below).
The perfect fit according to my formula! 😵
Yeah it's Google reviews but still... 😵
The major observation here - beside the huge 4.7 overall score - is the massive top-heaviness of these ratings... an absolute barrage of 5 star reviews, offset by very little else. Top this with the fact there had been 1308 reviews!!!!! That would have required almost 5 guests reviewing the hotel every single day for an entire year!
Below is a review from TripAdvisor for the same hotel - which is way more aligned with what I actually saw:
A review from Trip Advisor that was a lot more aligned with my actual experiences!
We all know google reviews are easy to manipulate. Heres some reasons why:
Mr Taylor Described himself as "cynical"...
Surely Booking.com is "above" this right... you have to stay there to write a review right...???
There's a nice little (actually massive) scam going on where hotel owners “pay people” to make a booking at their own property and give a positive review… the money from the "booking" of course comes back to the hotel. Some other benefit is worked out on the side with the reviewer.
Another "cool" thing with booking.com is that if you cancel your booking mid-stay - you can't leave a review. Which is what happened in my case. This clearly artificially inflates ratings.
Lord Saruman overseeing his army of fake reviewers...
Yeah they are all fake too. Althose product reviews you spend so much time sifting through, reading and comparing. You can easily tell the fake ones right? Well sometimes... but you can't read every single review on the internet!
In Amazon-land- personal data used by sellers to gather fake "verified reviews"! This "practice" known as "brushing", sees sellers obtain people's name and address and then send them goods which they did not purchase - for the sole purpose of writing a favourable review of said product...
Yep. Thats happening. And it's rife!
In the following article from the BBC, Architect Paul Bailey, in Essex believes he may have been targeted. He received a number of unexpected "gifts", including a key-ring, a phone case, a tattoo removal kit and a charcoal toothpaste set. for more details check out here: https://www.bbc.com/news/uk-47952165
The infamous Red Hen incident where Sarah Sanders was refused service at a restaurant, which then received 100s of fake negative reviews on Yelp, Facebook, Google etc
Reviews on owned domains have always been problematic.. even more so now that companies like Samsung are using tools called (no joke) "Bazaaar Voice".. that generate “user-generated content marketing”...
So yep - scrape all the reviews for a certain place or places and determine if you can see fraud somehow... sounds like an idea. EXCEPT!!! Google only lets you return 5 reviews from a place call! The last 5 reviews. WTAF? This issue is infuriating and has been an ongoing feature request since Feb 12, 2015 - with 371 users starring the issue and counting. See here for more: https://issuetracker.google.com/issues/35825957
You can try APIFY... it's a pretty cool tool that allows you to create "apps or scrapers" that amongst other things can scrape google maps reviews. It works pretty good in our experience. Here's some more details.
* Scrapes actual Google Reviews. $49 per month minimum..
* Breaks Google's T&Cs (not allowed to scrape anything) but is offset by multiple proxy servers.
* Does Not capture all the fields the Google place API does.
The biggest issue though is: even if you have all the reviews, how do you determine which ones are fake?
APIFY, this tool is pretty cool $49 a month.. yeah.. maybe you can get something happening? Need to determine if those reviews are fake or not first though..
akespot is a great concept - but it's generally inaccurate. In some cases, like ours above - it is great though..
Well... the results aren't too promising... according to a study by Cornell University way back in 2011!!!:
1) Human judges no better than random chance in determining whether reviews are fake or not
2) Humans suffer from a "truth bias” - assuming that what they are reading is true until they find evidence to the contrary.
3) Truthful hotel reviews, use concrete words relating to the hotel, like "bathroom," "check-in" or "price." Deceivers write more about things that set the scene, like "vacation," "business trip" or "my husband."
4) This is imaginative vs. informative writing, deceivers use more verbs and truth-tellers use more nouns.
5) Deceivers include more first person pronouns. If you're anxious about coming across as sincere, you talk about yourself more. That's probably why words like 'I' and 'me' appear more often in fake reviews.
6) Using these approaches, the researchers trained a computer on a subset… and achieved 89.8 percent accuracy.
Read more about this study here.
Here we see an actual example taken from the Cornell study. It's interesting to see. Clearly the fake review is:
2. "Sets the scene" / is low on very specific details
3. Uses "I", "we" a lot...
An example of a Real v Fake review taken from the Cornell Study.
1. You need to treat every review you read on the internet as potentially fake
It doesn't matter the network, even supposed "closed loop" review sites like bookng.com or even Amazon - are easily and mercilessly circumvented by unscrupulous operators. Google reviews and Facebook are just a laughing stock - but they really aren't too much worse then the others.
2. Detecting Fake Reviews with a Model is inaccurate and a work in progress
* Detecting fake reviews from an algorithm is going to have inherent accuracy.
* Is this error acceptable - ie is it better than nothing?
* Cornell study was 89% accuracy. Fakespot seems to be ~75% accuracy.
* Reviewers (not all) will likely become more sophisticated over time.
3. Detecting Fake Review with a “Trust Network” is likely the best option
* More accurate
* Only rely on reviews from people you know/trust <- social network
* Have a system/(maybe government agency) that you can trust for reviews.
Aside: Why does Google make it basically impossible to download reviews data (unless from GMB). And why the same policy for scraping SERPs? These services are now so vital in how the internet and hence much of our society works - that unfortunately they now serve much more important roles than just the original one: ie to make Google money.
Unleash the Fake Reviews
We're in a pandemic... a pandemic of busy-ness! As a society we appreciate quality work