State of Review Fraud: A Study of 4 Million Reviews

1. State of Review Fraud:
A Study of 4 Million Reviews
2021

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Greg Sterling
Analyst, blogger, researcher
Tracking evolution of local digital
marketing since 1999
Former editor Search Engine Land
VP Market Insights Uberall

3. 3
100% SaaS B2B
170+ resellers
7 Offices (DE, UK, FR, NL, US, CA)
1,350,000+ customer locations
2013 founded
1,500+ enterprise clients
300+ employees
125+ platforms and directories
Our customers rely on an experienced team that has helped boost the online
visibility of 1,350,000+ business locations
Uberall at a Glance

4. Brands and partners from
all over the world trust
Uberall to help them
create powerful ‘Near Me’
Customer Experiences
4

5. • Consumers rely on reviews to make purchase
decisions
• Incremental improvements in scores affect
visibility and conversions
• Google explicitly uses reviews as a ranking
factor
• Reviews have gained increasing importance
for SEO
• Google is the largest local reviews site -- by
far
What We Know

6. Reviews Are Trusted
Source: Kantar global survey, June 2020 (n=7,753)
“[We] trust online reviews as much as
personal recommendations
from friends or family.”
Most Trusted Sources of Information on Brands and Services

7. Source: TrustPilot survey of 1,627 consumers (2019)
Increase Brand Trust

8. Influence Over Consumer Purchases
Multiple studies:
82% - 97% of consumers
say reviews influence
their purchases
Source: Consumer surveys from Uberall, PowerReviews,BrightLocal, BIA/Kelsey, Bazaarvoice,
ReviewTrackers (2015 – 2020)

9. Top Single Influence on Purchases
Source: LSA-SOCi consumer survey (2018), n=400

10. I don’t have a
minimum.
2 Stars 2.5 Stars 3 Stars 3.5 Stars 4 Stars Above 4 Stars Wt. Average
Retail Services 19% 4% 3% 18% 19% 29% 7% 3.6
Personal Care Services 15% 1% 4% 15% 19% 35% 11% 3.8
Restaurants 12% 2% 3% 14% 25% 32% 13% 3.8
Medical/dental services 14% 1% 2% 10% 14% 36% 24% 4.0
Automotive repair 14% 0% 4% 9% 13% 43% 17% 3.9
Lawyers/legal services 17% 2% 2% 9% 13% 33% 24% 4.0
Realtors 22% 2% 2% 9% 17% 32% 17% 3.9
Home
services/contractors
15% 2% 2% 9% 11% 39% 23% 4.0
Insurance and financial
services
17% 1% 3% 10% 14% 35% 21% 4.0
Accountants 20% 1% 3% 9% 13% 35% 20% 4.0
Travel/hotels 13% 2% 2% 15% 18% 35% 16% 3.9
MINIMUM NUMBER OF STARS YOU REQUIRE BEFORE YOU’LL CONSIDER A BUSINESS BY CATEGORY
Source: SOCi consumer survey (2018)
Most Cases: 4.0 Minimum Star Threshold

11. A star rating increase of
just 0.1 stars can increase
conversion rates by 25%
Source: Uberall, Reputation Revolution (2019)
Small Improvement =
Significant Gain

12. Local Ranking Factor: Prominence
“Prominence is based on information that
Google has about a business, from across the
web, like links, articles, and directories.”
Google review count and review score factor
into local search ranking.
Organic SEO rankings a factor as well.
Source: Google
https://support.google.com/business/answer/7091?hl=en

13. Source: Whitespark, Local Search Ranking Factors Survey (2021)
Reviews: Top GMB Conversion Influences
Top 3 all review-related:
Scores, sentiment in
review text, quantity of
reviews.

14. Source: Whitespark, Local Search Ranking Factors Survey (2021)
Local SEOs: Review Importance Up
Local SEO
perceptions of the
ranking influence of
Google Reviews

15. Google: Reviews = Revenue

16. Sites Consulted for Local Business Reviews
Source: Uberall consumer survey, April 2021 (n=1,018 US adults)

17. Consumer Awareness of Fake Reviews
• 80% have read at least
one fake review in the
past year
• 33% said they’ve seen
multiple fake reviews
Source: BrightLocal Local Consumer Reviews survey 2020

18. Coverage Driving Awareness

19. Fake Reviews Are …
Source: Uberall consumer survey, April 2021 (n=1,018 US adults)
66.4% said that fake
reviews were a
problem online.

20. Multiple Review Sites
70% now look at multiple
review sites before making
a choice.
67% say they regularly ‘question
the authenticity of reviews.’
Source: BrightLocal Local Consumer Reviews survey 2019

21. Do You Look at Multiple Review Sites?
Source: Uberall consumer survey, April 2021 (n=1,018 US adults)
88.3% checking
multiple review sites
at least sometimes.

22. Spotting Fake News – And Reviews
Source: University of Utah (n=8,200); subjects were asked to evaluate the accuracy of a series of Facebook headlines and then rate their own abilities to
identify false news (2021)
“3 out of 4 people overestimated their
ability to identify fake news.”

23. Review Factors Considered
Source: BrightLocal Local Consumer Reviews survey 2020

24. 1. Business owners pay vendors that sell both positive and negative
online reviews.
2. Business owners directly or indirectly generate fake reviews for
themselves (through fake profiles).
3. Customers lying or exaggerating a negative experience to obtain a
refund or some other benefit (e.g., discount).
4. Current employees writing positive reviews on behalf of an
employer.
5. Review clusters (e.g., friends and family) writing positive or negative
reviews within a short period of one another.
6. Ex-employees writing negative reviews in retaliation for being
terminated/laid off.
7. Competitors writing negative reviews about a rival business.
Source: Objection.co 2021
Specific Types of Review Fraud

25. Fake Review Vendors Hiding in Plain Sight

26. FTC vs. Sunday Riley Skincare (2019)
• [M]anagers and employees at Sunday Riley Skincare wrote
reviews of their company’s products, using fake accounts
they created to hide their identities [on Sephora.com].
• After Sephora removed some phony employee-created reviews
… Sunday Riley managers doubled down on the deception,
getting what one manager described as “an Express VPN
account [to] ... allow us to hide our IP address and location
when we write reviews.”
• CEO sent email directing employees to “create three accounts
on Sephora.com, registered as … different identities.” The
email included step-by-step instructions for setting up fake
personas, using a VPN to hide their identities. She also
directed them to focus on certain products and that they should
be “[a]lways leaving 5 stars.”

27. FTC vs. Sunday Riley Skincare (2019)
One of the emails:
Now that Saturn is up and Space Race coming up next week, we need to make sure
the reviews for clients stay positive and help generate and [sic] confidence in the
products. Credibility is key to the reviews! If everyone could write at least 3
reviews for Saturn over the next week, and some for Space Race the week after.
I would encourage you to create profiles ASAP and write a couple reviews on a
makeup, hair or nail product to build a profile history. Please make sure to follow the
guidelines for VPN (see below) as this is essential so the reviews don’t get traced
back to our IP address. When reviewing Saturn please address things like how
cooling it felt, the green color, the non-drying mask effect, radiance boosting, got rid of
your acne after a couple uses. The biggest points of difference for this mask and other
acne masks 3 are how this mask increases radiance and doesn’t dry out the skin like all
other acne masks do. It helps to make yourself seem relatable – like you know how
hard acne is and you’ve tried everything, and this one actually works or mention things
like yes, it’s a little more expensive, but works incredible [sic] well compared to the
cheaper masks out there. If you need any help with things to come up with to say, feel
to ask myself, Sunday, or Addison.
As reviews come in, read them too. If you notice someone saying things like I
didn’t like “x” about it, write a review that says the opposite. The power of
reviews is mighty, people look to what others are saying to persuade them and
answer potential questions they have.

28. Study: 4M Reviews on 4 Sites
Uberall research partner Objection.co analyzed
4M reviews across Google, Facebook, Yelp,
Tripadvisor.
We wanted to know:
• How big a problem is review fraud really?
• Which of the four sites has the highest overall
percentage of suspect reviews (e.g., Google vs.
Yelp)?
• Which location features the highest percentage
of suspect reviews (e.g., Miami vs. New York)?
• Which industry has the highest percentage of
suspect reviews (e.g., locksmiths vs. dentists)?

29. Cities and Business Categories
1. Cosmetic Surgeons
2. Dermatologists
3. Moving Companies
4. Personal Injury Lawyers
5. Digital Marketing Agencies
6. Dentists
7. Locksmiths
8. Real Estate Agents
9. Restaurants
10. Insurance Life + Health (brokers)
11. Home Remodeling (contractors)
12. Automotive Dealership
13. Pharmacies/Drug
14. Luxury Hotels
15. Motels
16. Air Conditioning and Heating (HVAC)
17. Veterinarians
18. Plumbers
19. Nursing homes and assisted living
20. Vocational/Technical Schools/Colleges
1. New York
2. Los Angeles
3. Chicago
4. Philadelphia
5. Dallas-Ft. Worth
6. San Francisco-Oak-San Jose
7. Washington, DC (greater metro)
8. Houston
9. Boston (Manchester)
10. Atlanta
11. Phoenix
12. Tampa-St. Pete
13. Seattle-Tacoma
14. Detroit
15. Minneapolis-St. Paul
16. Miami-Ft. Lauderdale
17. Denver
18. Orlando-Daytona
19. Cleveland-Akron
20. London, England UK

30. Overall % of Reviews Suspect/Fake
7.3% 5.2% 4.9%
Note: TripAdvisor analysis covers only three business categories (Luxury Hotels, Motels & Restaurants)
10.7%
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

31. Methodology: Complex Version
Fake review generation
Two ways:
• Manually by people
• Automatically by bots
Text generative models (TGMs):
• NLP machine learning methods
• Human-like written text
• Microsoft Touring Natural Language
Generation: 17 billion parameters (Feb
2020)
Lots of NLP, ML and comparisons of real and
fake review databases.

32. Methodology: Simplified Version
Collection of data, examination and analysis of:
• Business profiles
• Review content
• Review profiles
Profile analysis includes:
• Distance Matrix Analysis – looks at distance between
businesses reviewed by same profile and review frequency.
• Review Pod Analysis – number of concurrent reviewers
• Comparison to DBs of known fraudulent profiles
Content analysis includes:
• NLP to understand reviews content and compare to
multiple databases of fraudulent content
• Keyword Repetition Analysis – keyword stuffing in
reviews
• ESL/Basic Grammatical Mistakes – offshore review
factories: China, India, Bangladesh, Philippines
• Multiple other variables

33. Specific Types of Fraud
Source: Ubrerall-Objection.co Fake Reviews Study (2021)
Types Profile/Content Google Facebook
Paid Fake Reviews
Fake Profiles with fake
reviews 7.40% 4.40%
Competitor Reviews, Employee Reviews,
Ex-Employee Reviews, Review Clusters
Real Profiles with fake
reviews 2.30% 0.40%
Paid Reviews
Fake Profiles with real
reviews 1.10% 0.20%
Real First-Person Consumer Reviews
Real profiles with real
reviews 89.20% 95%

34. Google: Hi-Lo
• High category: Moving Cos – 22.3% (21.4%
Plumbers)
• High location: NYC – 12.5% (12.5% Phoenix)
• Low category: Pharmacies – 1.35% (2.4% Life
Insurance)
• Low location: Boston – 5.7% (8.3% Cleveland-Akron)
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

35. Yelp: Hi-Lo
• High category: Locksmiths – 14.4% (22.8% fraud in
Miami-Ft. Lauderdale)
• High location: LA – 10% (9.8% in Miami-Ft.
Lauderdale)
• Low category: Pharmacies – 0.8% (2.4%
Vocational/Technical Schools)
• Low location: Boston – 4.3% (5.6% Orlando, Dallas)
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

36. Trip: Hi-Lo
• High category: Motels – 12.9%
• High location: Miami-Ft. Lauderdale – 10.7% (6.9%
Chicago)
• Low category: Luxury Hotels – 4.3%
• Low location: Cleveland-Akron – 3.5% (3.8%
Philadelphia)
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

37. Facebook: Hi-Lo
• High category: Moving Cos – 9.2% (20.1% Denver)
• High location: Phoenix – 7.1%
• Low category: Pharmacies – 1.35% (2.2% Luxury
Hotels)
• Low location: Boston – 3.42%
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

38. Most Honest City in America: Boston
Only 3.7%
of reviews across all
four platforms were
suspect
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

39. Most Dishonest City in America: Miami
9.3%
of reviews across all
four platforms were
suspect
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

40. Category with Least Fraud: Pharmacies
Only 1.5%
of reviews across
three platforms
were suspect
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

41. Category with Most Fraud: Locksmiths
14.4%
of reviews across
three platforms were
suspect
Moving Cos. a close
second with 13.7%
Source: Ubrerall-Objection.co Fake Reviews Study (2021)

42. Self Policing: Automation and Manual Review
In 2020 blocked or removed:
55 million reviews and 3 million fake Business
Profiles (20 million fewer reviews than in 2019)
960,000 reviews and more than 300,000 Business
Profiles that were reported by Google Maps users.

43. Removing Review Spam
Must violate Google’s policies:
• Spam and fake content
• Off-topic
• Restricted content
• Illegal content
• Terrorist content
• Sexually explicit content
• Offensive content
• Dangerous & Derogatory Content
• Impersonation
• Conflict of Interest
What to do:
• Respond to the review
• Flag review as ‘inappropriate’ from GMB
dashboard
• Contact @GoogleMyBiz

44. Engage
Conversion rates – measured
by clicks on phone numbers,
driving directions, and website
links – climb sharply when
companies engage with and
reply to online reviews left by
customers.
Source: Uberall Reputation Revolution study 2019

45. Source: 1) Uberall consumer survey 2018, 2) ReviewTrackers consumer survey 2018
86%
are more likely to
shop at a store that
replies to reviews
45% say that they’re more likely to
visit a business if it responds to
negative reviews.
45

46. • Have active review management in place
• Collect reviews ethically
• Respond to reviews in a timely way (local
response rates are very low for brands)*
• Flag review spam/fraud but don’t do it
frivolously
• Rinse and repeat
What You Can Do
*89% of consumers open to changing critical review... If the business
addresses the complaint or problem
Source: LSA-SOCi (2018)

47. Thank you!
Questions?
greg.sterling@uberall.com