TY - JOUR
T1 - The impact of fake reviews on online visibility
T2 - A vulnerability assessment of the hotel industry
AU - Lappas, Theodoros
AU - Sabnis, Gaurav
AU - Valkanas, Georgios
N1 - Publisher Copyright:
© 2016 INFORMS.
PY - 2016
Y1 - 2016
N2 - Extant research has focused on the detection of fake reviews on online review platforms, motivated by the well-documented impact of customer reviews on the users' purchase decisions. The problem is typically approached from the perspective of protecting the credibility of review platforms, as well as the reputation and revenue of the reviewed firms. However, there is little examination of the vulnerability of individual businesses to fake review attacks. This study focuses on formalizing the visibility of a business to the customer base and on evaluating its vulnerability to fake review attacks. We operationalize visibility as a function of the features that a business can cover and its position in the platform's review-based ranking. Using data from over 2.3 million reviews of 4,709 hotels from 17 cities, we study how visibility can be impacted by different attack strategies. We find that even limited injections of fake reviews can have a significant effect and explore the factors that contribute to this vulnerable state. Specifically, we find that, in certain markets, 50 fake reviews are sufficient for an attacker to surpass any of its competitors in terms of visibility. We also compare the strategy of self-injecting positive reviews with that of injecting competitors with negative reviews and find that each approach can be as much as 40% more effective than the other across different settings. We empirically explore response strategies for an attacked hotel, ranging from the enhancement of its own features to detecting and disputing fake reviews. In general, our measure of visibility and our modeling approach regarding attack and response strategies shed light on how businesses that are targeted by fake reviews can detect and tackle such attacks.
AB - Extant research has focused on the detection of fake reviews on online review platforms, motivated by the well-documented impact of customer reviews on the users' purchase decisions. The problem is typically approached from the perspective of protecting the credibility of review platforms, as well as the reputation and revenue of the reviewed firms. However, there is little examination of the vulnerability of individual businesses to fake review attacks. This study focuses on formalizing the visibility of a business to the customer base and on evaluating its vulnerability to fake review attacks. We operationalize visibility as a function of the features that a business can cover and its position in the platform's review-based ranking. Using data from over 2.3 million reviews of 4,709 hotels from 17 cities, we study how visibility can be impacted by different attack strategies. We find that even limited injections of fake reviews can have a significant effect and explore the factors that contribute to this vulnerable state. Specifically, we find that, in certain markets, 50 fake reviews are sufficient for an attacker to surpass any of its competitors in terms of visibility. We also compare the strategy of self-injecting positive reviews with that of injecting competitors with negative reviews and find that each approach can be as much as 40% more effective than the other across different settings. We empirically explore response strategies for an attacked hotel, ranging from the enhancement of its own features to detecting and disputing fake reviews. In general, our measure of visibility and our modeling approach regarding attack and response strategies shed light on how businesses that are targeted by fake reviews can detect and tackle such attacks.
KW - Customer reviews
KW - Decision support systems
KW - Fake reviews
KW - Knowledge management
KW - Vulnerability assessment
UR - http://www.scopus.com/inward/record.url?scp=85010304113&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010304113&partnerID=8YFLogxK
U2 - 10.1287/isre.2016.0674
DO - 10.1287/isre.2016.0674
M3 - Article
AN - SCOPUS:85010304113
SN - 1047-7047
VL - 27
SP - 940
EP - 961
JO - Information Systems Research
JF - Information Systems Research
IS - 4
ER -