|
Title:
|
FAKE REVIEWS DETECTION BASED ON BOTH THE REVIEW AND THE REVIEWER FEATURES UNDER BELIEF FUNCTION THEORY |
|
Author(s):
|
Malika Ben Khalifa, Zied Elouedi and Eric Lefèvre |
|
ISBN:
|
978-989-8533-95-1 |
|
Editors:
|
Hans Weghorn |
|
Year:
|
2019 |
|
Edition:
|
Single |
|
Keywords:
|
e-Commerce, Online Reviews, Spammers, Fake Reviews, Uncertainty, Belief Function Theory |
|
Type:
|
Full Paper |
|
First Page:
|
123 |
|
Last Page:
|
130 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
The online reviews play an increasingly spreading role in consumer purchasing decisions and they are also considered as
one of the most powerful source of information for companies. Due to this attraction, manufacturers and retailers rely on
spammers to promote their own products and demote the competitorsÂ’ one by posting fake reviews. Therefore, it is essential
to detect deceptive reviews in order to ensure customers confidence and to maintain companies' fair competition. To tackle
this problem, we propose a new approach able to spot spam reviews relying both on the rating reviews and the different
spammers' indicators under the belief function framework. This method treats uncertainty in the given reviews also in the
reviewers' information to take into account each reviewer spamicity when making decision. Experiments are conducted on
two real-world review data-sets from Yelp.com with filtered (spam) and recommended (non-spam) reviews to demonstrate
our method the effectiveness. |
|
|
|
|
|
|
