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ABSTRACT
Online review systems play an important role in affecting consumers’ behaviors and
decision making, attracting many spammers to insert fake reviews to manipulate review
content and ratings. To increase utility and improve user experience, some online review
systems allow users to form social relationships between each other and encourage their
interactions. Here, we aim at providing an efficient and effective method to identify
review spammers by incorporating social relations based on two assumptions that people
are more likely to consider reviews from those connected with them as trustworthy.
Review spammers are less likely to maintain a large relationship network with normal
users. The contributionsare two-fold:
We elaborate how social relationships can be incorporated into review rating prediction
and propose a trust-based rating prediction model using proximity as trust weight.
We design a trust-aware detection model based on rating variance which iteratively
calculates user-specific overall trustworthiness scores as the indicator.
Experiments on the dataset collected from Yelp.com show that the proposed trust-based
prediction achieves a higher accuracy than standard CF method, and there exists a strong
correlation between social relationships and the overall trustworthiness scores.
Keywords:
Machine Learning; Spam Detection; Scalability; Twitter
INTRODUCTION
What Is A Social Network?
Wikipedia defines a social network service as a service which “focuses on the building and
verifying of online social networks for communities of people who share interests and activities,
or who are interested in exploring the interests and activities of others, and which necessitates
the use of software.”
A report published by OCLC provides the following definition of social networking sites: “Web
sites primarily designed to facilitate interaction between users who share interests, attitudes
and activities, such as Facebook, Mixi and MySpace.”
What Can Social Networks Be Used For?
Social networks can provide a range of benefits to members of an organization:
Support for learning: Social networks can enhance informal learning and support social
connections within groups of learners and with those involved in the support of learning.
Support for members of an organization: Social networks can potentially be used my all
members of an organization, and not just those involved in working with students. Social
networks can help the development of communities of practice.
