Open Science Research Excellence
@article{(International Science Index):,
  title    = {A Framework for Review Spam Detection Research},
  author    = {Mohammadali Tavakoli and  Atefeh Heydari and  Zuriati Ismail and  Naomie Salim},
  country   = {Malaysia},
  institution={Universiti Teknologi Malaysia},
  abstract  = {With the increasing number of people reviewing
products online in recent years, opinion sharing websites has become
the most important source of customers’ opinions. Unfortunately,
spammers generate and post fake reviews in order to promote or
demote brands and mislead potential customers. These are notably
destructive not only for potential customers, but also for business
holders and manufacturers. However, research in this area is not
adequate, and many critical problems related to spam detection have
not been solved to date. To provide green researchers in the domain
with a great aid, in this paper, we have attempted to create a highquality
framework to make a clear vision on review spam-detection
methods. In addition, this report contains a comprehensive collection
of detection metrics used in proposed spam-detection approaches.
These metrics are extremely applicable for developing novel
detection methods.
  {International Journal of Computer and Information Engineering },  volume    = {10},
  number    = {1},
  year      = {2016},
  pages     = {67 - 71},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 109, 2016},