Abstract: Seeking and sharing knowledge on online forums
have made them popular in recent years. Although online forums are
valuable sources of information, due to variety of sources of
messages, retrieving reliable threads with high quality content is an
issue. Majority of the existing information retrieval systems ignore
the quality of retrieved documents, particularly, in the field of thread
retrieval. In this research, we present an approach that employs
various quality features in order to investigate the quality of retrieved
threads. Different aspects of content quality, including completeness,
comprehensiveness, and politeness, are assessed using these features,
which lead to finding not only textual, but also conceptual relevant
threads for a user query within a forum. To analyse the influence of
the features, we used an adopted version of voting model thread
search as a retrieval system. We equipped it with each feature solely
and also various combinations of features in turn during multiple
runs. The results show that incorporating the quality features
enhances the effectiveness of the utilised retrieval system
significantly.
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.