Opinion spamming in social media is an activity of
people giving or sharing fake reviews or irrelevant opinions to online communities. The fake reviews are not merely misguided sentiment analysis and opinion mining system, but also severely affected online communities’ decision and businesses reputation.Thus, opinion spamming detection (OSD) technique is needed to enhance an opinion mining system and prevent such cases from happening to the online communities.This study was conducted using the systematic literature review (SLR) procedure to classify known opinion spam features in social media platforms, and to reveal types of social media platforms that are being addressed by OSD’s researchers.The result is, we found that, spatial and temporal factors in reviewer feature type is a current issue and is important to be solved because of spammer always changing their spamming strategy.On the other hand, most of the studies leveraged ngram character and part-of-speech approaches in a
review feature type because of its significant
improved OSD’s accuracy. Furthermore, we found
that, most of the studies focused on trading and
marketing-based social media platform, in which a
lack of OSD’s study in other forms of social media
platforms i.e. social networking and user generated content sites
