A Unified Model for Unsupervised Opinion Spamming Detection Incorporating Text Generality

Unlike other forms of spamming, it is difficult to collect a large amount of gold-standard labels for reviews Many existing methods on review spam detection by means of manual effort. Thus, most of these methods considering text content merely utilize simple text [Mukherjee et al., 2013; Li et al., 2013a; Sun et al., features such as content similarity. We explore a 2013] just rely on the ad-hoc or pseudo fake or non-fake novel idea of exploiting text generality for improving labels for model training, such as the labels annotated by spam detection. Besides, apart from the task the Amazon anonymous online workers [Ott et al., 2011; of review spam detection, although there have also Li et al., 2014]. On the other hand, some unsupervised been some works on identifying the review spammers methods have been proposed to detect the individual review (users) and the manipulated offerings (items), spammer [Mukherjee et al., 2013; Lim et al., 2010; no previous works have attempted to solve these Wang et al., 2011] and review spammer groups [Mukherjee et three tasks in a unified model. We have proposed al., 2012]. In addition, time series pattern [Xie et al., 2012], a unified probabilistic graphical model to detect rating distribution [Feng et al., 2012], reviewer graph [Wang et the suspicious review spams, the review spammers al., 2011], and reviewing burstiness [Fei et al., 2013] have also and the manipulated offerings in an unsupervised been applied to identify the review spams in an unsupervised manner.