  Online consumer reviews reflect the testimonials of real people, unlike
advertisements. As such, they have critical impact on potential consumers, and
indirectly on businesses. According to a Harvard study (Luca 2011), +1 rise in
star-rating increases revenue by 5-9%. Problematically, such financial
incentives have created a market for spammers to fabricate reviews, to unjustly
promote or demote businesses, activities known as opinion spam (Jindal and Liu
2008). A vast majority of existing work on this problem have formulations based
on static review data, with respective techniques operating in an offline
fashion. Spam campaigns, however, are intended to make most impact during their
course. Abnormal events triggered by spammers' activities could be masked in
the load of future events, which static analysis would fail to identify. In
this work, we approach the opinion spam problem with a temporal formulation.
Specifically, we monitor a list of carefully selected indicative signals of
opinion spam over time and design efficient techniques to both detect and
characterize abnormal events in real-time. Experiments on datasets from two
different review sites show that our approach is fast, effective, and practical
to be deployed in real-world systems.
