Credible Review Detection with Limited Information Using Consistency Features.

Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers’ purchasing decisions. However, the proliferation of non-credible reviews — either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased — entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users — which might not be readily available in several domains — that provide only limited interpretability as to why a review is deemed non-credible.