View article

Recently, product/ service reviews and online businesses have been similar to the blood–heart relationship as they greatly impact customers’ purchase decisions. There is an increasing incentive to manipulate reviews, mostly profit-motivated, as positive reviews imply high purchases and vice versa. Therefore, a suitable fake review detection approach is paramount in ensuring fair e-business competition and sustainability. Most existing methods mainly utilize discrete review features such as text similarity, rating deviation, review content, product information, the semantic meaning of reviews, and reviewer behaviors. In the matter of discourse, some recent researchers attempted multi-feature (review- and reviewer-centric features) integration. However, such approaches face two issues: (1) Review representation is extracted in an independent manner, thus ignoring correlations between them (2) Lack of a unified …