Facebook reviews contain reviews and reviewers' information and include a set of likes, comments, sharing, and reactions called Facebook Behaviors (FBs). We extend existing research on review helpfulness to fit Facebook reviews by demonstrating that Facebook behaviors can impact review helpfulness. This study proposes a theoretical model that explains reviews' helpfulness based on FBs and baseline features. The model is empirically validated using a real Facebook data set and different feature selection methods (FS) to determine the importance level of such features to maximize the helpfulness prediction. Consequently, a combination of the impactful features is identified based on a robust and effective model. In this context, the like and love behaviors deliver the best predictive performance. Furthermore, we employ different classification techniques and a set of influencer features. The results showed the performance of the proposed model by 0.925 of accuracy.The outcomes of the current study can be applied to develop a smart review ranking system for Facebook product pages.