Identification of Advertising and Rebate Fake Positive Reviews:A Model Framework with Both Emotional and Text Features
Online product reviews have become an important reference for online purchases.However,this value also triggers businesses to intentionally create fake positive reviews to mislead consumers'purchasing decisions.As social e-commerce extends to all aspects of life,fake positive reviews have also shown character-istics of scale and industrialization,seriously restricting the healthy development of the digital economy.There are currently two types of fake positive reviews:"advertising fake reviews"written by spe-cialized swiping companies hired by merchants for advertising pro-motion,and the other is"rebate fake reviews"written by consumers according to merchant requirements to obtain rebates.The extant research on identifying fake positive reviews mainly focuses on advertising fake positive reviews.However,due to the concealment expression of rebate fake positive reviews,existing methods have limitations in identifying the two types of fake positive reviews.Therefore,it is important to study the identification methods of the two types of fake positive reviews to establish a good online busi-ness environment.This paper constructed a model framework that combines emotional and text features to identify two types of fake positive reviews and authentic ones.First,labeling rules were extracted based on the practice of review writers,and Chinese datasets were established for two types of fake positive reviews and authentic ones.Afterward,the PAD(Pleasant-Arousal-Dominance)emotion theory was intro-duced to deconstruct the emotions contained in reviews,combined with the emotional intensification modifier to construct the emo-tional features of reviews.Meanwhile,n-gram segmentation and TF-IDF were utilized to vectorize reviews to construct text features.Then,eighteen mainstream classification algorithms were used to construct a recognition model for multi-class positive reviews.The research results found significant differences in the distribution of the emotional intensification modifier effects and the three dimen-sions of PAD between two types of fake positive reviews and au-thentic ones.Introducing multidimensional emotional features can significantly improve classification algorithms'ability to identify three types of positive reviews,especially the highly concealed re-bate fake positive reviews.The influence of the emotional intensifi-cation modifier helps to improve the classification algorithm's abili-ty to distinguish positive reviews with extreme emotional intensity,while the three dimensions of the PAD theory makes classification algorithms capable of distinguishing three types of positive reviews with various degrees of emotional intensity.This paper extends previous literature from the following aspects.First,it introduces rebate fake positive reviews,expands the types of objects for research on fake positive reviews,and proposes a la-beling method close to the real transaction environment,providing a data basis for in-depth research on identifying fake reviews.Second,it introduces the PAD emotion theory to expand the emotional eval-uation of reviews from the positive-negative dimension to include arousal-non-arousal and active-passive dimensions and examines the impact of the emotional intensification modifier,improving the three-dimensional level of emotional modeling in reviews.Third,it constructs a fake positive review evaluation model that includes both text and emotional features,which exhibits the characteristic of not relying on specific classification algorithms in identifying three types of positive reviews,helping to establish a filtering mechanism for identifying different fake positive reviews.Fourth,it reveals the unique value of emotional and text features of reviews in identifying two types of fake positive reviews and authentic ones.The emotional evaluation and recognition model for multiple types of positive reviews presented in this paper has outstanding practical significance.First,e-commerce platform operators can enhance the identification ability of existing review filtering mechanisms for different types of fake positive reviews based on the fake positive review identification framework proposed in this paper.Second,consumers can preliminarily identify the type of positive reviews and consequently filter out products and merchants based on the multidimensional emotional characteristics reflected in the reviews.Third,the framework of the fake review recognition method pro-posed in this paper can also be extended to other scenarios of fake information recognition using text as a carrier.