Fake online reviews: Literature review, synthesis, and directions for future research

Highlights

• The antecedent–consequence–intervention (ACI) conceptual framework is proposed.

• A review of fake online reviews is presented.

• Twenty future research questions are identified.

• Eighteen propositions are proposed to guide the future research.

• The publicly available datasets are compiled and summarized.

Abstract

Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.

Introduction

Online reviews form a critical and unavoidable facet of e-commerce. These reviews have a significant impact on consumers purchase decisions as well as the amount of money spent by consumers. As e-commerce grows, however, so does the prevalence of fake online reviews (from here on, “fake reviews”). The proportion of fake reviews ranges from 16% [1], 20% [2], and 25% [3] to 33.3% [4]. As early as 2012, approximately 10.3% of online products were subjected to review manipulation [5].

There also exist infamous cases that demonstrate the seriousness of fake reviews in e-commerce. For example, in 2012, the UK Advertising Standards Authority found TripAdvisor to be involved in creating fake reviews: approximately 50 million online reviews on its site could not be verified as trusted [6]. In 2013, Samsung was ordered to pay a fine of $340,000 by the Taiwan Federal Trade Commission for posting negative fake reviews against its competitor HTC [7]. In 2015, Amazon sued 1114 unidentified people for posting fake reviews [8]. In 2018, Mafengwo.com, a famous tourism platform in China, was involved in review fraud, which included activities such as duplicating online reviews from competitors; the platform subsequently admitted to the issue with fake reviews [9].

Both industry and academia have expended serious efforts in detecting fake reviews and penalizing its perpetrators in order to restrict the prevalence of fake reviews. Governments, for instance, continually attempt to perfect legal systems in response to a crises of fake reviews, often taking tremendous measures to supervise online sellers and platforms. In 2013, the Attorney General of the State of New York spearheaded “Operation Clean Turf,” a year-long undercover investigation to identify and expose firms that create fake reviews [10]. In 2018, China enacted the first “E-commerce Law.” The law stipulates that merchants cannot conduct false or misleading commercial promotions to defraud or mislead consumers with fictional transactions, by fabricating online reviews, or through any other means [11]. Industry professionals and scholars continue to develop algorithms to detect fake reviews to assist in this process [[12], [13], [14], [15], [16], [17], [18], [19], [20]].

However, the effect of reducing fake reviews is unclear, when only external efforts, such as the above, are considered. Developing computerized algorithms to identify fake reviews predominantly emphasizes “treatment” of the “symptom.” Such measures seldom grasp the underlying causes and mechanisms of fake reviews. Many fake reviews still exist regardless of algorithms that have high detection rates, as smart promulgators frequently post fake reviews endowed with new features that evade filter detection.

Fake reviews seriously affect the development of online product reviews and stakeholders. Because such reviews can have a significant effect on product perception, many vendors, retailers, and platforms often manipulate online reviews [[21], [22], [23]]. Online sellers tend to publish positive fake reviews for their products or negative fake reviews against competitors' products for financial gains [24]. Platforms are inclined to acquiesce to review manipulations and add fake reviews to increase traffic and consumer engagement [23]. Opportunity seeking is an example of why such individuals post fake reviews [4].

Fake reviews, compared with genuine online reviews, tend to more influential and complex in structure, impressing upon us the need to rigorously explore them. There is a need for a historiography of the research on fake reviews, which would offer insights into future research prospects. Despite recent and increasing research, scholars are yet to clearly define fake reviews. For example, what types of fake reviews have already been explored by the extant studies? Further, how do we conduct more interesting studies on fake reviews? Research on fake reviews is also limited by the available datasets—In fact, there is little information on public datasets that could be used to study fake reviews.

A systematic literature review of fake reviews is extremely limited. A rare review on false information does mention three types of false information—fake reviews in e-commerce, hoaxes on collaborative platforms, and fake news in social media. However, it does not singularly focus on fake reviews [25]. To the best of our knowledge, there exist nine relevant reviews on this topic [[12], [13], [14], [15], [16], [17], [18], [19], [20]]. These works, however, largely explore the detection of fake reviews, without an overall analysis of the antecedents and consequences of fake reviews. In the present study, we address this gap in the literature in order to guide future research.

The remaining paper is structured as follows. Section 2 presents the working definition of fake reviews, research design, and general quantitative findings of the reviewed literature. It also puts forth the analytical framework of this study, based on which we thoroughly investigate the existing literature to understand the antecedents and consequences of fake reviews in Section 3. Then, we identify future research questions and propose propositions in Section 4. Section 5 summarizes and analyzes the dataset resources, following by a conclusion in Section 6.