A Proposed Solution for Identifying Online Fake Reviews in the Research Process

A Proposed Solution for Identifying Online Fake Reviews in the Research Process

Victor-Alexandru Briciu (Transilvania University of Brasov, Romania), Cristian-Laurențiu Roman (Transilvania University of Brasov, Romania), and Arabela Briciu (Transilvania University of Brasov, Romania)
DOI: 10.4018/978-1-7998-8061-5.ch010
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This chapter aims to present the issue of manipulation of online reviews, behind which there is always an interest, whether it is about increasing sales, promoting a product, degrading the image of a competing brand or product. Such reviews can influence the purchase decision or the sales of a company. Combining users' text with their behavior has yielded the best results in identifying fake reviews, and this remains probably the most effective method to date. The chapter proposes, as a novelty factor, a methodological solution before analyzing reviews through specialized software (e.g., SmartMunk, Revuze, Aspectiva, SentiGeek, etc.), a filter for identifying fake reviews by introducing them into a fake review application called Fakespot. Moreover, the idea that these false reviews can influence the purchase decision of customers in any field is emphasized, so it is very important that large companies develop programs or systems that detect them.
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Web 2.0 is the platform that underlies the concepts we will discuss in this chapter and represents the second version of the Internet, the one that appeared after the 2000s, the one in which the possibilities to browse online have become much more diverse, websites and applications have grown a lot, and people have started to keep in touch more and more in the online environment. At the same time, companies have also started to concentrate a large part of their resources for promotion in the online environment, and marketing strategies have also developed greatly.

Web 2.0 consists of all “online tools, applications and approaches, such as blogs, social networking sites, online communities and customer review sites” (Constantinides & Holleschovsky, 2016, p. 271); in addition, the Internet has started “facilitating the ‘social’ customer electronic word of mouth (eWOM) and a major source of customer information and empowerment” (Constantinides & Holleschovsky, 2016, p. 271), which has made reviews become an important source of information.

Another definition states that Web 2.0 is “a collection of open-source, interactive and user controlled online applications expanding the experiences, knowledge and market power of the users as participants in business and social processes” (Constantinides & Fountain, 2008, p. 232). The authors also discuss the importance of Web 2.0 for marketing and business in general. For brands, there are many possibilities to stay closer to people, learning “about the needs and opinions of their customers as well as interacting with them in a direct and personalised way” (Constantinides & Fountain, 2008, p. 233).

There are many differences between Web 1.0 and 2.0 (Briciu & Briciu, 2019), but it is said that one of the most important ones is that “content creators were few in Web 1.0 with the vast majority of users simply acting as consumers of content, while any participant can be a content creator in Web 2.0 and numerous technological aids have been created to maximize the potential for content creation” (Cormode & Krishnamurthy, 2008, p. 2). With the advent of Web 2.0 (Briciu & Briciu, 2021), many users have learned to create content, and this is how vlogging appeared, a phenomenon that has grown today, as well as Internet advertising, online stores, but also social networks (Briciu & Briciu, 2020), on which people have become dependent. Also, the Internet “is present in all areas of human life and activity” (Czerwinska, 2020, p. 77) and takes up more and more people’s time, the most important effect being that “is displacing traditional media such as television, radio and newspapers” (Czerwinska, 2020, p. 77).

Another big difference is that Web 2.0 addresses users differently, most sites “encourage users to spend as much time as possible on their site” (Cormode & Krishnamurthy, 2008, p. 7). The interest of the sites is to have as much traffic as possible, as they post quite a few ads from which they earn significant amounts of money. Instead, Web 1.0 was much more limited, and fewer users visited the sites, their main disadvantage being that they used to “tend to cover a single topic and do not require users to log in to access them” (Cormode & Krishnamurthy, 2008, p.7).

Six dimensions seem to underlie the concept, referring to “Individual production and User Generated Content, Harness the power of the crowd, Data on an epic sale, Architecture of Participation, Network Effects, Openness” (Anderson, 2007, p. 14). In short, even before the advent of Web 2.0, thoughts were directed towards user-created content and sales through the Internet, and the goals were met, these two dimensions being of great importance today.

Key Terms in this Chapter

Generator: An advanced tool who can post hundreds of credible reviews, very difficult to detect even with special programs.

Fakespot: Software that analyze the reviews from some important sites from the world, it can detect fake reviews by analyzing the words used, the user profile and another important element: the appearance of the check mark “Verified Purchase”.

Self-Approval: The need to confirm a good purchase by sharing an experience with other users, this for feel at peace with the decision made.

Manipulate: Editing the reviews by the companies to create an advantage and to convince potential customers that their product is the best.

Rating: Users who write good reviews can be appreciated with 4 or 5 stars and others can be charged with fewer stars, in time the users with a bad rating will not be taken into account.

Non-Authentic: The reviews that are posted to influence the users, those who post hide usually behind fake profiles.

Credibility: The way a review is written, the feelings transmitted are very important as well as detection the intention to influence decision.

Sentiments: Stories or experiences exposed by users on the Internet, usually in connection with a product.

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