Social media package: eWOM Biased Information

eWOM communication presents some biased information related to the purchase, the customer, and the company/third party organizations. The p...

eWOM communication presents some biased information related to the purchase, the customer, and the company/third party organizations. The problem of legitimacy and authenticity of reviews posted on commercial travel review websites is one of the key current challenges. The main reasons of biased messages can be identified.

First, the online feedbacks are generally written by consumers who purchase the product (purchasing bias) and hence display a favorable disposition toward a product (Hu et al. 2009).

Second, people who post a comment on the Net are generally extremely satisfied or extremely dissatisfied (Anderson 1998; Litvin et al. 2008) because consumers with a moderate satisfaction are not stimulated by the same motivation to report online their perceptions (under-reporting bias) (Hu et al. 2009).

social media package


Third, consumers rely on the rating (the average rating given by the grade of online reviews) because it is intuitive and easy to understand, especially in cases with a lot of information to be selected. Sometimes they operate a first selection of travel services considering only the rating and not the content and the distribution of the reviews (Hu et al. 2009, 2012). Due to the subjective nature of online recommendations, this could be misleading for the travelers because the score does not always express fully the quality of the service experienced, unlike the content of the reviews (Lappas 2012).7 We could name this bias “consumer bias”. However, some studies suggest that the receiver of the message (user of social media during the travel planning process) is generally conscious of these two biases (purchasing and under-reporting) and compensates by using additional information about online product reviews to form the quality perceptions and decide whether to purchase or not (Hu et al. 2009). In fact, according to Banerjee and Fudenberg (2004), consumers who search information online are smart and aware of the presence of selfselection bias in online product reviews.

A fourth source of user-generated biased information is the possibility of deliberate manipulation of online reviews (deceptive reviews, Banerjee and Chua 2014) thanks to possible decontextualization and anonymity.8 This may generate fake positive or negative reviews which could be posted by professionals (the company and the competitors) or by customers. Hu et al. (2012) define reviews manipulation as “vendors, publishers, writers, or any third-party consistently monitoring the online reviews and posting non-authentic online reviews on behalf of customers when needed, with the goal of boosting the sales of their products”. Along the same line Mukherjee et al. (2012) describe opinion spamming as the “human activities (e.g., writing fake reviews) that try to deliberately mislead readers by giving unfair reviews to some entities (e.g., products) in order to promote them or damage their reputation”.

Manipulators can be both customers and professionals. In the first case, hotel guests could try to extort hotels in order to obtain discounts or favorable services. In the second case, professional manipulators may be the service producer aiming to improve its reputation or a competitor who tries to damage the reputation of a rival firm. Recently, manipulating companies designate groups of spammers, i.e., groups of people who are paid to write fake positive or negative reviews about a target product. This can be particularly damaging because a group can take control of the “sentiment” on a certain product (Mukherjee et al. 2012).

The probability to consult fake reviews changes according to the website considered and the type of verifying policy. The anonymity of the message can increase ease of manipulation. There are two categories of online social media which allow people to spread word-of-mouth: peer networks (i.e., Facebook, Twitter, Linkedin, etc.) and anonymous review websites (i.e., TripAdvisor, Yelp) (Tiwari and Richards 2013). Peer networks have the advantage of a higher trust than anonymous review websites which, however, offer deeper knowledge, and different perspectives (Cheung and Lee 2012). Another frequent distinction in the travel sector is that between the well-known anonymous travel review websites (i.e., TripAdvisor, Yelp) and OTAs (like Expedia and Booking). Anyone can post a review on a travel review website, while some OTAs allow customers to post a review only following an actual booking. The different organization of these operators determines a higher volume of reviews with a possible higher percentage of fake comments in comparison with OTAs (Mayzlin et al. 2012). In fact, to book a hotel room on an OTA you have to insert the number of your credit card and this generally discourages manipulators. But OTAs too are sometimes affected by problems with fake reviews. First of all, some other OTAs such as Orbitz allow anyone to post a review. However, there is a major difference in comparison with anonymous travel review websites: reviews are checked and classified as “verified” if the customer has booked the hotel room on the website and “unverified” if no booking has occurred (Mayzlin et al. 2012). Moreover, we have to consider the opportunity to review a service for which we do not have paid, for example a dinner or the Spa service. After having booked a room in a hotel paying only for the night, the traveler can
actually review all the services of the hotel. But how is it possible to ascertain whether the customer had dinner at that hotel if this information is not included in the booking?

The detection of manipulated online reviews is a problem dealt with in various researches. Scholars have studied fake online reviews in different ways: detecting spam in collaborative settings (Mukheriee et al. 2012; Feng et al. 2012); exploring the impact of this manipulation on consumers and firms (Dellarocas 2006; O’Connor 2010); examining the market factors which can influence the propensity to engage in online manipulation (Mayzlin et al. 2012)10; understanding how customers respond to products when there is the suspect of manipulated reviews (Hu et al. 2012); and analyzing the attacker perspective, that is how to create a fake review that seems to be authentic (Lappas 2012). Many of these studies focus on the rating of the review while others prefer to concentrate on the text of the message to overcome what was termed above “consumer bias” (Hu et al. 2012; Lappas 2012).11 The focus on the content of the message rather than on the rating comes from the conviction that the average rating can fail in evaluating the quality of an item because it does not consider the numerous attributes involved in the process and present in the content of the message (a mix of comments characterized by a positive and negative polarity). Therefore, analyzing the writing style could be a way of detecting manipulated reviews (Banerjee and Chua 2014). According to Hu et al. (2012) authentic reviews are different from manipulated ones because they are random and express a personal view of the experience arising from the specific background of the reviewer (i.e., culture, education, occupation, etc.). In the case of spamming groups which monitor the rating of a certain product and then, when it increases/decreases, start writing to manipulate the result, the message cannot be random and the writing style will tend to use emotive (positive/negative) sentiment to influence customers’ choices.12 Semantic analyses of reviews are particularly useful for social media like Facebook or Foursquare where quality evaluation is expressed only through text messages and visual content. Nevertheless, other scholars believe that trying to decide which reviews are manipulated by means of a semantic analysis is particularly difficult and sometimes misleading; this is why they developed other methodologies. An interesting case is the contribution of Mayzlin et al. (2012) based on the comparison of the hotel reviews distribution and rating between two different websites (TripAdvisor and Expedia) exploiting the cited difference in organizational structure that should determine a different distribution of online reviews.

However, since consumers expect to find fake reviews as the volume and quality of user-generated content increases, they interpret and filter what they read and see considering also this bias. Awareness that readers will have this perception even in case of authentic reviews could lead companies to manipulate online reviews with the aim to compensate (Dellarocas 2006).

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