Taking Fake Online Consumer Reviews Seriously

Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus

“…The preliminarily tested validity of one of the cues (i.e., identity-related disclosure) and the combination of this cue with two additional ones (i.e., consensus information and persuasion knowledge activation) in an experimental study reveal not only the general potential of two of those cues (Bambauer-Sachse and Mangold, 2013; Ghose and Ipeirotis 2011;Grazioli and Jarvenpaa 2000) but also the power of social proof (i.e., consensus information) and the amount of identity-related information about the reviewer in the trustworthiness assessment of a review's source by consumers. Second, the present study deepens the knowledge around the malicious practice of online marketplace deception in the marketing discipline (Boush, Friestad and Wright, 2009;Malbon, 2013) and, thus, complements related research in computer and information systems research (Bhattarai, Rus and Dasgupta, 2009;Ott et al, 2011). While the approaches in computer and information systems science are largely review-centric and should assist review site operators in updating and developing their filters for fake reviews, the present research is customer-centric and addresses information linked to the context in which the review is embedded (Luca and Zervas, 2014;Mayzlin, Dover and Chevalier 2014).…”

“…However, few studies have investigated the effects of eWOM on credibility and trust as perceived by the receiver (Chatterjee, 2001;Senecal and Nantel 2004;Tsang and Prendergast 2009). This wide neglect of trust-related factors is surprising given that trust was identified as a crucial driver of online interactions and transactions (Lee and Turban, 2001;Peppers and Rogers 2012) and should become even more salient due to the recent discussion of deceptive reviews (Malbon, 2013;Mayzlin, Dover and Chevalier 2014). In their commitment-trust theory, Morgan and Hunt (1994) highlight the importance of trust as a mediating variable.…”

Understanding online product ratings: A customer satisfaction model

“…They are created by or on behalf of manufacturers and retailers to increase the average ratings and, hence, the sales of their products. This effect spans a stream of research of its own (e.g., Malbon, 2013;Lappas et al, 2012;Mukherjee et al, 2012). We find no indications for the reverse effect, that is, fake reviews given to products by competitors in order to decrease their average rating.…”

Separation of Genuine and Fake Users From e-Commerce Site Using Hybrid Learning Approach

“…Through data mining and intelligent decision based on data, enterprises can foresee the potential request of customers so as to complete focused on-line marketing with the end goal of benefit growth. Because of some reasons, such as commercial competition and a time limit of the customer, a lot of product's spam reviews, in any case, have showed up in the product reviews, resulting in the ineffectiveness and the mistake of the intelligent decision making that was based on data analysis [1]. Table I shows some selected mobile phone reviews from the Amazon website.…”