Manipulation of online reviews: An analysis of ratings, readability, and sentiments

By Nan Hu, Indranil Bose, Noi Sian Koh, Lingbing Liu. Decision Support Systems, 2012

534 citations recorded by the source corpus.

As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the reader's discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we propose a simple statistical method to detect online reviews manipulation, and assess how consumers respond to products with manipulated reviews. In particular, the writing style of reviewers is examined, and the effectiveness of manipulation through ratings, sentiments, and readability is investigated. Our analysis examines textual information available in online reviews by combining sentiment mining techniques with readability assessments. We discover that around 10.3% of the products are subject to online reviews manipulation. In spite of the deliberate use of sentiments and ratings in manipulated products, consumers are only able to detect manipulation taking place through ratings, but not through sentiments. The findings from this research ensue a note of caution for all consumers that rely on online reviews of books for making purchases, and encourage them to delve deep into the book reviews without being deceived by fraudulent manipulation.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ce263750e003bc99af8f2028b1296519493159d1

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Citations in the corpus

Strategic and tactical price decisions in hotel revenue management (2016, background): “Despite fraudulent reviews from the same companies or competitors have been shown to pose a threat also for consumers’ reviews (Hu et al., 2012), the role of consumer generated reviews is considered trustable and independent (Mauri and Minazzi, 2013), and it is gaining importance when compared with…”
Predicting the “helpfulness” of online consumer reviews (2017, background): “Marketers take advantage of networks of experienced customers to influence the purchase behavior of potential buyers (Hu et al., 2012).”
Promotional Reviews: An Empirical Investigation of Online Review Manipulation (2014, methodology): “In particular, we examine the di erence in review distributions across Expedia and TripAdvisor, sites with di erent 17The reason behind the later data collection e ort is that Orbitz had relatively few reviews in October of 2011: 1) there were 104 hotels that had reviews at TripAdvisor and Expedia…”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “In work of Hu et al. (2012) expected that reviews written by customers would have random writing styles due to the random backgrounds of customers.”
Setting the future of digital and social media marketing research: Perspectives and research propositions (2020, background): “Scholars have investigated when and for what type of vendor fake reviews are more likely to appear (e.g. Hu et al., 2012), the role of credible information on consumer perceptions and behavior (e.g. Cheung et al., 2009) while other studies have focused on the impact of various elements (source,…”
External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews? (2014, background): “The articles found for manipulation are given here [24,25,36,80–87].”
Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews (2019, background): “The linguistic style of a review with a higher readability score usually implies that the writer is more educated (Hu et al., 2012).”
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics (2014, unknown): “One study failed to recognize manipulated reviews using sentiment mining but found that consumers are able to recognize them (Hu et al. 2012).”