The measurement of readability: useful information for communicators
ACM Journal of Computer Documentation (JCD)
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
Promotional Chat on the Internet
Marketing Science
Show me the money!: deriving the pricing power of product features by mining consumer reviews
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Do online reviews matter? - An empirical investigation of panel data
Decision Support Systems
Fraud detection in online consumer reviews
Decision Support Systems
Manipulation in digital word-of-mouth: A reality check for book reviews
Decision Support Systems
IEEE Transactions on Knowledge and Data Engineering
More than words: Social networks' text mining for consumer brand sentiments
Expert Systems with Applications: An International Journal
βP: A novel approach to filter out malicious rating profiles from recommender systems
Decision Support Systems
Using social software for enhancing IS talents' e-learning motivation
Proceedings of the 2013 annual conference on Computers and people research
Semantic similarity measurement using historical google search patterns
Information Systems Frontiers
A study of manipulative and authentic negative reviews
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Discovering content-based behavioral roles in social networks
Decision Support Systems
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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.