Consumers' purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam fictitious reviews that have been deliberately written to sound authentic, to deceive the readers. Existing approaches mainly focus on developing automatic supervised learning based methods to help users identify deceptive opinion spams. This work, we used the LSI and Sprinkled LSI technique to reduce the dimension for deception detection. We make our contribution to demonstrate what LSI is capturing in latent semantic space and reveal how deceptive opinions can be recognized automatically from truthful opinions. Finally, we proposed a voting scheme which integrates different approaches to further improve the classification performance.
In addition to exploring text or linguistic features in deception, some existing work looks into customers' behavior to identify deception @cite_2 . For example, delved into group behavior to identify group of reviewers who work collaboratively to write fake reviews.
@cite_2
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Most previous studies in computerized deception detection have relied only on shallow lexico-syntactic patterns. This paper investigates syntactic stylometry for deception detection, adding a somewhat unconventional angle to prior literature. Over four different datasets spanning from the product review to the essay domain, we demonstrate that features driven from Context Free Grammar (CFG) parse trees consistently improve the detection performance over several baselines that are based only on shallow lexico-syntactic features. Our results improve the best published result on the hotel review data (, 2011) reaching 91.2 accuracy with 14 error reduction. A growing field in computer applications is the use of algorithms to spot the lie. The most promising area within this field is the analysis of the language of the liar since speakers effectively control only the meaning they wish to convey, but not the linguistic style of the communication. With the advent of computational means to analyze language, we now have the ability to recognize differences in the way speakers phrase their lies as opposed to their truths. The main goal of this book is to cover the advances of the last 10 years in automatically discriminating truths from lies. To give the reader a grounding in deception studies, it describes a range of behaviors (physiological, gestural as well as verbal) that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and real-world data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, fraud, and false product reviews. Having established the background, the book concentrates on computational analyses of deceptive verbal behavior, which have enabled the field of deception studies to move from individual cues to overall differences in behavior. The book concludes with a set of open questions that the computational work has generated. The widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts using a machine learning approach. First, we collect a new open domain deception dataset also containing demographic data such as gender and age. Second, we extract feature sets including n-grams, shallow and deep syntactic features, semantic features, and syntactic complexity and readability metrics. Third, we build classifiers that aim to predict deception, gender, and age. Our findings show that while deception detection can be performed in short texts even in the absence of a predetermined domain, gender and age prediction in deceptive texts is a challenging task. We further explore the linguistic differences in deceptive content that relate to deceivers gender and age and find evidence that both age and gender play an important role in people’s word choices when fabricating lies. Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In this paper, we explore the use of multimodal real-life data for the task of deception detection. We develop a new deception dataset consisting of videos from reallife scenarios, and build deception tools relying on verbal and nonverbal features. We achieve classification accuracies in the range of 77-82 when using a model that extracts and fuses features from the linguistic and visual modalities. We show that these results outperform the human capability of identifying deceit.