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Detecting Lies and Deceit: Pitfalls and Opportunities

01 Jan 2008-
TL;DR: Detecting Lies and Deceit as discussed by the authors provides an up-to-date account of deception research and discusses the working and efficacy of the most commonly used lie detection tools, including: •Behaviour Analysis Interview •Statement Validity Assessment •Reality Monitoring •Scientific Content Analysis •Several different polygraph tests •Voice Stress Analysis •Thermal Imaging •EEG-P300 •Functional Magnetic Resonance Imaging (fMRI)
Abstract: Detecting Lies and Deceit provides the most comprehensive review of deception to date. This revised edition provides an up-to-date account of deception research and discusses the working and efficacy of the most commonly used lie detection tools, including: •Behaviour Analysis Interview •Statement Validity Assessment •Reality Monitoring •Scientific Content Analysis •Several different polygraph tests •Voice Stress Analysis •Thermal Imaging •EEG-P300 •Functional Magnetic Resonance Imaging (fMRI) All three aspects of deception are covered: nonverbal cues, speech and written statement analysis and (neuro)physiological responses. The most common errors in lie detection are discussed and practical guidelines are provided to help professionals improve their lie detection skills. Detecting Lies and Deceit is a must-have resource for students, academics and professionals in psychology, criminology, policing and law.
Citations
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Book
01 May 2012
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Abstract: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

4,515 citations

Proceedings Article
19 Jun 2011
TL;DR: This work develops and compares three approaches to detecting deceptive opinion spam, and develops a classifier that is nearly 90% accurate on the authors' gold-standard opinion spam dataset, and reveals a relationship between deceptive opinions and imaginative writing.
Abstract: Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

1,083 citations


Cites background from "Detecting Lies and Deceit: Pitfalls..."

  • ...Furthermore, all three judges suffer from truth-bias (Vrij, 2008), a common finding in deception detection research in which human judges are more likely to classify an opinion as truthful than deceptive....

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  • ...We suspect that agreement among our human judges is so low precisely because humans are poor judges of deception (Vrij, 2008), and therefore they perform nearly at-chance respective to one another....

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  • ...Thus, for each of the 20 chosen hotels, we select 20 truthful reviews from a log-normal (lefttruncated at 150 characters) distribution fit to the lengths of the deceptive reviews.14 Combined with the 400 deceptive reviews gathered in Section 3.1 this yields our final dataset of 800 reviews....

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  • ...We observe that automated classifiers outperform human judges for every metric, except truthful recall where JUDGE 2 performs best.16 However, this is expected given that untrained humans often focus on unreliable cues to deception (Vrij, 2008)....

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Journal ArticleDOI
01 Sep 2016
TL;DR: This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.

531 citations

Journal ArticleDOI
TL;DR: This article argues that self-deception evolved to facilitate interpersonal deception by allowing people to avoid the cues to conscious deception that might reveal deceptive intent, and proposes that this is achieved through dissociations of mental processes, includingconscious versus unconscious memories, conscious versus unconscious attitudes, and automatic versus controlled processes.
Abstract: In this article we argue that self-deception evolved to facilitate interpersonal deception by allowing people to avoid the cues to conscious deception that might reveal deceptive intent. Self-deception has two additional advantages: It eliminates the costly cognitive load that is typically associated with deceiving, and it can minimize retribution if the deception is discovered. Beyond its role in specific acts of deception, self-deceptive self-enhancement also allows people to display more confidence than is warranted, which has a host of social advantages. The question then arises of how the self can be both deceiver and deceived. We propose that this is achieved through dissociations of mental processes, including conscious versus unconscious memories, conscious versus unconscious attitudes, and automatic versus controlled processes. Given the variety of methods for deceiving others, it should come as no surprise that self-deception manifests itself in a number of different psychological processes, and we discuss various types of self-deception. We then discuss the interpersonal versus intrapersonal nature of self-deception before considering the levels of consciousness at which the self can be deceived. Finally, we contrast our evolutionary approach to self-deception with current theories and debates in psychology and consider some of the costs associated with self-deception.

496 citations

Posted Content
TL;DR: This article developed and compared three approaches to detecting deceptive opinion spam, and ultimately developed a classifier that is nearly 90% accurate on the gold-standard opinion spam dataset. And they also made several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.
Abstract: Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

486 citations