Determining, measuring and testing quantitative signatures of deceptive behaviour

Given the frequent and often successful attempts at trafficking illegal items of high value across borders, many systems have been put in place (e.g. airport baggage screening) to detect such attempts. However, given the limitations of these current systems, this study investigated the prevalence of visible behavioural signatures to concealment that could be seen by a multiple camera set-up. Ten participants were asked to conceal a high value item of which they could keep if they could successfully lie to our “lie detector machine”. 13 cameras observing every visible area of each participant were used to collect over 500 videos for analysis of bodily movement. Each participant underwent a conceal condition and a separate baseline condition where they did not conceal any items. 1500+ quantitative measures of bodily movement, including response time, were performed. It was found that, in the conceal condition, response time to the critical question increased, hand movements decreased, blink rate increased, and the left foot was nearly always in front of the right. In conclusion it appears that, within our experiment, there do exist behavioural signatures for concealment that could be used in automated screening applications. Further work to address the limitations of this study including ecological validity will follow

Online Deception in Social Media

The unknown and the invisible exploit the unwary and the uninformed for illicit financial gain and reputation damage

Multiple Account Identity Deception Detection in Social Media Using Nonverbal Behavior

Identity deception has become an increasingly important issue in the social media environment. The case of blocked users initiating new accounts, often called sockpuppetry, is widely known and past efforts, which have attempted to detect such users, have been primarily based on verbal behavior (e.g., using profile data or lexic al features in text). Although these methods yield a high detection accuracy rate, they are computationally inefficient for the social media environment, which often involves databases with large volumes of data. To date, little attention has been paid to detecting online decep- tion using nonverbal behavior. We present a detection method based on nonverbal behavior for identity deception, which can be applied to many types of social media. Using Wikipedia as an experimental case, we demonstrate that our proposed method results in high detection accuracy over previous methods pro- posed while being computationally efficient for the social media environment. We also demonstrate the potential of nonverbal behavior data that exists in social media and how designers and developers can leverage such nonverbal information in detecting deception to safeguard their online communities

An evaluation of identity in online social networking: distinguishing fact from fiction

Online social networks are understood to replicate the real life connections between people. As the technology matures, more people are joining social networking communities such as MySpace (www.myspace.com) and Facebook (www.facebook.com). These online communities provide the opportunity for individuals to present themselves and maintain social interactions through their profiles. Such traces in profiles can be used as evidence in deciding the level of trust with which to imbue individuals in making access control decisions. However, online profiles have serious implications over the reality of identity disclosure. There are many reasons why someone may choose not to reveal their true self, which sometimes leads to misidentification or deception. On one hand, the structure of online profiles allows anonymity, which gives users the opportunity to create a persona that may not represent their true identity. On the other hand, we often play multiple identities in different contexts where such behaviour is acceptable. However, realizing the context for each identity representation depends on the individual. As a result, some represented identities will be essentially real, if edited for public view, some will be disguised, and others will be fictitious or humorous. The millions of social network profiles, and billions of connections between them, make it difficult to formalize an automated approach to differentiate fact from fiction in online self-described identities. How can we be sure with whom we are interacting, and whether these individuals or groups are being truthful with the online identities they present to the rest of the community? What tools and techniques can be used to gather, organize, and explore the available data for informing the level of honesty that should be entrusted to an individual? Can we verify the validity of the identity automatically, based on the available information online? We aim to evaluate identity representation online and examine how identity can be verified in a less trusted online community. We propose a personality classifier model to identify a user‟s personality (such as expressive, valid, active, positive, popular, sociable and traceable) using traces of 2.2 million profile features collected from MySpace. We use data mining techniques and social network analysis to extract significant patterns in the data and network structure, and improve the classifier during the cycle of development. We evaluate our classifier model on profiles with known identities such as „real‟ and „fake‟. Our results indicate that by utilizing people‟s online, self-reported information, personality, and their network of friends and interactions, we are able to provide evidence for validating the type of identity in a manner that is both accurate and scalable

Uncovering the Hidden Cognitive Processes and Underlying Dynamics of Deception

This dissertation examines the processing demands associated with motor responding and verbal statements during deceptive (or deceptive-like) behavior. In the first set of studies presented in Chapter 2, participants motor movements in a false response paradigm revealed signatures of competition with the truth. In a second set of studies presented in Chapter 3, deceptive participants used language that reflected cognitive and social demands inherent to various types of deception. In evaluating both motor and verbal cues, this dissertation provides a comprehensive, multi-modal approach to better understanding the cognitive processes underlying deception. in conducting the motor responding studies, participants\u27 arm movements were analyzed as they navigated a motor tracking device (computer-mouse, Nintendo Wiimote). To visually co-present response options, where the true option acts as a competitor to a false target. In an initial study, competition during deceptive responding was shown to be much greater than during truthful responding. In two follow-up studies, the introduction of various task-based cognitive demands was shown to systematically modulate response performance. Specifically, these studies suggest that an intention to false respond early in question presentation will amplify competition effects, and that false responding to information in autobiographical memory is much more difficult than responding to information in general semantic memory. In the studies analyzing verbal statements, the focus is turned to large-scale linguistic analyses using automated natural language processing tools. In the first study, changes in language use were identifed between deceptive and truthful narratives using six psychologically relevant categories. A major finding was that the language of deception is adapted to faciliate ease of cognitive processing. In a second study, the indicative phrasing and semantic content of deceptive texts was extracted using a contrastive corpus analysis, whereby indicative features are defined by frequent use in one corpus while being infrequent in a comparative corpus. Two contexts of deception were evaluated. In the first context of computer-mediated conversations, decievers used a range of unique thematic elements, as in avoiding personal involvement in their narrative accounts. In the second context of attitudes towards abortion, unique thematic elements once again emerged; for example, participants tended to position their arguments in terms of formal law

Detecting Deception Through Non-Verbal Behaviour

The security protocols used in airport security checkpoints primarily aim to detect prohibited items, as well as the detection of malicious intent and associated deception to thwart any threats. However, some of the security protocols that are used are not substantiated by scientifically validated cues of deception. Instead, some protocols, such as the Screening of Passengers by Observation Techniques (SPOT) program, have been developed based on anecdotal evidence and invalid cues of deception. As such, the use of these protocols has received a lot of criticism in recent years from government agencies, civil rights organisations and academia. These security protocols rely on security personnel’s ability to infer intent from non-verbal behaviour, yet the literature suggests that the relationship between non-verbal cues and deception is unreliable and that people are poor at detecting deception. To improve upon our understanding of the validity of these protocols, this thesis used virtual reality to replicate a security checkpoint to explore whether there were valid cues of deception, specifically in an airport context. People’s ability to identify whether others were behaving deceptively was assessed, as well as the factors that may be informing decision-making. Chapter Four of this thesis found that the non-verbal cues of interest, which were segment displacement, centre of mass displacement, cadence, step length and speed were not significantly different between honest and deceptive people. A verbal measure, response latency, was found to only distinguish between honest people and those who were deceptive about a future intention, but not those who were deceptive about having a prohibited item. In light of the use of non-verbal measures in practice despite the lack of scientific support, Chapters Five to Seven aimed to gain a greater insight into people’s deception detection capabilities. The findings from Chapters Five to Seven reflected that the ability to detect deception from non-verbal behaviour was no better than guessing. Specifically, Chapter Five found that the accuracy of detecting deception was no different from chance levels. Six themes emerged as the factors that were used to inform decision-making. The themes were physical appearance, disposition, walking behaviour, body positioning, looking behaviour and upper limb movement, though a qualitative analysis revealed that there were subjective interpretations of how the themes mapped onto deception. Chapter Six introduced two techniques of information reduction to assess whether accuracy could be improved above chance levels by lessening the impact of biasing factors. Neither technique resulted in accuracy above chance levels. In Chapter Seven, eye tracking was utilised to assess the gaze patterns associated with the detection of deception. People looked at the legs more than other areas of the body prior to decision-making, though only looking at the left arm and hand were linked with accuracy. Detection accuracy was poor overall, though looking at the left arm was linked with reduced accuracy, whilst looking at the left hand was linked with increased accuracy. Overall, this thesis showed that the non-verbal cues that were assessed could not distinguish between honest and deceptive people. In the absence of valid cues, observers were not able to identify deception at a rate above chance even with the reduction of potentially biasing factors. The results of this thesis reinforce the idea that incorporating nonverbal measures into threat/deception detection protocols may not be warranted because of the dubious nature of their reliability and validity, as well as the poor deception identification capabilities when relying on non-verbal behaviour