9 Detecting Deceptive Chat-Based Communication ...

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Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues DOUGLAS C. DERRICK, University of Nebraska at Omaha THOMAS O. MESERVY and JEFFREY L. JENKINS, Brigham Young University JUDEE K. BURGOON and JAY F. NUNAMAKER, JR., University of Arizona

Computer-mediated deception is prevalent and may have serious consequences for individuals, organizations, and society. This article investigates several metrics as predictors of deception in synchronous chatbased environments, where participants must often spontaneously formulate deceptive responses. Based on cognitive load theory, we hypothesize that deception influences response time, word count, lexical diversity, and the number of times a chat message is edited. Using a custom chatbot to conduct interviews in an experiment, we collected 1,572 deceitful and 1,590 truthful chat-based responses. The results of the experiment confirm that deception is positively correlated with response time and the number of edits and negatively correlated to word count. Contrary to our prediction, we found that deception is not significantly correlated with lexical diversity. Furthermore, the age of the participant moderates the influence of deception on response time. Our results have implications for understanding deceit in chat-based communication and building deception-detection decision aids in chat-based systems. Categories and Subject Descriptors: H.4.2 [Information Systems Applications]: Types of Systems General Terms: Experimentation, Verification Additional Key Words and Phrases: Decision support system, deception detection, chat, typing bahavior ACM Reference Format: Derrick, D. C., Meservy, T. O., Jenkins, J. L., Burgoon, J. K., and Nunamaker Jr., J. F. 2013. Detecting deceptive chat-based communication using typing behavior and message cues. ACM Trans. Manage. Inf. Syst. 4, 2, Article 9 (August 2013), 21 pages. DOI:http://dx.doi.org/10.1145/2499962.2499967

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1. INTRODUCTION

Deception in computer-mediated chat is becoming more prevalent and has real consequences for individuals, employers, and society [Zhou and Zhang 2007]. Several recent studies and events have clearly shown the impact of online deception. These include US Congressional action against online predators [Fitzpatrick 2006], online dating studies [Epstein 2007], and analysis of terrorist networks [Chen and Wang 2005]. In

Portions of this research were supported by funding from the Center for Identification Technology Research (National Science Foundation Grant # 1068026) and the US Department of Homeland Security (2008-ST061-BS0002). The views, opinions, and/or findings in this report are those of the authors and should not be construed as an official NSF or US government position, policy, or decision. Authors’ addresses: D. C. Derrick (corresponding author), University of Nebraska at Omaha, PKI 280C, 1110 S. 67th Street, Omaha, NE 68182-0116; email: [email protected]; T. O. Meservy; email: [email protected]; J. L. Jenkins; email: [email protected]; J. K. Burgoon; email: [email protected]; J. F. Nunamaker, Jr.; email: [email protected]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or [email protected]. c 2013 ACM 2158-656X/2013/08-ART9 $15.00  DOI:http://dx.doi.org/10.1145/2499962.2499967 ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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a dramatic example, Megan Meier was a thirteen-year-old girl who took her own life after being deceived by a series of chat-based messages and email communications. A fictitious boy (actually an adult woman) developed an online relationship with the girl and then turned hostile. Megan ended her life in October 2006 not knowing of the deceit [ABC News 2007]. Computer-mediated chat is a very fertile ground for deception because people can easily conceal their identities and computer-mediated messages often appear credible [Zhou and Zhang 2007]. Deception in computer-mediated chat refers to deceit that occurs in synchronous text-based communication conducted over a computer network. As with most forms of synchronous communication, deceptive messages must often be formulated on the spot [Whitty et al. 2012]. Such deceptive communication is very common in organizational and individual settings [Zhou and Zhang 2007] and can include applications such as instant messaging, online forums, chat rooms, virtual worlds, live customer service sites, product review sites, and social networking sites (e.g., Lau et al. [2012] and Twitchell et al. [2005]). Compounding the problem is the fact that a large and consistent body of research shows that without special training or technological aid, human deception-detection accuracy is near chance (e.g., Jensen et al. [2011]). A meta-analysis summary of over 200 experiments shows that people are, on average, correct 54% of the time at distinguishing truths from lies [Aamodt and Custer 2006; ´ Bond and DePaulo 2006]. This level of accuracy is statistically greater than chance but is extremely poor in a practical sense. Further, specific individual differences such as age, professional experience, education, cognitive ability, and social skills have little impact and there is little variance overall in a person’s ability to detect deception ´ according to a substantial meta-analysis [Bond and DePaulo 2006], although a few individual studies suggest that training/experience can make a difference (e.g., Smith [2001]). In a seminal study on synchronous computer-mediated communication, Zhou and Zhang [2007] identify two channels for detecting deception in computer-mediated chat—messaging and typing. Messaging refers to the content and characteristics of the sequential messages displayed on the screen in a chat application; typing refers to the deceiver’s behavior while typing the message. These two categories were derived from interpersonal deception theory [Buller and Burgoon 1996; Zhou 2005], which posits that cues of deception may be categorized as verbal or nonverbal in interpersonal communication. Several cues of verbal interpersonal communication (messaging cues) may be relevant to computer-mediated chat such as lexical diversity, word count, and message content [Zhou et al. 2004]. These cues are important because empirical evidence suggests that deceivers may use language differently than truth tellers [Buller et al. 1994; Newman et al. 2003]. Deceivers may also demonstrate less language diversity, less complexity, and use more negative words [Newman et al. 2003]. Numerous linguistic features may be used to gauge the credibility of messages. For example, average word lengths may be used to judge language complexity, and linguistic features may be used to uncover defensive speech routines [Kuo and Yin 2011]. These cues are additionally relevant because they are used with synchronous and asynchronous persistent conversations (e.g., chat, email). Additionally, messaging cues that use criteria-based content analysis (CBCA) [Vrij 2005] or reality monitoring [Johnson and Raye 1981] analyze the content of speech, rather than the form, in terms of message length or count. CBCA is a widely used veracity assessment technique for discriminating between true and fabricated events through systematic analysis of witnesses’ spoken accounts [Vrij 2005]. Reality monitoring is a technique for detecting deception based on the postulate that imagined memories involve more information about the cognitive process that produced them than true memories [Johnson et al. 1981]. In this type of assessment, attention is given to types of terms such as spatial terms (or ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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their absence) and particular words such as modifiers or hedges (e.g., maybe, possibly, might, could) [Vrij 2000]. All of these studies focus on the cues of the message itself. Conversely, important interpersonal non-verbal cues of deception such as proxemics (distance) and kinesics (body movement) [Burgoon et al. 2002] are not applicable to text-based communication. Computer-mediated chat, however, does offer other unique and unexplored paralanguage cues relating to people’s typing behavior that can be captured, analyzed, and measured. These cues can include the number of edits, deletions, and response time and can provide valuable insights into meaning, emotion, and intent [Zhou and Zhang 2007]. In interpersonal, face-to-face communication, “how something is said” is often as important as the message itself. Message construction is an important part of the communication process, and cues of deception may be manifested in this process. In face-to-face interactions, cues related to message construction may be ´ manifested in response latency, vocal changes, and other nonverbal behavior [DePaulo et al. 2003; Sporer and Schwandt 2006]. So, it holds that message construction behavior (i.e., typing behavior) in computer-mediated communication may provide insights into deception. In other words, message creation is an important part of the deception process that can be analyzed independent of the actual message. As an example of this, unaided chat participants that observe messaging behavior have been shown to exhibit higher deception-detection accuracy than unaided chat participants who observe typing behavior. Participants were able to correctly classify on average 20.4% (sd = 51%) of deceptive video clips based on observing typing behavior and 35.7% (sd =41%) based on messaging behavior [Zhou and Zhang 2007]. Although observing typing behavior did not improve humans’ deception detection, we cannot conclude that typing behavior is not diagnostic of deception. Rather, it may suggest that humans have difficulty identifying and interpreting theoretically sound cues of deception from typing behavior and combining this information with theoretically sound cues of messaging behavior. To our knowledge, extant research has not identified theoretically sound cues of deception from typing behavior nor has it confirmed the applicability of messaging cues of deception in computer-mediated chat. This article addresses this limitation by creating a theoretical model of chat-based deceit that identifies two typing cues of deception (response time and the number of edits) and two messaging cues (lexical diversity and word count) and empirically tests them in a chat-based environment. These selected cues are noteworthy because they are easily implemented in most current chat-based applications. We focus on spontaneous deception in chat-based communication because this type of deception is most common in synchronous computer-mediated contexts [Whitty et al. 2012]. We attempt to demonstrate that increased cognitive load [Arnsten and Goldman-Rakic 1998; Hains and Arnsten 2008] and other associated psychological processes (e.g., inhibition of the prefrontal cortex) associated with deception can be operationalized and measured in a distributed, online environment. We use a custom chat-based instrument with precise timing measurements to capture and test the typing and messaging behaviors. In summary, we answer the following research question: What is the relationship between spontaneous deception and the number of edits (e.g., backspaces, deletes), response time, word count, and lexical diversity in chat-based communication? 2. THEORETICAL MODEL OF DECEPTIVE CHAT-BASED COMMUNICATION

We begin by deriving a theoretical model of chat-based deceit based on human psychophysiology. Psychophysiology is a branch of psychology that explains how physiology influences psychological processes and human behavior [National Institutes of Health 2011]. We apply this science to explain how the physiological effect of increased cognitive load during deception will influence the number of edits and deletions, ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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response time, word count, and lexical diversity in computer-mediated chat [see Arnsten and Goldman-Rakic 1998; Hains and Arnsten 2008]. Cognitive load refers to an increase in activation patterns and dopamine levels across the cortical areas of the brain. It has been consistently argued that lying increases cognitive load and that this increase can be used to detect deceit. Vrij et al. [2007] extended this by arguing that it is easier to detect deceit if a person has to tell their story in reverse order, which increases their cognitive load. Additionally, through the use of functional magnetic resonance imaging (fMRI), these physiological characteristics of cognitive load have been shown to increase with complex activities, creative tasks, difficult tasks, high memory load, concentration, stress, threat, and other forms of cognitive effort [Allen et al. 2007]. It is widely held that the act of deception creates a higher cognitive load than truth telling because of the complexity, stress, and threat of detection [Buller and Burgoon 1996; Ekman and Friesen 1969; Zuckerman et al. 1981]. Consistent with this body of research, we suggest that deception is a complex activity that adds additional cognitive demands beyond those already associated with simply maintaining the chat-based conversation. Deceivers must create plausible messages in real-time, while truth tellers must only recall information. So, when all else is equal, we expect that those engaging in deception during chat-based exchanges should exhibit more cognitive load. The effects of cognitive load on the cortical area of the brain can help us explain typing and messaging behavior in the presence of deception. The cortical area of the brain plays a critical role in memory, attention, perceptual awareness, thought, language, and consciousness processes [Shipp 2007]. Cognitive load has been shown to modify these functions [Sweller 1988]. Given the presence of increased cognitive load, the part of the cortical region that moderates complex cognitive behaviors, personality expression, decision-making, and correct social behavior—the prefrontal cortex—will be inhibited to promote more habitual, survival responses governed by the subcortical structures and posterior cortex [Arnsten and Goldman-Rakic 1998; Hains and Arnsten 2008]. This change of cortical function due to increased cognitive load is evident in memory malfunctions [Bremner 2006], language disorders [Vargha-Khadem et al. 1998], thought malfunctions [Hains and Arnsten 2008], initiation of the flightor-fight responses [Henry 1993], and perhaps is related to deception [Vrij 2008]. The generation and writing of human-language during chat is a complex behavior [Hauser et al. 2002]. When cognitive load and stress impair the prefrontal cortex of the brain, this increases the difficulty and thereby time to compose and communicate a chat-based response. The increased difficulty will result in more edits and deliberation, which take time. Furthermore, the human brain attempts to reduce the cognitive demands of language processing by spreading the task processing over a longer period of time [Hendy et al. 1997], especially during stressful situations. Following this logic, we predict the following. Hypothesis 1. Deceptive chat-based responses take longer to create than truthful responses. As the cognitive load caused by the stress and threat of deception will inhibit the prefrontal cortex associated with high-level cognitive processing, it becomes more difficult to compose coherent responses. In addition, deceptive messages are typically more difficult to create than truthful messages. However, the stress and cognitive load will activate the subcortical structures and posterior cortex that increase perceptual awareness and activate discharges to the sympathetic nervous system, which instigates the flight-or-fight response, greater awareness, and defensive behavior [Brodal 2004]. Hence, the message becomes more difficult to create and the individual makes ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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more strategic and defensive efforts to avoid potential threats, such as mistakes in the response. In computer-mediated chat, this is corollary to controlling the content and linguistics of a deceptive message at the expense of greater editing and revision. If deceivers are more strategic and aware in their communication and the communication is also more difficult, then they will engage in more editing and revisions. Thus, we posit Hypothesis 2. Hypothesis 2. Deceptive responses are edited more (backspaces and deletes) than truthful responses. An increase in cognitive load caused by deception should also result in shorter responses in deceptive communication than in truthful communication. One task a deceiver must perform is generating false or misrepresenting truthful information. This task is more complex than merely recalling truthful information, and the difficulty is further compounded by the deactivation of the prefrontal cortex, decreasing the deceiver’s ability to recall information from memory, communicate social messages, and generate complex sentences [Hains and Arnsten 2008]. Because of these difficulties, we suspect that deceptive chat messages will be less elaborate than truthful/true chat messages. Multiple studies have provided support for the hypothesis that deceitful responses were shorter (had fewer words) than truthful responses [Hancock et al. 2008; Lee et al. 2002; Zhou 2005; Zhou and Zhang 2004; Zhou et al. 2004]. We propose that this result will hold in our study as well. In summary, we predict the following. Hypothesis 3. Deceptive responses are shorter (fewer words) than truthful responses. The increased cognitive load, and thereby the deactivation of the prefrontal cortex, will decrease lexical diversity of chat messages. Lexical diversity is the ratio of unique words in the response to total words in the response. Zhou and Zenebe [2008] investigated several modalities of communication (e.g., email, chat, and face-to-face) for different task types (e.g., group decision-making, interview). They found that deceivers had lower lexical diversity than truth tellers because of the high cognitive load, arousal, and stress associated with deception. As previously discussed, this cognitive load impairs the prefrontal cortex of the brain, resulting in more habitual processing [Arnsten and Goldman-Rakic 1998]. The reduction of the prefrontal cortex makes it more difficult to perform the cognitively intensive processes of language generation. As such, it will become/be more difficult to retrieve a diverse set of words, and people will rely more on habitual words that are most easily accessible. In summary, lexical diversity will be reduced and we predict the following. Hypothesis 4. Deceptive responses have lower lexical diversity ratios than truthful responses. Figure 1 depicts the hypothesized model.

3. METHOD

To test our theoretical model, we utilized a repeated-measures experimental design, employing a custom chatbot to conduct a chat-based interview with participants. Unbeknownst to the participants, the custom chatbot captured the response time for each participant and the number of times each participant edited his or her message. ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 1. Hypothesized model of chat-based deceit. Table I. Participant Ethnicity Ethnicity African-American Asian Hispanic White

Number 27 10 2 69

Percent of Sample 25.0% 9.3% 1.9% 63.9%

3.1. Participants

One hundred and eight participants were recruited for the experiment from two large universities—one in the southeastern United States and one in the southwestern United States. Participants were solicited primarily through email and class announcements in several university courses. They were offered extra credit for participation. The mean age of the population was 26.51 years (SD = 10.56). Fifty-five of the participants were female and 53 were male. The sample was taken from an ethnically diverse population as shown in Table I. Ninety-seven of the participants spoke English as their native language, 11 spoke English as a second language. Each of the 108 participants was asked 30 questions resulting in 3,240 questions asked. Seventyeight questions were not answered, resulting in 3,162 usable chat-messages. Of these responses, 1,572 were deceitful (49.7%) and 1,590 were truthful (50.3%). 3.2. Custom Chatbot

The experimental task was to participate in a computer-mediated chat-based interview. To improve experimental control and accurately collect the chat-based communication metrics, we created a customized chatbot to conduct the interview. Chatbots were introduced in the 1970’s [Cerf 1973] as computer programs designed to simulate intelligent conversation. Recent studies have used artificial agents as confederates in experiments for increased control and reliability [Elkins et al. 2013]. Using the chatbot agent allowed for precise measurements and absolute consistency in the interaction. Computer as Social Actors (CASA) theory proposes that human beings interact with computers as though computers were people [Nass et al. 1994]. In multiple studies, ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Table II. Question Type Breakdowns Type Description Affect Narrative Personality Moral Dilemma

Comparison Narrative

Attitude Future Action

Sample Question Please describe the contents of your wallet, purse, or backpack. Tell me about your favorite movie and the reasons you like it. Describe, in detail, how you spent your last vacation. What do you consider to be your greatest strength? If you found a wallet containing $1,000 and no identification in it, what would you do with it and why? Tell me about a time when you thought of taking something of value from someone who trusted you. What do you think should be done about the global warming? What do you plan to do during your next break or vacation?

Number of Questions 8 7 4 1 3

3

3 2

researchers have found that participants react to interactive computer systems no differently than participants react to other people [Nass et al. 1997]. It is suggested that people fail to critically assess the computer and its limitations as an interaction partner [Nass et al. 2000]. So, we used the chatbot to gain precision and consistency with the expectation that the norms of interaction observed between people occur no differently between a person and a computer [Hall et al. 2008]. The custom chat client for this experiment was designed to ensure accurate measurement of response times and to remotely capture user-editing behavior. The system has an internal timer that measures response time from the moment the question is asked until the moment the user submits the chat message. The custom chatbot also implements a series of keyboard traps that capture the following keystrokes: Backspace, Delete, Spacebar, and Ctrl-X. This information is sent via XML to a server, which securely stores and processes the information. An internal script was used by the chatbot to conduct the interview with the participants. The chatbot always asked the same questions in the same order. Since deceptive chat-based behavior is almost certainly context dependent, we chose to ask varying types of questions that could elicit differing responses. The categories included description questions, feeling (affect) questions narratives, personal introspection, moral dilemmas, comparisons, attitudes, and future actions. The categories of questions were also selected to broadly represent the six different dimensions of deception as outlined in Hopper and Bell [1984]: fiction, playing, lies, crimes, masks, and unlies. Furthermore, the different types of questions required varying levels of creative and inventive thinking. For example, the future-action questions (e.g., “What do you plan to do during your next break or vacation?”) likely require both truthful and deceptive participants to be creative and inventive. On the other hand, some questions (e.g., descriptive questions) require less creativity from both groups. Thus, asking varying types of questions allows us to investigate whether our results are robust across a variety of contexts, questions types, and levels of creative thinking The questions were derived from previous deception studies and loosely based on Interpersonal Deception Theory [Buller and Burgoon 1996]. The eight types and the number of questions from each category are shown in Table II. ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 2. Screenshot of Chatbot.

3.3. Procedure

Participants were told that the purpose of the study was to understand deception in chat-based communication, but were not told that their responses would be timed, text would be analyzed, and certain keystrokes measured. The experiment took place online and participants used a computer browser to access the experimental website. On the site, they were presented with a consent form and asked to input some basic demographic information if they agreed to participate. Participants were then instructed to go to an online system where they received basic instruction on how to operate the chatbot system. They were told that they would be asked a series of questions and would be instructed to be deceitful or truthful when prompted by the system. Furthermore, participants were told that extra credit would only be given for active and realistic participation. When participants were ready to begin the interview, they were instructed to type the word “yes” in the chatbot. For each question, the chatbot first posed a question in the top screen of the chat client, appearing like a real person in a chat-based communication. Next, the system prompted the user to tell a lie or to tell the truth in response to that question by placing “Truth” or “Lie” in the box on the bottom right of the screen (refer to Figure 2, number 2). The purpose of telling participants to be deceitful or truthful as questions are received is to promote spontaneous deception. Spontaneous deception has been shown to be the most common form of deception in synchronous communication (e.g., chat or FTF communication) [Whitty et al. 2012]. This condition assignment was made randomly by the computer and was held constant for follow-up questions. This ensured random assignment to conditions for every question group. Since it was a distributed task, we could not ensure vigilance at the computer. However, using random assignment should have taken care of any systematic error (e.g., it is equally likely that a participant would have gotten distracted by something other than the experiment during either condition). Additionally, we ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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performed outlier analysis and looked at responses that took longer than three minutes. Approximately 3% of the responses fell in this range (46 truthful, 65 deceitful), and over half of these responses came from 7 participants that consistently had a longer response times across both conditions. In the truth condition, participants were instructed to tell the whole truth and nothing but the truth. When they were asked to lie, they were encouraged to make up false statements, exaggerate, or be vague. In short, be deceptive, but try to appear credible. Third, the participants typed in their response to the question. As a manipulation check, participants rated how truthful or deceitful the response was after they submitted the message—1 being absolutely false, 5 meaning absolutely truthful (see Figure 2, number 1). Given the distributed nature of the task and the fact that some statements may be partially true or omit details, the manipulation check was tested. The measure was highly correlated with the condition (Pearson’s r = .62, p < 0.001), thus providing us with evidence that participants followed instructions and attempted to deceive or tell the truth when prompted. The users then clicked “Send” (see number 3 in Figure 2) and the chatbot asked the next question in the script. The process was repeated until the interview was complete. The average interview took about 45 minutes. 4. RESULTS

To test hypothesis 1 (Deceptive chat-based responses take longer to create than truthful responses), we performed a point-biserial correlation between response time and condition (truthful or deceptive) and found that the correlation was −0.042 and significant at p < 0.01 for a one-tailed test. The users’ ratings of the truthfulness of their responses were highly correlated with the condition (Pearson’s r = .62, p < 0.001), and when the users’ reported measures were analyzed in relationship to response time a stronger relationship was discovered −.097, with p < .001. Next, we compared the mean response time for deceitful and truthful responses across the entire sample and found that a deceitful response took an average of 5.24 seconds longer than a truthful response (truth mean = 50.15, truth SD = 51.47; deceit mean = 55.39, deceit SD = 71.85). Figure 3 shows the average response time per question during the interaction. A repeated-measures analysis of variance showed that this was a statistically significant difference (F = 6.961, p < .01, df = 3158). In order to ensure that the difference in response time for truthful and deceitful responses was valid within subjects, we averaged the deceitful and truthful response time for each participant and conducted a within-subjects paired t-test. Again, we found a statistically significant difference between truthful and deceitful responses (t = 2.40, p < 0.05, df = 106), where deceitful responses took longer. Using power analysis, we determined that Cohen’s d = .312, which shows small effect size [Cohen 1988]. Figure 4 shows the average response time for truthful and deceitful responses for each participant. Therefore, hypothesis 1 was strongly supported. As a further supplemental, ad hoc analysis of response time, we examined the moderating influence of age on the relationship between deception and response time. We suspected that age might have a moderating effect because younger people are more familiar with technology and are generally more accustomed to chat-based communication than older people [Atkin et al. 2005]. In addition, studies have indicated that deterioration of the biological framework that underlies the ability to think and reason begins as early as the mid-twenties [Pieperhoff et al. 2008]. Technical familiarity coupled with quicker reaction times could cause younger people to have generally shorter response times to questions and thus cause a positive correlation between age and response time. We found that age was positively related to response time with a Pearson’s coefficient of .281 (p < .001); ANOVA showed that there is ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 3. Average response time per question.

Fig. 4. Average response times by participant.

a main effect of age on response time (F = 52.767, p < 0.001, df = 3158). Figure 5 shows the average response time based on the age of the participant. We followed the recommendations of Baron and Kenny [1986] to test moderation of age, and the result was a model with high significance for the interaction of age and the users’ reported scale of deceit on response time (F = 4.235, p < 0.05, df = 3158). To illustrate this, we ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 5. Average response time and age.

Fig. 6. Average response times by age group.

split the population at age 28. The average age of our participants was 26.51 years. We rounded up to 27 years and included the only participant that was 28 in this group (the data showed a natural split at this age). As shown in Figure 6, both types of responses take longer for older people and there is a much larger difference between ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 7. Average edits per question.

deceitful and truthful answers. Using this split, we examined the difference between truth and deceit in younger participants and noted there was not a main effect found between condition and response time (F = 0.2478, p = 0.6187, df = 2347). Therefore, we conclude that age is an important moderator of response time. To test hypothesis 2 (Deceptive responses are edited more than truthful responses), the custom client captured the number of characters that were edited during a response. We first tested this hypothesis using repeated-measures ANOVA and found that there were no significant differences in the number of edits between truthful and deceitful responses (F = 1.28, p = .258, df = 3158). Figure 7 shows the average of message-editing for each question. However, when we averaged the deceitful and truthful number of edits for each participant and conducted a within-subjects paired t-test, we found a statistically significant difference between truthful and deceitful responses (t = 2.08, p < .05, df = 106). Deceitful responses were edited more often. Using power analysis, we determined that Cohen’s d = .282, which shows small effect size [Cohen 1988]. Figure 8 shows the average by participant based on condition. Thus, hypothesis 2 was partially supported. Further post hoc analysis on the number of edits found that age was correlated with the number of edits (Pearson correlation .096, p < .01). This is not a large effect and ANOVA showed that there was not a main effect (F = 0.2355, p = 0.6275, df = 3158). Further analysis did not provide support for age moderating the relationship between deception and the number of edits (F = .4979, p = .4805, df = 3158). To test hypothesis 3 (word count is negatively correlated to deceitful responses), we ran a bivariate correlation and found a Pearson correlation of 0.024, which was not significant. Repeated-measures ANOVA found a significant main effect for word count in that deceitful responses were shorter than truthful responses (F = 4.665, p < .05, df = 3158) by an average of less than one word (truth mean = 18.55, truth SD = 19.41; deceit mean = 17.63, deceit SD = 19.45). No support was found when we averaged the ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 8. Average edits by participant.

deceitful and truthful word counts for each participant and conducted a paired t-test (t = −0.224, p = .82, df = 106). As we examined this further, we looked at the skew of the word count responses. In order to test the overall population of truthful and deceitful responses, we needed to transform the raw word count to log(word count), as shown in Figure 9. Once the distribution was normal, we conducted a Welch Two Sample t-test to compare the two populations. We found significance at p < .05 (t = −2.45, df = 3157.15), where the deceitful responses were shorter than the truthful responses (logtruth mean = 2.615, logtruth SD = .870; logdeceit mean = 2.538, logdeceit SD = .886). Using power analysis, we determined that Cohen’s d = .083, which shows small effect size [Cohen 1988]. Therefore, hypothesis 3 was supported. Lexical diversity is the ratio of unique words in a response to total words in the response. We calculated this ratio for each response and used the resulting coefficient in the analysis. A bivariate correlation between lexical diversity and condition yielded a Pearson’s correlation of −.036 and was significant at p < .05 for a two-tailed test. The population of truthful responses did not have statistically different lexical diversity than the deceitful response population (truth mean = 89.69, truth SD = 16.74; deceit mean = 90.83, deceit mean = 14.96). A repeated-measures ANOVA shows that this was not statistically significant (F = .4765, p = .85, df = 3158). Similarly, a paired t-test was not significant (t = −.43, p < .6, df = 104). Contrary to our prediction and past literature, hypothesis 4 (deceptive responses have lower lexical diversity ratios than truthful responses) was not supported. Many of the dependent variables are related and Table III shows the correlation of these values. These are all significant at p < .001. We predicted and showed that when a person is lying, s/he would need more time to respond and use fewer words. This only makes sense if the number of edits serves as/is a moderator between response time and word count. We tested this relationship and in fact found that edits is a strong moderator between total time and word count (F = 14.263, p < .001, df = 3158). ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Fig. 9. Histograms of raw word count and Log(Word Count). Table III. Correlations Between Dependent Variables Response Time Response Time Edits Word Count Lexical Diversity

.57 .39 −.12

Edits .57 .33 −.11

Word Count .39 .33

Lexical Diversity −.12 −.11 −.32

−.32

Table IV. Averages for Participants with English as a Second Language Truth Condition Deceit Condition

Response Time 35.4 seconds 47.6 seconds

Edits 6.12 edits 7.23 edits

Word Count 10.3 words 9.7 words

Lexical Diversity .930 .946

As a final ad hoc analysis, we looked at some descriptive statistics for those users that had English as a second language. Eleven (7 Asian, 2 White, 2 African-American) participants, whose average age was 23.4 years, fell into this category. We looked at the averages for response time, edits, word count, and lexical diversity for this group and they are shown in Table IV. Although we cannot say anything with statistical power, all of the general relationships seem to hold for this group. 5. DISCUSSION 5.1. Summary of Findings

The summary of the four hypotheses and findings are listed in Table V. Our data analysis supported three of our four hypotheses. As predicted, hypotheses that examine the influence of typing cues of spontaneous deception (H1, H2, and H3) were supported. Consistent with psychophysiology research on deception, cognitive load, and brain processes, deceptive chat responses took significantly more time to create than truthful chat responses by an average of 10% (H1). Deceivers also performed more message-editing than truth tellers. This was especially evident when comparing within subjects in both truthful and deceptive conditions. Interestingly, both typing cues of deception—response time and number of edits—were strongly correlated to age. The older participants took longer and edited their messages more frequently. There are several possible explanations for this phenomenon. First, younger users may ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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Table V. Overview of Hypotheses and Findings Hypotheses Hypothesis 1: Deceptive chat-based responses take longer to create than truthful responses. Hypothesis 2: Deceptive responses are edited more (e.g., more backspaces and deletes) than truthful responses. Hypothesis 3: Deceptive responses are shorter (i.e., fewer words) than truthful responses. Hypothesis 4: Deceptive responses have lower lexical diversity ratios than truthful responses.

Results Supported Supported Supported Not Supported

be more familiar with the medium and less concerned about correct spelling and grammatical features such as sentence structure. Second, younger participants may simply be faster typists and use fewer edits because they are used to typing and do not make as many errors as older people. However, the fact that the difference in both edits and response time is markedly increased during deception in the older population lends credence to the notion that the psychological processes of deception discussed earlier (e.g., cognitive load) may cause the difference. In other words, if deception does cause increased stress and increased cognitive effort, than these may have a greater effect on an older mind. These are empirical questions that deserve further exploration in future studies. It is important to note that response time consists of several different factors, including reading, internalizing, and making sense of the question being asked, response latency (the time between when the question was asked and when the user started their response), and the crafting of the response including edit time. Thus, the measure that we captured is a gross measure of overall cognitive processing. Various researchers [Sporer and Schwandt 2006] have suggested that response latency is correlated with ´ cognitive effort and deception (k = 18, d = 0.18), although DePaulo et al’s [2003] metaanalysis (k = 32, d = 0.02) did not find support for that. Although our response time measure includes response latency, ideally, in retrospect, we would like to have captured more precise and fine-grained measures of the timing of various message processing/response components. For example, it may be insightful to look at response latency as a separate measure (i.e., the time from when the question is asked until the response is first started). Similarly, overall user typing speed and typing patterns, as well as variations from these patterns, may offer insights into the cognitive processes. Surprisingly, the messaging cue lexical diversity was not negatively related to deception. This challenges previous assumptions that linguistic cues of deception in an interpersonal context also apply to a chat-based context. This finding may be due to the type of interview being performed. The chatbot performed a structured interaction. The agent asked interview-style questions rather than allowing the user to communicate freely. This resulted in responses that were generally shorter than would be found in face-to-face interactions or in an email or long document or manuscript. These shorter responses (fewer words) mathematically encourage higher lexical diversity coefficients because lexical diversity is calculated by dividing the number of unique words by the number of total words. Alternative explanations are discussed in the following section. 5.2. Implications for Research

To our knowledge, this is the first research paper to theoretically derive typing cues of spontaneous deception in synchronous chat and empirically validate these cues in an experimental setting. We found that both the number of edits/deletions and the response time are indicative of deception based on the data collected and analyzed in our ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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study. This is contrary to previous face-to-face findings that failed to find support for these relationships including response latency (k = 32, d = 0.02), length of interaction ´ (k = 3, d = −.20), and a variety of other interruptions/disfluencies. [DePaulo et al. 2003]. Of particular interest, Zhou and Zhang [2007] found that the ability to observe typing behavior did not improve unaided humans’ deception-detection ability. Hence, the results of our study suggest that humans are generally unaware of, have difficulty in tracking the precision of, or have difficulty interpreting response time and the number of edits as significant cues of chat-based deception. This stresses the need for deception aids to detect deception in computer-mediated chat. In our study, we show that it is possible to implement a system that is capable of measuring typing cues of deception. Our research challenges the generalizability of past studies that find certain messaging cues (i.e., lexical diversity) are indicative of deception [Zhou et al. 2004, 2008a]. In the context we investigated, we found that lexical diversity was not diagnostic of deception. However, lexical diversity has been shown to be a significant predictor of deception in other CMC-mediated contexts, despite having similar findings regarding the influence of deception on word count [Zhou and Sung 2008; Zhou et al. 2004]. Perhaps the discrepancy relates to the experimental task even though the studies similarly investigated deception over CMC channels. Zhou et al. [2004] investigated dyadic communication using a modified desert survival task where two individuals engaged in prolonged and potentially persuasive appeals to adopt a subset of the most important survival items. In another study, Zhou and Zenebe [2008] instituted a Mafia game in a synchronous online chat room, which also had participants engage in prolonged, persuasive discussion. These discrepant findings may suggest that communication patterns vary depending on the task being done and, more specifically, the length of discussion. As opposed to the prolonged discussion task in [Zhou and Sung 2008; Zhou et al. 2004], our task utilized a shorter question/answer format. Thus, lexical diversity may not behave as predicted because all responses are inherently short and lexical diversity is therefore inherently high for everyone. Prolonged discussions, however, allow for more variation in lexical diversity and thus may be significant. Interestingly, within our dataset we did find evidence of shortened code words or symbols that you may not find in other non-chat contexts. Just over one percent (1.42%) of the messages contained paralinguistic symbols or code words (e.g., smiley face, “. . . ”). We did not conduct differentiated analysis on how the type of question affects typing and messaging behavior, but it is likely that this will have an impact. One of the reasons this could not be tested is that, as shown in Figures 3 and 7, users’ response times and edits decreased over the duration of the interview (i.e., time affects behavior). In order to test the effect of the question type robustly, a new experiment should be conducted with randomized question order to alleviate this confound. Our research stresses the importance of considering age in interpreting the influence of typing behavior on deception. In our study, age positively moderates response time. Future research might investigate what underlying characteristics correlated with age—for example, computer anxiety [Laguna and Babcock 1997], computer selfefficacy [Reed et al. 2005], or risk aversion [Halek and Eisenhauer 2001]—are responsible for this moderating effect. 5.3. Implications for Practice

Typing is the most common form of computer-mediated communication. Although the typed message is a very rich source of information, the process of typing also yields valuable information and is often ignored in organizational settings. We show that two important typing behaviors to monitor are response time and the number of edits and deletions, as they can be indicative of deception. Although most current chat-based ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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clients do not offer this information to users, they are easily calculated and could be used in practice to inform users. There are also numerous other contexts where these metrics could be used to inform decision makers. For example, many job applications now have an initial portion that must be filled out online (other examples include dating sites, bank loan applications, visa applications, etc.). With further examination and expansion, metrics like the ones in this study could be used in many online environments. We do not envision a system that makes a blanket determination of truth versus deceit using these measures. Rather, we envision these metrics being used as possible indicators of message trustworthiness that would need to be evaluated in the message context and with other considerations included. As previously discussed, chat-based deception is a serious problem in computermediated communication. Our preliminary research suggests that practitioners should examine metrics such as response time and number of edits in addition to message behavior such as lexical diversity to detect deception in computer-mediated chat. These techniques for detecting deception can be applied to a great breadth of applications such as online job applications, admission submissions, social aid applications, and other forms of communication that exhibit characteristics similar to chat-based messages. 5.4. Limitations

One of the main limitations of the study is the fact that participants did not have advance warning to plan for their deceitful responses. In the actual world, chat-based deceivers may have systematically planned their deceptive response and rehearsed it ´ several times. Previous research [DePaulo et al. 2003; Sporer and Schwandt 2006] has found that certain cues of deception become significant when individuals are given time to prepare. For instance, in a meta-analysis of paraverbal deception behaviors, [Sporer and Schwandt 2006] found that the overall message duration was significantly less for deceivers when participants were afforded an opportunity to prepare. In conditions where individuals were only given minimal preparation time, deceivers response latency was significantly higher than nondeceivers. Our participants were asked the question and informed of their condition at the same time. So, although the medium and interface was like a normal chat, the opportunity for the deceiver to plan beforehand was limited. Deceivers in the actual world do not know a priori the questions a chat partner may ask, yet they may have been deceiving for a long time and their response times and edits may be less correlated to their deception. Hence, the scope of our study is limited to situations in which participants must formulate responses on the spot in synchronous communication. A second limitation is the possible lack of motivation for our participants. Our participants had moderate to low motivation to actually deceive the system, where a deceiver in the field may have substantial motivation to successfully deceive. Deception studies employ either sanctioned or unsanctioned deception. While most studies would prefer a design where unsanctioned deception could occur, much deception research (including this research) relies on participants being explicitly asked or instructed to deceive, which we acknowledge may impact the motivations and behaviors of an individual. Additionally, in our experiment, students were incentivized to participate based on extra credit they would receive if they engaged in active and realistic participation. It is plausible that these conditions created a context or environment that may not be fully representative of certain types of deception encountered outside the lab. Thus, we highlight and acknowledge that individual motivations to deceive and the type of answers provided (e.g., potentially above average quality deceptive answers) are a limitation of this study. ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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However, the truth status measure was highly correlated with the condition (Pearson’s r = .62, p < 0.001), thus providing us with evidence that participants followed instructions and attempted to deceive or tell the truth when prompted. In actual chat-based interactions, we would argue that increased motivation for successful deception may in fact increase response times and the number of edits for a deceiver and so these measures may have been understated in this study. It is important to remember that system users did not know that their responses were being timed or that their editing was being monitored. It is conceivable that a highly motivated deceiver would take more time to create a plausible response and also edit it more carefully. These are empirical questions for further study. Future research should attempt to differentiate between how deception and creativity influence chat behavior. In our study (as is common in the real-world), being deceptive sometimes required a person to be creative and invent information, and thus creativity and deception are not always mutually exclusive. Creativity, like deception, results in higher cognitive load and may thereby influence chat behavior. Acknowledging this overlap that may exist between deception and creativity, our results still provide strong evidence that deception will cause significant effects above and beyond those of merely being creative or inventive. First, in our experiment we manipulate deception across questions that require varying levels of creativity for both truthful and deceptive respondents. For example, the question ”What do you plan to do during your next break or vacation?” likely requires both truthful and deceptive participants to be creative and invent information. However, deceivers still showed a significant difference in chat behavior despite both groups having to engage in creative and inventive cognition. On the other hand, some questions require less creativity by both groups (e.g., please describe the contents of your wallet, purse or backpack). The differences in chat behavior for deceivers were still significant. This suggests that the effects of deception are robust across different levels of creativity and invention. Second, we also analyzed whether participants’ self-reported level of deceit is significantly correlated with chat behavior. This measure only asks about level of deceit, not creativity or invention. Hypothetically, it could be totally possible that someone engaged in highly creative and inventive behavior, but at the same time, rated the level of deception as low. We found that our results were still significant predictors of deceit when using the self-reported measure. In summary, because our results were robust when manipulating deceit across questions requiring different levels of creativity (for both deceivers and truth tellers) and our results were cross-validated when analyzing the self-reported level of deceit, we argue that deception will cause significant differences in chat behavior beyond the effects of creativity or invention. While we took care to create an experimental instrument that implemented the basic functionality of a chat-based environment, we recognize that the control afforded by experimental design may impact the natural interactions that participants may engage in if observed in the field. As such, some of the findings here may not generalize to a more natural setting. Similarly, experiment participants were primarily US university students and this also impacts the generalizability of the findings to other contexts and cultures. Future research should investigate the constructs examined in this study in the field; that is, observe, capture, and analyze data streams from chatbased conversations occurring between multiple individuals using software or other tools that are typically used in that environment despite the challenges that accompany that paradigm of research. A final limitation of the study is that it only examined a limited number of typing and messaging cues. In some situations (e.g., well thought-out responses), typing and message cues such as response time and the number of edits may also be high for truthful messages. This is a typical limitation for all deception cues and algorithms, ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

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in that there is no perfect predictor of deception. As such, it is important to examine many cues of deception in addition to the ones studied in this article. Additional messaging cues that could be examined may include message meaning, content diversity, redundancy, spatio-temporal information, perceptual information, positive affect, and negative affect [Zhou et al. 2004]. Typing cues of deception has received far less attention in literature, and future research should consider if any other cues of deception can be extracted from typing behavior in addition to the number of edits and response time. Finally, future research should examine in more detail how certain types of chat messages (e.g., declarative, interrogative, exclamatory, or imperative sentences) influence subsequent chat messages. 6. CONCLUSION

This research identifies typing behaviors that predict deception in chat-based communication. Deception in chat is prevalent and can result in serious consequences. Based on cognitive load theory, we predicted that deception would increase response time and the number of edits while decreasing lexical diversity and word count. We created a custom chatbot that conducted interviews with 108 participants and indicated when participants should be truthful or deceptive, resulting in 3,162 usable chat messages. Our analysis confirms that deception increases the number of edits and response time. Contrary to our prediction, deception does not significantly increase lexical diversity in chat-based communication. Age mediated the influence of deception on response time. These results have implications for better understanding deception in chat-based communication and developing decision aids to identify deception in a variety of chat contexts. REFERENCES Aamodt, M. G. and Custer, H. 2006. Who can best catch a liar? A meta-analysis of individual differences in detecting deception. Foren. Exam. 15, 1, 6–11. ABC NEWS. 2007. Cyber bullying leads to teen’s suicide. Allen, M. D., Bigler, E. D., Larsen, J., Goodrich-Hunsaker, N. J., and Hopkins, R. O. 2007. Functional neuroimaging evidence for high cognitive effort on the Word Memory Test in the absence of external incentives. Brain Injury 21, 13–14, 1425–1428. Arnsten, A. F. T. and Goldman-Rakic, P. S. 1998. Noise stress impairs prefrontal cortical cognitive function in monkeys - Evidence for a hyperdopaminergic mechanism. Archi. General Psychi. 55, 4, 362–368. Atkin, D., Jeffres, L., Neuendorf, K., Lange, R., and Skalski, P. 2005. Why they chat: Predicting adoption and use of chat rooms. In Online News and the Public, Michael B. Salwen, Bruce Garrison and Paul D. Driscoll Eds., Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 303–320. Baron, R. and Kenny, D. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Person. Social Psych. 51, 6, 1173–1182. ´ Bond, C. F. and DePaulo, B. M. 2006. Accuracy of deception judgments. Person. Social Psych. Rev. 10, 3, 214–234. Bremner, J. D. 2006. Stress and brain atrophy. CNS Neurol. Disord. Drug Targets 5, 5, 503–512. Brodal, P. 2004. The Central Nervous System: Structure and Function 3rd Ed. Oxford University Press, Oxford, UK. Buller, D. and Burgoon, J. K. 1996. Interpersonal deception theory. Comm. Theory 6, 3, 203–242. Buller, D. B., Burgoon, J. K., Buslig, A. L. S., and Roiger, J. F. 1994. Interpersonal deception .8. Further analysis of nonverbal and verbal correlates of equivocation from the bavelas et-al (1990) research. J. Lang. Social Psych. 13, 4, 396–417. Burgoon, J. K., Burgoon, M., Broneck, K., Alvaro, E., and Nunmaker, J. F. 2002. Effects of synchronicity and proximity on group communication. In Proceedings of the National Communication Convention. Cerf, V. G. 1973. RFC 439: PARRY encounters the DOCTOR. Chen, H. and Wang, F. 2005. Artificial intelligence for homeland security, IEEE Intell. Syst. 12–16. Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences 2nd Ed. Lawrence Erlbaum Associates, Inc.

ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

i

i i

i

i

i

i

i

9:20

D. C. Derrick et al.

´ DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., and Cooper, H. 2003. Cues to deception. Psych. Bull. 129, 1, 74–118. Ekman, P. and Friesen, W. V. 1969. Nonverbal leakage and clues to deception. Psychiatry 32, 1, 88–106. Elkins, A. C. and Derrick D. C. 2013. The sound of trust: Voice as a measurement of trust during interactions with embodied conversational agents. Group Dec. Negot. 1–17. Epstein, R. 2007. The truth about online dating. Scientific Amer. 26–34. Fitzpatrick, M. 2006. Deleting online predators Act (H.R. 5319). Hains, A. B. and Arnsten, A. F. T. 2008. Molecular mechanisms of stress-induced prefrontal cortical impairment: Implications for mental illness. Learn. Memory 15, 8, 551–564. Halek, M. and Eisenhauer, J. G. 2001. Demography of risk aversion. J. Risk Ins. 68, 1, 1–24. Hall, B. and Henningsen, D. D. 2008. Social facilitation and human-computer interaction. Comput. Hum. Behav. 24, 6, 2965–2971. Hancock, J. T., Curry, L. E., Goorha, S., and Woodworth, M. 2008. On lying and being lied to: A linguistic analysis of deception in computer-mediated communication. Discourse Process. 45, 1, 1–23. Hauser, M. D., Chomsky, N., and Fitch, W. T. 2002. The faculty of language: What is it, who has it, and how did it evolve? Science 298, 5598, 1569–1579. Hendy, K. C., Liao, J. Q., and Milgram, P. 1997. Combining time and intensity effects in assessing operator information-processing load. Human Factors 39, 1, 30–47. Henry, J. P. 1993. Biological basis of the stress response. News Physi. Sci. 8, 69–73. Hopper, R. and Bell, R. A. 1984. Broadening the deception construct. Quart. J. Speech 70, 3, 288–302. Jensen, M. L., Lowry, P. B., and Jenkins, J. L. 2011. Effects of automated and participative decision support in computer-aided credibility assessment. J. Manage. Inf. Syst. 28, 1, 201–234. Johnson, M. K. and Raye, C. L. 1981. Reality ,monitoring. Psych. Rev. 88, 1, 67–85. Kuo, F.-Y. and Yin, C.-P. 2011. A linguistic analysis of group support systems interactions for uncovering social realities of organizations. ACM Trans. Manage. Inf. Syst. 2, 1, 1–21. Laguna, K. and Babcock, R. L. 1997. Computer anxiety in young and older adults: Implications for humancomputer interactions in older populations. Comput. Human Behav. 13, 3, 317–326. Lau, R. Y. K., Liao, S. Y., Kwok, R. C.-W., Xu, K., Xia, Y., and Li, Y. 2012. Text mining and probabilistic language modeling for online review spam detection. ACM Trans. Manage. Inf. Syst. 2, 4, 1–30. Lee, T. M. C., Liu, H. L., Tan, L. H., Chan, C. C. H., Mahankali, S., Feng, C. M., Hou, J. W., Fox, P. T., and Gao, J. H. 2002. Lie detection by functional magnetic resonance imaging. Human Brain Mapping 15, 3, 157–164. National Institutes of Health. 2011. National Library of Medicine - Medical Subject Headings. http://www.nlm.nih.gov/cgi/mesh/2010/MBcgi?mode=&term=Psychophysiology. Nass, C. and Moon, Y. 2000. Machines and mindleness: Social responses to computers. J. Soc. Issues 56, 1, 81–103. Nass, C., Steuer, J., and Tauber, E. R. 1994. Computers are social actors. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Celebrating Independence. ACM, New York, 72–78. Nass, C., Moon, Y., Morkes, J., Kim, E.-Y., and Fogg, B. J. 1997. Computers are social actors: A review of current research. In Human Values and the Design of Computer Technology, Cambridge University Press, Cambridge, UK, 137–161. Newman, M. L., Pennebaker, J. W., Berry, D. S., and Richards, J. M. 2003. Lying words: Predicting deception from linguistic styles. Personal. Social Psych. Bull. 29, 5, 665–675. Pieperhoff, P., Homke, L., Schneider, F., Habel, U., Shah, N. J., Zilles, K., and Amunts, K. 2008. Deformation field morphometry reveals age-related structural differences between the brains of adults up to 51 years. J. Neurosci. 28, 4, 828–842. Reed, K., Doty, D. H., and May, D. R. 2005. The impact of aging on self-efficacy and computer skill acquisition. J. Manager.-Issues. Summer. Shipp, S. 2007. Structure and function of the cerebral cortex. Current Biology 17, 12, R443–R449. Smith, N. 2001. Reading between the lines: An evaluation of the scientific content analysis technique (SCAN). Police research series paper 135. London: UK Home Office, Research, Development and Statistics Directorate. Sporer, S. L. and Schwandt, B. 2006. Paraverbal indicators of deception: A meta-analytic synthesis. Appl. Cog. Psych. 20, 4, 421–446. Sweller, J. 1988. Cognitive load during problem-solving - Effects on learning. Cogniti. Sci. 12, 2, 257–285. Twitchell, D. P., Forsgren, N., Wiers, K., Burgoon, J. K., and Nunmaker, J. F. 2005. Detecting deception in synchronous computer-mediated communication using speech act profiling, Intel. Security Informatics.

ACM Transactions on Management Information Systems, Vol. 4, No. 2, Article 9, Publication date: August 2013.

i

i i

i

i

i

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i

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Vargha-Khadem, F., Watkins, K. E., Price, C. J., Ashburner, J., Alcock, K. J., Connelly, A., Frackowiak, R. S., Friston, K. J., Pembrey, M. E., Mishkin, M., Gadian, D. G., and Passingham, R. E. 1998. Neural basis of an inherited speech and language disorder. Proc. Natl. Acad. Sci. 95, 21, 12695–12700. Vrij, A. 2000. Detecting Lies and Deceit: the Psychology of Lying and the Implications for Professional Practice. Wiley, New York. Vrij, A. 2005. Criteria-based content analysis: A qualitative review of the first 37 studies. Psychol., Public Policy Law 11, 1, 3–605. Vrij, A. 2008. Detecting Lies and Deceit: Pitfalls and Opportunities 2nd Ed. Wiley, Hoboken, NJ. Vrij, A., Mann, S., Kristen, S., and Fisher, R. P. 2007. Cues to deception and ability to detect lies as a function of police interview styles. Law Human Behav. 31, 5, 499–518. Whitty, M. T., Buchanan, T., Joinson, A. N., and Meredith, A. 2012. Not all lies are spontaneous: An examination of deception across different modes of communication. J. Ameri. Soc. Inf. Sci. Tech. 63, 1, 208–216. Zhou, L. 2005. An empirical investigation of deception behavior in instant messaging. IEEE Trans. Prof. Comm. 48, 2, 147–160. Zhou, L. and Sung, Y.-W. 2008. Cues to deception in online Chinese groups. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences. 146–146. Zhou, L. and Zenebe, A. 2008. Representation and reasoning under uncertainty in deception detection: A neuro-fuzzy approach. IEEE Trans. Fuzzy Syst. 16, 2, 442–454. Zhou, L. and Zhang, D. 2004. Can online behavior unveil deceivers? An exploratory investigation of deception in instant messaging. In Proceeding of the 37th Hawaii International Conference on System Sciences. Hawaii. Zhou, L. and Zhang, D. 2007. Typing or messaging? Modality effect on deception detection in computermediated communication. Decis. Supp. Syst. 44, 1, 188–201. Zhou, L., Burgoon, J. K., Nunamaker, J. F., and Twitchell, D. 2004. Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communications. Group Decision Negoti. 13, 1, 81–106. ´ Zuckerman, M., DePaulo, B. M., and Rosenthal, R. 1981. Verbal and nonverbal communication of deception. In Advances Experimental Social Pyschology, L. Berkowitz Ed., Academic Press, Inc., vol. 14, 1–60. Received March 2012; revised October 2012, April 2013; accepted May 2013

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