DETECTING DECEPTION 1

Transcription

1 DETECTING DECEPTION 1 Detecting Deception in Face to Face and Computer Mediated Conversations Victoria Foglia Algoma University

2 DETECTING DECEPTION 2 Abstract Detecting deception is difficult. In order to detect deception in face to face conversations one must examine changes in body language. Detecting deception in an online conversation does not rely on body language but rather psycholinguistics cues. I compared participant s ability to detect a lie in either face to face or conditions with either honest or deceptive content. After viewing the video (face to face) or sales pitch participants completed a questionnaire to evaluate how honest the sale was perceived. I hypothesized that the -deceptive condition would be better detected compared to the face to face-deceptive condition due to the increased time one has to analyze what they are being presented with. The hypothesis was confirmed, the -deceptive condition was detected as deceptive significantly more than the face to facedeceptive condition. Thus psycholinguistic cues may be better detected than body language cues in deceptive conversations. Keywords: deception detection, body language cues, psycholinguistic cues, deception

3 DETECTING DECEPTION 3 Detecting Deception in Face to Face and Computer Mediated Conversations Deception is a common social phenomenon (Rowe, 2007). Much of deception includes small, everyday deceptions or white lies that are a part of people s everyday communication and used typically to remain polite or compliant (DePaulo, Kashy, Kirkendol, Wyer & Epstein, 1996). In face to face situations one must rely on verbal and non-verbal cues in order to detect deception. Some factors that make deceivers more detectable are motivation, cognitive load and personal consequences for those deceiving. Factors that influence one s ability to detect deception are gender, attractiveness, profession, training and ability to interpret nonverbal cues (DePaulo, Lanier, & Davis, 1983; Ekman, & O'Sullivan, 1991). Although there are many factors that may increase one s ability to detect deception it is still a very difficult task to do (DePaulo, et al., 2003; Vrij et al., 2000). Many factors may influence a deceptive person s statements that allow for their deception to be more easily detected. In determining the effects of increased motivation on the deceptive DePaul et al., (1983) and Ekman et al., (1988) both discovered that highly motivated deceivers increased their likelihood of being detected due to increased nonverbal body language cues. Thus, when deceptive individuals have higher motivation to deceive successfully they display more nonverbal body language cues that allow for their deception to become detected. Another factor that influences the detection of deception is cognitive load. Vrij, et al. (2008) asked deceptive individuals to recite their statements in reverse, increasing cognitive load and causing their behaviour to be more detectable. Increasing cognitive load has been shown to cause deceptive individuals to make more speech hesitations and speak more slowly. Increasing cognitive load results in increases in the verbal cues to deception, causing the deceptive to become more detectable (DePaulet al., 1983). Finally, ten Brinke and Porter (2012) found that

4 DETECTING DECEPTION 4 another factor influencing the detectability of a deceptive person is the personal consequences they face if caught. Due to the high consequences they face if detected they have a difficult time masking their emotions, leaking microexpressions that allow for them to be more detectable. Although there are certain circumstances in which body language cues to deception occur more frequently, their brevity in nature makes them difficult to notice. Thus further research has examined what characteristics in an individual may cause them to be better at detecting deceit. Multiple characteristics may influence a person to better detect deception such as gender, attractiveness, profession, training, ability to detect non-verbal cues and age. DePaulo et al. (1985) found that females who are deceiving are more difficult to detect than males and that attractive people were better at detecting compliant deceptions, such as agreeing when one truly does not want to. Ekman and O Sullivan (1991) discovered that profession did not have a large impact on one s ability to successfully detect deceit. Out of many different types of law enforcement professions, the only group to have a difference in detection ability was the U.S. Secret Service. This difference may be due to the amount of crowd scanning they do in order to look for unusual behaviour as well as an increase in accuracy training. When Bond (2008) attempted to find a difference in profession and detection ability, there was also a lack of effect. Porter, Woodworth and Birt (2000) were interested in discovering whether college students and Canadian parole officers could be trained to improve their deception detection ability. After attending workshops focusing on detecting deception through nonverbal body language cues, both groups improved their detection ability, but the parole officer s abilities improved to a greater extent. A final influence that affects one s ability to better detect deception is one s ability to detect microexpressions, which are very brief, nonverbal expressions. Frank and Ekman (1997)

5 DETECTING DECEPTION 5 found that participants who were successful at detecting deceit were more successful at detecting microexpressions than those who were not as successful at detecting deceit. Thus, the ability to detect microexpressions successfully, though a difficult task, can influence one s ability in detecting deceit. In sum, there are multiple body language cues that can be used to detect deception in a face to face conversation. Although these cues are difficult to detect there are certain circumstances in which they are displayed more frequently allowing the deceptive to be better detected. Even so these cues are difficult to analyze. Few people are significantly better than chance at detecting deception in others. Consequently, body language cues to deception can only be used when the conversation is in face to face. Today there are many other ways to communicate that do not include body language such as computer mediated conversations. Without body language cues in these conversations there must be another set of skills used to detect deception in these forms of communication. Modern conversations can take place over many computer-mediated devices such as cell phones, s, and instant messaging. Detecting deception through computer-mediation differs from face to face conversations due to the lack of verbal and non-verbal body language cues. In order to detect deceit in computer-mediated conversations one relies on patterns of psycholinguistic cues in written statements (Newman, Pennebaker, Berry, & Richards, 2003; Duran, Hall, McCarthy & McNamara, 2010). Deceptive statements have been found to be less complex than honest statements. Deceptive statements also typically include more errors, and an increase in responding time in comparison to an honest statement (Duran et al., 2010; Zhou, Burgoon, Nunamaker, & Twitchell, 2004; Newman et al., 2003). In order to more fully

6 DETECTING DECEPTION 6 understand how to detect patterns of deceptive behaviour it must be evaluated through different mediums such as comparing face to face and computer-mediated deception. Unlike in face to face conversations, deception detection in computer-mediated conversations relies heavily on psycholinguistic cues (Newman et al., 2003; Duran et al., 2010). Psycholinguistics studies the relationship between linguistics and psychological behaviour, consisting of verbal and written language (Arciuli, Mallard, & Villar, 2010). Newman et al., (2003) and Duran et al., (2010) both have found that when one is deceiving in a computermediated conversation their statements are less complex and include fewer personal pronouns than honest statements. Zhou et al., (2004) found that when deceiving through computermediated conversations the deceivers use more negative emotion words, fewer exclusive words, and take longer to respond than those who are being honest. Due to the increase in popularity of computer mediated conversations it is important to further understand common factors that influence on to deceive through this form of communication. Utz (2005) and Hancock, et al. (2009) found that certain types of computermediated deception are considered acceptable and others unacceptable. Deceiving about how available one is to talk is considered socially acceptable, whereas deceiving about one s gender or attractiveness is not. Thus, the prevalence of deception in computer-mediated conversations has led to comparisons as to how it differs from face to face deception. In comparing face to face and computer- mediated deceptive conversations Van Swol, Braun and Kolb (2013) found that deception through computer-mediated conversations was better detected than deception in face to face conversations. They also found that participants reported feeling less guilty in computer-mediated deceptive conversations in comparison to face

7 DETECTING DECEPTION 7 to face, and deceive more frequently in computer based conversations. Similarily, Zimbler and Feldman (2011) found that participants were more likely to deceive about themselves, and deceive more frequently in computer-mediated than face to face conversations. This is assumed to be due to the increase in time an individual has to respond in a computer-mediated conversation as compared to face to face (Whitty, Buchanan, Joinson & Meredith, 2012). Also, Naquin, Kurtzberg, and Belkin (2010), when comparing an ed statement to a hand written statement, found that those in the condition deceived more frequently, were bolder in their deceptions, and felt more justified. I will examine deception detection in face to face versus computer-mediated communication that contains more or less deceitful information. In order to determine this, participants will either view a video, simulating face to face, or of a sales pitch. These videos and s will either appear to be deceiving or not deceiving the viewer of what they are selling. Due to spam mail (Young & McLeod, 2007) and computer-mediated deception becoming more common I predict that the computer-mediated deception condition will be more easily detected. I also assume that -deceptive condition will be better detected than the video-deceptive condition due to the type of cue used to analyze deception within these conditions. The psycholinguistic cues to deception are static, therefore a lie through computer mediation allows for more time to analyze what is being presented. Face to face conversations rely on body language cues to detect deception which are much briefer and therefore more difficult to analyze and detect. Therefore, I hypothesis that the computer mediated-deceptive condition will be more accurately detected than a face to face conversation, implying that psycholinguistics cues are more readily detectable in deception than body language cues. Method

8 DETECTING DECEPTION 8 Participants Forty seven adults (18-22 years) were recruited with flyers distributed to Introductory Psychology classes at Algoma University. Participants received 0.5% towards their grades as compensation for their participation. Materials Participants either read an or viewed a video on a Dell Inspiron 13-Inch Laptop (Chennai, India). The s were displayed through a Microsoft, Outlook account (Redmond, Washington, United States). The videos were displayed through Windows Media Player (Redmond, Washington, United States) and listened through Sennheiser HD 201 headphones (Wedemark, Lower Saxony, Germany). Each participant completed a 9 item survey to evaluate their perceptions of either the or video. The questions included whether or not they would consider purchasing the product, how sincere the sale appeared, how unusual it appeared, how common it appeared and how honest it appeared. They were asked to circle their answers on a 5 point sale. This scale ranged from strongly agrees to strongly disagree. Procedure The study was a 2x2, between subjects design. Participants were either in the or video condition. Within each condition participants were either given deceptive or non-deceptive content. Participants were aware upon their appointments that they would be viewing a form of a sales pitch in either a video or an . The videos and s both consisted of sales in which anti-virus protect was offered as a sale. In order to manipulate a deceptive condition the deceptive sales also included a warning for the participants, stating their computer had a virus. The video participants were informed that the video would be of a door-to -door sales, and to

9 DETECTING DECEPTION 9 imagine that this was happening to them. The participants were told that the would be a company trying to sell a product and to imagine this was in their own inbox. Participants were told to take as much time needed; allowing participants to view the video again if needed or re-read the . Participants in the video conditions were asked to wear headphones while viewing the video. Once finished their viewing or reading participants were instructed to complete a 9 item survey evaluating how they perceived the sale. After completing the survey all participants were debriefed. Results Factorial ANOVAs were used to examine participant s perceptions (i.e. honesty, persuasiveness, likelihood of purchasing and unusualness) of face to face (i.e. video) and computer mediated (i.e. ) sales pitches. An alpha level of p <.05 was set for all statistical analyses. Main Effects of Presentation ( or Video) There were no statistically significant main effects on honesty (F (1,62) =.004, p>.05, η 2 =.00), persuasion (F (1,62) =.47, p =.49, η 2 =.01), or likeliness to purchase the product, (F (1,62) =.29, p =.60, η 2 =.00) when comparing (M = 7.87, SD = 3.62) versus video presentation (M = 7.84, SD = 2.71). There was a statistically significant main effect of presentation condition on perceived unusualness. The video condition was perceived as more unusual than the condition, F (1,62) =19.92, p<.001, η 2 =.24 Specifically, the video-honest condition, (M=6.35, SD=2.32), was perceived as more unusual than the video-deceptive condition, (M=4.31, SD=1.62).

10 DETECTING DECEPTION 10 Main Effects of Level of Deception (Deceptive or Non-Deceptive) There was a statistically significant main effect of deception versus no deception on levels of perceived honesty. Participants identified the honest sales pitch as significantly more honest (M =9.44) than the deceptive sales pitch (M = 6.18), F(1, 62) = 27.09, p<.001, η 2 =.30. There was also a statistically significant main effect of level of perceived persuasiveness on deception. Participants were able to accurately identify that they sale s pitch was deceptive influencing their perception of the sales persuasion. Thus, the video-deceptive and face to facedeceptive conditions were perceived as less persuasive than the honest conditions F(1, 62) = 9.654, p<.003, η 2 =.13. There was a statistically significant main effect of deception on the likeliness to purchase the product. Participants were able to accurately identify that they sale s pitch was deceptive in both the video-deceptive (M = 3.25, SD = 1.24) and face to face- deceptive conditions (M = 3.87, SD =1.99), influencing them to be less likely to purchase the product F(1, 62) = , p<.002, η 2 =.15. There was a statistically significant main effect of perceived deception on unusualness. Participants were able to accurately identify that they sale s pitch was deceptive based on its level of perceived unusualness, F(1, 62) = 7.566, p =.008, η 2 =.11 Interactions There was a statistically significant interaction of deception and presentation on level of perceived honesty, F(1,62) = 13.95, p = <.001, η 2 =.18. Post-hoc analysis using Tukey s HSD revealed that the participants who viewed the deceptive condition (M = 5.00, SD = 2.22) perceived their sale s pitch as less honest, than those in the deceptive video condition (M =7.38, SD = 2.31). (See Figure 1 in Appendix A)

11 DETECTING DECEPTION 11 There was also a statistically significant interaction of level of deception on persuasion and condition, F (1,62) = 6.388, p =.014, η 2 =.09. Post-hoc analysis using Tukey s HSD revealed that the participants who viewed the deceptive condition (M = , SD = ) perceived their sale s pitch as less persuasive than those in the deceptive video condition (M = , SD = ) (See Figure 2 in Appendix A). Also the non-deceptive was perceived as less deceptive than the deceptive but no difference was found between the video conditions. There were no statistically significant interactions for the likeliness to purchase the product or perceived unusualness. Discussion Due to the increase in popularity of computer-mediated conversations it is important to examine how successful we are at detecting deception through these forms of communication. Researchers have found that computer mediated deception is better detected than face to face deception (Van Swol, Bruan & Kolb, 2013). Based on these findings I tested the hypothesis that computer-mediated deception would be better detected than video simulations of face to face deception. This hypothesis was supported in the present study; the -deceptive condition was perceived as significantly less honest than the video-deceptive condition. Also, the conditions over all were better detected as having a difference between honest and deceptive content. Participants were more accurate in detecting honesty in the condition than the video condition. Lastly, there were no significant differences between the video-honest and the video-deceptive conditions on levels of perceived honestly. Thus, the video conditions over all were difficult to determine levels of honesty or deceptiveness. Deception in the video conditions

12 DETECTING DECEPTION 12 may have been more difficult to detect due to the amount of time the participants had to analyze the sale. conditions allow for a longer duration of time to analyze what is being presented where as video, or face to face conversations, are quicker. Furthermore, the video condition may have been perceived as less authentic than the conditions, influencing how honest or deceptive they were perceived. Similar patterns were found for persuasiveness of the sale. The more deceptive the condition the less persuasive the sales pitch. Levels of persuasion were lower in the deceptive than the deceptive video. Coinciding with previous findings it makes sense that the video conditions were perceived as less persuasive than the conditions. It can be assumed that a possible reason why a sale is not persuasive could be due to uncertainty in how honest the sale is perceived. A possible explanation as to why the -deceptive condition is better detected than the video-deceptive condition is due to the amount of time given for one to analyze communication content. Whitty, et al., (2012) stated that online conversations allow one to have an increase in time to process what is being read. As face to face conversations can be very brief, there may be less time for thorough analysis of the content compared to an . Another possible explanation as to why it was more difficult to detect deception in the is that in general, most people are no better than chance at detecting lies in a face to face situation (DePaulo, et al., 2003; Vrij et al., 2000). When detecting lies in a face to face conversation we must rely on body language cues. These body language cues are very brief in nature, making them very difficult to notice and analyze. Whereas analyzing psycholinguistic cues in a computer mediated conversation are static. Thus, body language cues indicating deception in the video may have been brief and difficult to detect.

13 DETECTING DECEPTION 13 Although the deceptive sales pitch was perceived as less honest and persuasive than the face to face pitch there was no difference found in the likeliness of purchasing the product. I would have expected there to be less likeliness in purchasing in the deceptive conditions. A possible explanation could be that the participants did not consider that they actually needed the product. All participants viewed the study on a laptop that was not their own while all conditions involved a version of anti-virus software sale. Since they were not using their own computers they may not have considered needing to purchase this software. Some of the limitations of this study are the authenticity of the sale and lack of variation in the ages of participants. It is possible that the video sales were not authentic enough. Also, due to there being a very small range in the ages of participants it was not possible to make any inferences on how age affects the ability to detect deception. In this study age of the participants may have benefitted them, causing them to be better at detecting online scams. Younger people may have more experience with online scams causing them to be better at detecting them. Thus, a larger variety in ages of participants would allow for possible inferences on what causes one to be better at detecting online forms of deception. Also, I could have evaluated the level of experience participants had with computer-mediated conversations in order to make inferences on how that effected their perceptions of the sale. Future research should focus on varying the types of computer-mediated conversations, the ages of the participants and the experience participants have with computer-mediated conversations. Since I found a difference in detecting deception across two different communication forms further research could study how well we detect lies and if we detect them differently across other forms of computer mediated conversations such as instant messaging and texting. Finally, future research could also study the effect of the participants experience with

14 DETECTING DECEPTION 14 online communication in order to determine how that affects their ability to detect deception within this form of communication. In conclusion, a possible implication from the results of this study is that we are better at detecting the psycholinguistic cues to deception than the body language cues to deception. Since the -deceptive condition was better detected than the video-deceptive condition it can be assumed that the participants were able to detect the differences in the way deceptive statements are written. If this implication is true than this finding would be beneficial to many considering how much modern technology has influenced the way we converse today, as well as better understanding what people pay attention to in detecting deceit. The ability to detect psycholinguistic cues or lies in general through any form of communication not only would benefit someone in day to day conversations but protect other from falling for online scams.

15 DETECTING DECEPTION 15 Acknowledgements Thank you Dr. Paul Dupuis for your supervision, assistance and continuous support. Dr. Laurie Bloomfield and Dr. Dave Brodbeck for all of your guidance and support. Matthew Leonard for acting in the videos and constantly reassuring me. Jesse Merelaid for editing the videos and providing me with many lunches. My friends, Maddie Brodbeck and Chaney Finlayson for making this year so memorable, Sarah Devon for your editing, Ida-Marie Romano, Eden Alessandrini and Julia Mancuso for participating. Finally, my family, Mom, Dad, John and Nic for all of your love and encouragement.

16 DETECTING DECEPTION 16 References Bond, G. D. (2008). Deception detection expertise. Law and Human Behavior, 32(4), doi: /s z DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of Personality and Social Psychology, 70, doi: / DePaulo, B. M., Lanier, K., & Davis, T. (1983). Detecting the deceit of the motivated liar. Journal of Personality and Social Psychology, 45(5), doi: / DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003). Cues to deception. Psychological Bulletin, 129(1),74. doi: / DePaulo, B. M., Stone, J. I., & Lassiter, G. D. (1985). Telling ingratiating lies: Effects of target sex and target attractiveness on verbal and nonverbal deceptive success. Journal of Personality and Social Psychology, 48(5), doi: / Duran, N. D., Hall, C., McCarthy, P. M., & McNamara, D. S. (2010). The linguistic correlates of conversational deception: Comparing natural language processing technologies. Applied Psycholinguistics, 31(03), doi: /S Ekman, P., & O'Sullivan, M. (1991). Who can catch a liar? American Psychologist, 46(9), doi: / X

17 DETECTING DECEPTION 17 Ekman, P., Friesen, W. V., & O'Sullivan, M. (1988). Smiles when lying. Journal of Personality and Social Psychology, 54(3), doi: / Frank, M. G., & Ekman, P. (1997). The ability to detect deceit generalizes across different types of high-stake lies. Journal of Personality and Social Psychology, 72(6), doi: / Hancock, J., Birnholtz, J., Bazarova, N., Guillory, J., Perlin, J., & Amos, B. (2009). Butler lies: Awareness, deception and design. Human Factors in Computing Systems, 34(4) Naquin, C. E., Kurtzberg, T. R., & Belkin, L. Y. (2010). The finer points of lying online: versus pen and paper. Journal of Applied Psychology, 95(2), 387. doi: 1037/a Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29(5), doi: Porter, S., Woodworth, M., & Birt, A. R. (2000). Truth, lies, and videotape: An investigation of the ability of federal parole officers to detect deception. Law and Human Behavior, 24(6), doi: /A: Rowe, N.C. (2007). Finding logically consistent resource-deception plans for defense in cyberspace. Advanced Information Networking and Applications Workshops, 1(2), doi: /AINAW ten Brinke, L., & Porter, S. (2012). Cry me a river: Identifying the behavioral consequences of extremely high-stakes interpersonal deception. Law and Human Behavior, 36(6), doi: /h

18 DETECTING DECEPTION 18 Utz, S. (2005). Types of deception and underlying motivation what people think. Social Science Computer Review, 23(1), doi: / Van Swol, L. M., Braun, M. T., & Kolb, M. R. (2013). Deception, detection, demeanor, and truth bias in face-to-face and computer-mediated communication. Communication Research, 42(4), doi: é Vrij, A., Edward, K., Roberts, K. P., & Bull, R. (2000). Detecting deceit via analysis of verbal and nonverbal behavior. Journal of Nonverbal Behavior, 24(4), Vrij, A., Mann, S. A., Fisher, R. P., Leal, S., Milne, R., & Bull, R. (2008). Increasing cognitive load to facilitate lie detection: The benefit of recalling an event in reverse order. Law and Human Behavior, 32(3), doi: /s y Whitty, M. T., Buchanan, T., Joinson, A. N., & Meredith, A. (2012). Not all lies are spontaneous: An examination of deception across different modes of communication. Journal of the American Society for Information Science and Technology, 63(1), doi: /asi Youn, S., & McLeod, D. (2007). A comparative study for classification. Advances and Innovations in Systems, Computing Sciences and Software Engineering. 39(1), doi: / Zhou, L., Burgoon, J. K., Nunamaker, J. F., & Twitchell, D. (2004). Automating linguisticsbased cues for detecting deception in text-based asynchronous computer-mediated communications. Group decision and negotiation, 13(1),

19 DETECTING DECEPTION 19 Zimbler, M., & Feldman, R. S. (2011). Liar, liar, hard drive on fire: How media context affects lying behavior. Journal of Applied Social Psychology, 41(10),

20 DETECTING DECEPTION 20 Appendix A Figure 1. Mean levels of perceived honesty in the -deceptive condition are significantly lower than the video-deceptive condition. The -non-deceptive condition is perceived significantly more honest than the -deceptive condition.

21 DETECTING DECEPTION 21 Figure 2. Mean levels of perceived persuasion in the -deceptive condition are significantly lower than the mean levels of perceived persuasion in the video-deceptive condition. The non-deceptive condition is perceived significantly more persuasive than the -deceptive condition.