detecting deception in face to face and computer mediated...

DETECTING DECEPTION 1

Detecting Deception in Face to Face and Computer Mediated Conversations

Victoria Foglia

Algoma University


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 email conditions with either honest or deceptive content. After

viewing the video (face to face) or email sales pitch participants completed a questionnaire to

evaluate how honest the sale was perceived. I hypothesized that the email-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

email-deceptive condition was detected as deceptive significantly more than the face to face-

deceptive condition. Thus psycholinguistic cues may be better detected than body language cues

in deceptive conversations.

Keywords: deception detection, body language cues, psycholinguistic cues, deception


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


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)


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, emails, 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


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 computer-

mediated conversation their statements are less complex and include fewer personal pronouns

than honest statements. Zhou et al., (2004) found that when deceiving through computer-

mediated 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 computer-

mediated 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


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 emailed statement to a hand written

statement, found that those in the email 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 email of a sales pitch. These

videos and emails 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 email-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


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 email or viewed a video on a Dell Inspiron 13-Inch Laptop

(Chennai, India). The emails 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 email 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 email 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 email. The videos and emails 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


imagine that this was happening to them. The email participants were told that the email would

be a company trying to sell a product and to imagine this was in their own email inbox.

Participants were told to take as much time needed; allowing participants to view the

video again if needed or re-read the email. 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. email) sales pitches. An alpha level of p < .05 was set for all statistical

analyses.

Main Effects of Presentation (Email or Video)

There were no statistically significant main effects on honesty (F (1,62) =.004, p> .05, η2

=.00), persuasion (F (1,62) =.47, p = .49, η2

= .01), or likeliness to purchase the product, (F

(1,62) = .29, p = .60, η2

= .00) when comparing email (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 email condition, F

(1,62) =19.92, p<.001, η2

=.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).


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, η2 = .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 face-

deceptive conditions were perceived as less persuasive than the honest conditions F(1, 62) =

9.654, p<.003, η2 = .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) = 10.971, p<.002,

η2 = .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, η2 = .11

Interactions

There was a statistically significant interaction of deception and presentation on level of

perceived honesty, F(1,62) = 13.95, p = <.001, η2

= .18. Post-hoc analysis using Tukey’s HSD

revealed that the participants who viewed the deceptive email 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)


There was also a statistically significant interaction of level of deception on persuasion

and condition, F (1,62) = 6.388, p = .014, η2

=.09. Post-hoc analysis using Tukey’s HSD

revealed that the participants who viewed the deceptive email condition (M = 3.4375, SD =

1.41274) perceived their sale’s pitch as less persuasive than those in the deceptive video

condition (M = 4.3125, SD =1.88746) (See Figure 2 in Appendix A). Also the non-deceptive

email was perceived as less deceptive than the deceptive email 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 email-deceptive condition was

perceived as significantly less honest than the video-deceptive condition. Also, the email

conditions over all were better detected as having a difference between honest and deceptive

content. Participants were more accurate in detecting honesty in the email 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


may have been more difficult to detect due to the amount of time the participants had to analyze

the sale. Email 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 email 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

email than the deceptive video. Coinciding with previous findings it makes sense that the video

conditions were perceived as less persuasive than the email 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 email-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 email. Another possible

explanation as to why it was more difficult to detect deception in the email 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.


Although the deceptive email 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


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 email-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.


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.


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Appendix A

Figure 1. Mean levels of perceived honesty in the email-deceptive condition are significantly

lower than the video-deceptive condition. The email-non-deceptive condition is perceived

significantly more honest than the email-deceptive condition.


Figure 2. Mean levels of perceived persuasion in the email-deceptive condition are significantly

lower than the mean levels of perceived persuasion in the video-deceptive condition. The email-

non-deceptive condition is perceived significantly more persuasive than the email-deceptive

condition.

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