DETECTING DECEPTION 1
Detecting Deception in Face to Face and Computer Mediated Conversations
Victoria Foglia
Algoma University
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 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 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
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)
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, 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
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 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
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 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
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 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
DETECTING DECEPTION 9
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).
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, 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)
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, 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
DETECTING DECEPTION 12
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.
DETECTING DECEPTION 13
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
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 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.
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.
DETECTING DECEPTION 16
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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.
DETECTING DECEPTION 21
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.