What a Tangled Web We Weave:
Lying Backfires in Location-Sharing Social Media
Xinru Page, Bart P. Knijnenburg, Alfred Kobsa
Department of Informatics, Donald Bren School of Information and Computer Sciences
University of California, Irvine, USA
{xpage, kobsa, bart.k}@uci.edu
ABSTRACT
Prior research shows that a root cause of many privacy
concerns in location-sharing social media is people’s desire
to preserve offline relationship boundaries. Other literature
recognizes lying as an everyday phenomenon that preserves
such relationship boundaries by facilitating smooth social
interactions. Combining these strands of research, one
might hypothesize that people with a predisposition to lie
would generally have lower privacy concerns since lying is
a means to preserve relationship boundaries. We tested this
hypothesis using structural equation modeling on data from
a survey administered nationwide (N=1532), and found that
for location-sharing, people with a high propensity to lie
actually have increased boundary preservation concerns as
well as increased privacy concerns. We explain these
findings using results from semi-structured interviews.
Author Keywords
Privacy; Lying; Boundary Preservation; Location Sharing;
Social Media; Structural Equation Modeling; Survey.
ACM Classification Keywords
H.5.m. Information interfaces and presentation (e.g., HCI):
Miscellaneous.
General Terms
Human Factors; Design; Measurement; Theory.
INTRODUCTION
Location-sharing services (LSS) are rapidly inundating the
technology landscape, in the guise of dedicated mobile
applications (e.g. Foursquare) or location-sharing features
incorporated into other social media (e.g. Facebook Places)
[67]. Although nearly half of all U.S. adults now have a
smart phone, only 10% have tried location-sharing services
[62]. And while 74% of smartphone owners are willing to
use location-based services such as location-based search,
only 18% have ever used geo-social services that share
location with other people [74]. As the technology is widely
available, many researchers blame various privacy concerns
for the slow adoption of LSS [67]. In prior work, Page et al.
identified the desire for relationship boundary preservation
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as one key driver of these location-sharing privacy concerns
[50]. They demonstrated that privacy concerns increase
when users are concerned about location-sharing technology changing the boundaries of their offline relationships.
For example, one interviewee explained how his girlfriend
didn’t want him to hang out with friends who she thought
were a bad influence. Since LSS would reveal his
encounters with these friends, he realized that sharing his
location with her could cause problems for their relationship. Once they broke up, he was fine sharing his location
with her since he was no longer accountable to her in their
new relationship.
In analyzing the qualitative portion of the data from Page et
al., we further noticed that some people admitted to lying as
a routine privacy management tactic. Lying occurred in
face-to-face as well as online interactions as a way to
maintain relationship boundaries. Lies were often used to
hide information or to avoid going out with someone in
favor of going out with others.
Some interviewees were confident that they could get away
with such lies, even when sharing their location: “I probably don’t have anything to hide [about my location], but
even if I do, I can just cover it up…Just make up some
story.” On the other hand, more interviewees expressed
concern about being caught in their offline lies if their
location were known:
[If] I’m calling in sick...to go somewhere like a three
day trip, [I’ll say I’m] sick Friday [and] sick Monday
to make it more plausible [be]cause no one gets better
over the weekend… I don't think I'd ever want to
reveal my location to my coworkers or my boss.
Here, coworkers would question whether this interviewee is
really sick if they could see that he is out of town rather
than at home. A quarter of the interviewees brought up
similar situations in which they had misled someone as to
their whereabouts and feared being caught lying. Examples
like these led us to wonder whether one’s propensity to lie
also plays a role in shaping one’s location-sharing privacy
concerns. On the one hand, people may be less privacy
concerned if they feel they can count on lying to facilitate
interactions. Indeed, much research characterizes lying as a
common practice used to facilitate daily social interactions
and to maintain relationship boundaries (e.g. [19]). Studies
of deception in computer-mediated interactions show that
lying also occurs as a boundary management tactic in
various mediated communications [27]. On the other hand,
people with a propensity to lie may be more privacy
concerned for fear of being caught at some point.
Moreover, the medium of communication affects one’s
ability to tell and detect lies (which may explain why the
rates of lying vary across media [28]). In LSS, which typically give an unbiased view of one’s location, these fears
and concerns of seasoned liars may thus be aggravated.
Not all interviewees who lie may have admitted it during
in-person interviews. Thus, to verify whether or not lying
propensity affects location-sharing privacy concerns, we
administered a nationwide anonymous survey that probes
on the propensity to lie alongside boundary preservation
and privacy concerns (N=1532). Using structural equation
modeling and a latent factor to represent Propensity to Lie,
we establish that this factor has a significant effect on a
large majority of privacy concerns. Interestingly, these
effects are all positive: one’s propensity to lie increases
one’s privacy concerns. Upon integrating Propensity to Lie
into the boundary preservation model identified in Page et
al. [50], this factor continues to have a positive direct effect
on privacy concerns. Additionally, we demonstrate that part
of this effect is mediated by boundary preservation
concerns (BPC) towards the use of location-sharing
services. In other words, Propensity to Lie increases
concerns about preserving one’s relationship boundaries in
location-sharing social media, and this in turn causes one to
be more privacy-concerned. These findings indicate that the
privacy management tactic of lying tends to backfire in
location-sharing social media.
RELATED WORK
Our work falls at the intersection of research on lies and
deception, and research on privacy in location-sharing
services. We survey related work in these areas.
Lies and Deception
Lying can be characterized as an act that “deliberately seeks
to create a false belief” in the other person [27]. Lies may
be enacted for self-interests (e.g. enhancing self-image,
avoiding embarrassment) or told in the interest of helping
another [20]. Some reasons for lying include interactional
and relational goals, relieving role strain, achieving highvalue outcomes or guarding against high-risk outcomes
[16]. Studies estimate that one quarter to one third of all
interpersonal interactions involve some sort of deception
[14, 28]. The vast majority of these lies are what researchers consider minor deceptions such as pretending to agree
with someone or making excuses to avoid interacting with
someone [27]. In everyday life, many people lie about a
diverse range of topics including their feelings, achievements or failures, plans, actions, and location [19]. This is
often for impression management reasons (e.g. a concept
popularized by Goffman [26] and further developed in
subsequent empirical research by various scholars [38, 39]).
Lies can come in many forms including outright deception,
equivocation, plausible deniability, and nondisclosure [47].
Many studies in ubiquitous computing encourage systems
to support white lies for plausible deniability [31, 40].
Researchers have even addressed this in contexts where
technology is always on [5]. Studies of SMS use have
identified diverse ways of creating ambiguity such as using
temporal, activity-based and location-based excuses [10].
Many scholars focus on how everyday lies are trivial and do
not generally cause much distress [19]. Some even emphasize how it is essential to maintaining close relationships
[47]: partners may collaborate to maintain lies or use ambiguity rather than full disclosure [20, 22]. However, other
research shows how an act of deception and subsequent
concealment may instill anxiety. Deceivers may experience
apprehension associated with the fear of being caught, or
guilt for violating social expectations [14]. Lying to a
partner can result in discomfort, less meaningful interactions, and can even cause the liar to trust their partner less
[8]. Lying has even been connected to physiological
indicators of stress, such as elevated heart rate [15], and
lying tendency linked to survival rates for various forms of
cancer [58]. These studies suggest that lying, as a coping
mechanism, can have negative physiological side effects.
Rather than being pre-planned, most lies unfold as a reaction during the course of an interaction [17]. Nonetheless,
psychological research shows that people differ in their
tendency to resort to lying in the face of a threat [23]. In
other words, although lying is triggered by a threatening
situation, the propensity of a person to tell a lie in that
situation is a disposition. Studies have also found that
people’s predisposition “to use their defensive maneuvers is
somewhat independent of the nature of the threatening
situation” [15]. This may help explain why people lie just
as much offline as online [8, 17, 28], and that the importance and content of lies [28] and the motivations behind
lies are similar across media [47].
However, the rates of lying vary across media, and
researchers have different explanations for this. Some
authors posit a Social Distance theory in which the liar
chooses less rich media to put social distance between
themselves and the recipient [19]. Others subscribe to
Media Richness theory which views lying as equivocal
work that benefits from the richest medium so that the liar
can personalize and closely monitor the interaction [28].
Hancock et al. criticized these theories for ignoring three
aspects: synchronicity, the ability to record the interaction,
and whether the communication partners are co-present
[28]. They argue that since most lies are unplanned, lying is
better carried out in media that are synchronous, hence
increasing the opportunities for deception to occur.
Moreover, media that can record an interaction allow
people to review the content, which deters lying. Lastly,
being physically co-present serves as a deterrent too, since
one cannot lie about the shared physical environment nor
anything observable by both parties.
Based on media richness theory, one would predict that
liars do not prefer LSS since it is a limited interaction that
does not allow personalization. Furthermore, it supports a
limited version of Hancock et al.’s physical co-presence in
that both parties can observe the physical location of the
person, which deters lying about it. Real-time sharing and
ability to record are also popular features of many LSS,
which could further deter lying. In all, it seems that for
people with a high propensity to lie, LSS is a contentious
medium. It inhibits their natural coping mechanism, which
impacts their ability to alleviate privacy concerns.
Although lie scales have been developed in prior literature,
this was mostly done to detect positive survey response
bias, i.e., the (situation-specific) act of lying as a means for
social conformity or impression management [23, 55, 59].
We instead focus on the (situation-independent) propensity
of people to lie as a way to manage their relationships.
There are also scholars who claim that existing scales confound multiple constructs [24, 55]. When applied to new
contexts, some scales have even been shown to assign high
lie scores to the most honest respondents since items
wrongly assume universal truths [44, 56]. Thus, we develop
a new Propensity to Lie scale that focuses on lying as a
relationship management tactic.
Location-Sharing
Location-sharing technologies come in many guises. They
range from automatic, continuous, real-time locationsharing (e.g. Google Latitude) to manual check-ins (e.g.
Gowalla, Foursquare), to authorized disclosures in response
to requests (e.g. HeyWAY), to location tags (e.g. GPS
coordinates appended to a Twitter tweet). There are a range
of methods to determine the location of a mobile device
including GPS, cell towers, wireless access points, and IP
address. Until recently, location-sharing technologies have
mainly been studied in the lab using experiments and
questionnaires. Privacy has often been a primary emphasis
in these studies. Researchers posit that the primary
determinants of location disclosure are who is requesting
one’s location, when, and why [18, 41, 48]. Recent research
has also focused on other aspects of who and found that
one’s subjective perception of closeness is enough to
predict disclosure in a given set of scenarios [73]. Others
focused on weighing disclosure benefits versus risks [67].
Some studies conduct field trials of location-sharing
prototypes [33]. Real-time disclosures to a limited group of
socially connected participants (e.g. co-workers, family,
friends) can be useful for being socially connected,
coordinating day-to-day activities, and making sure loved
ones are okay [6, 13, 57, 70]. Brown et al. point out that in
a household setting, location sharing creates a moral
component. Family members have to account for their
location and justify deviations [11]. To avoid disclosing too
much location information, prototypes support nondisclosure, ambiguity, and varying level-of-detail.
Some researchers have studied publically available location
sharing services. Humphreys described how Dodgeball, a
text-based application, helped people meet up at venues in
the city [32]. Page and Kobsa interviewed adopters and
non-adopters of Google Latitude and found that several
social forces shape people’s attitudes (norms, reciprocity,
symbolism) regardless of whether they had adopted the
technology [51]. Boesen et al. studied domestic use of
Google Latitude as a way of monitoring and making sure
children were where they should be [11]. They point out
that despite behavioral improvements, this ability to more
easily detect lies undermined the perceived trust in their
relationships. Our research concentrates on privacy concerns of users and non-users of publically available LSS.
Privacy
There are diverse conceptualizations of privacy [64]. Some
prominent definitions are Warren and Brandeis’s “Right to
be left alone” [71], Westin’s “claim of individuals, groups,
or institutions to determine for themselves when, how and
to what extent information about them is communicated to
others” [72], and Altman’s “interpersonal boundary regulation” [3]. These definitions depict privacy as the ability to
regulate physical, informational or social access.
Location-sharing research often focuses on informational
privacy and on sharing information with, or withholding it
from, the appropriate people [73]. Some researchers have
also found impression management concerns [66], worries
about being disturbed or disturbing others [33], and
apprehension towards potential stalkers [67].
Researchers highlight similar concerns in other social
media. Social network users are concerned about who sees
what, often because of the various social spheres (e.g.
family, work, friends) that intersect on their Facebook page
[7]. Users often limit who sees their profile to a defined
audience [43, 65]. Likewise, social media users worry about
self-presentation and how others’ postings will reflect on
them [68], sometimes getting annoyed or being overwhelmed by too much information from others [21].
Studies have also shown concerns for feeling compelled to
interact with others online [41].
Nonetheless, throughout the literature there are also counterexamples to these privacy concerns. Location-sharing in
families or close friend groups often engenders social
connection rather than privacy concern [6]. People disclose
enormous amounts of personal information in social
networks despite potential embarrassment or threats to their
employment, identity, or safety [1, 2, 46]. Individuals leave
intimate details open to strangers online [45] without
disclosing those details to their closest relationships [29].
Location-sharing services such as SinglesAroundMe.com or
Skout are even used to connect with strangers nearby.
Theories of privacy as boundary regulation may shed some
insight on these seeming contradictions. For example, Palen
and Dourish [53] extend Altman’s theory [3] of offline
privacy to the online world. Like in the real world, online
privacy is a regulation of various boundaries: one’s desired
level of privacy can be more open or more closed, and
one’s current level of privacy may or may not match what
is desired. For example, being cut off from interaction
could be a privacy violation that results in the person feeling isolated. Along similar lines, sharing more information
is not necessarily a privacy problem.
Page et al. [50] validated a model that explains a motivation
behind online boundary regulation in location-sharing
social media. They identified the desire for boundary
preservation of offline relationships as a root cause of
diverse location-sharing privacy concerns. In other words,
people’s privacy concerns are higher if they are concerned
that location-sharing social media would redefine their
offline relationships. They found a direct effect of boundary
preservation concern on eight privacy concerns that ranged
from informational to interactional to physical privacy (see
Table 1 for the specific concerns). Furthermore, they found
that increased social media use lowered location-sharing
boundary preservation concerns and thus had an indirect
effect on privacy concerns. This suggests that as people use
more social media, they may become better at, and/or less
concerned about, preserving their relationship boundaries.
In turn, this lowers their privacy concerns. In this paper, we
build upon Page et al.’s findings that boundary preservation
is a root cause of privacy concerns.
Integrating the Different Fields
Merging the main findings from these fields, a common
pattern emerges: People’s privacy concerns are caused by
their need to maintain offline relationship boundaries in an
online environment. Lying is a commonly used strategy to
maintain existing boundaries. However, some evidence
suggests that upholding a lie may be much harder in
location sharing systems than in other types of social media,
and may therefore increase, not decrease, privacy concerns.
We tested the veracity of these claims as part of a larger
study, other parts of which have been published elsewhere
[50, 51]. The current paper concentrates on how one’s
propensity to lie, a determinant not analyzed in previous
work, affects privacy and boundary preservation concerns
towards location-sharing social media.
STUDY DESIGN
The data for this analysis comes from a two-phase study
that comprises a larger effort to understand social media
and location-sharing attitudes and adoption. In the first
phase, 21 semi-structured interviews were conducted. In the
second phase, a nation-wide survey (N=1532) was administered. For the first time, this paper reports on items measuring a Propensity to Lie factor, the qualitative insights
that led to its construction, and new analysis that connects
lying propensity to boundary preservation and privacy
concerns. We thereby build upon findings by Page et al.
[50] that relate privacy to boundary preservation concerns.
Phase 1: Interview study
Past research points out that to understand human computer
interaction, it is just as important to study non-use of
technology as it is to study use [9, 60]. This is particularly
relevant for location-sharing technology. Despite widespread availability of smartphones and location-sharing services, adoption of these services has been low [62, 74].
Understanding concerns of non-users could shed light on
what is driving people away from these services. Little is
known about who is using social media and who is not [12].
We therefore interviewed users of a real-time locationsharing social medium, Google Latitude, as well as nonusers. We conducted 1 to 2 hour semi-structured interviews
of 7 location-sharing users, 7 who had explicitly chosen not
to adopt Latitude, 4 who had abandoned the technology,
and 3 who wanted to use it but could not (because the
service was not yet available for their phones). This allowed
us to study attitudes and privacy concerns that might
impede adoption, as well as those arising from usage after
adoption. Thus, our findings are applicable to the user
populations as well as those who do not use LSS.
The mean age of the subjects was 28 (ages 21 to mid 40’s)
and there were 4 females (one of each type of user/non
users). [51] reports more broadly on adoption barriers
uncovered by these interviews, while the current paper
focuses on propensity to lie (we described essential details
here, for complete interview procedures see [51]).
We used open-coding, purposeful sampling, and constant
comparison to generate grounded theory [25]. During coding phase, three types of lying emerged (see next section for
items). These lies were sometimes the source of privacy
concerns and other times the solution to a potential privacy
breach. “Non-lying” also emerged, and was either associated with increased concerns (because these interviewees
refused to lie to cover up a sticky situation), or lowered
privacy concerns (because non-lying interviewees had
nothing to hide). This led us to wonder whether one’s
propensity to lie impacts one’s privacy concerns and how.
Below we report (for the first time) on the development of
items reflecting the three types of lying that we encountered
in our interviews.
Phase 2: Nation-wide Survey
Based on qualitative insight derived from grounded theory
analysis of those interviews, we developed and administered a geographically balanced survey across the U.S.
Items used in the current analysis are a subset of the survey
questions. In spring of 2011, we posted the survey in 13
Craigslist cities and invited participation from anyone 18
and older who had resided in the U.S. for at least 5 years (as
has been done in other privacy studies to control for culture
[54]). Past research shows that liars tend to be aware of
their behavior and produce fairly accurate self-reports [34].
Also, anonymous online surveys have been found to elicit
more honest responses for deviant behavior (such as lying)
than methods where a researcher is present [30]. We did not
require location-sharing or other social media use as a precondition for participation. This way, our results would be
relevant for understanding both user and non-user attitudes.
The first 50 participants each received a $10 Amazon.com
gift card and the first 1000 were entered into a raffle for two
$100 gift cards. After removing surveys that failed two or
more of seven quality checks (e.g. reverse-coded items,
unrealistic completion times), we were left with 1532 valid
responses. To make the sample more representative of the
U.S. population, we normalized responses from each of the
U.S. census-defined geographic regions by their respective
metropolitan population sizes. 24.0% of the respondents
had tried location-sharing services, 79.0% used social
media at least weekly, 54.0% owned smart phones, 59.7%
had an unlimited data plan, 66.6% were female, the education level was in line with the U.S. Internet population,
and the average age was 35.5 years (range 18-73).
what was reported in [50], performing exploratory factor
analysis (EFA) on the original sample (N=1021). The items
did not constitute a well-fitting factor model since the
average inter-item correlation was 0.26, whereas 0.30 is a
minimal requirement for factorization [35]. Furthermore,
the average variance extracted (AVE) was 0.311, below the
0.5 cut-off for factor creation [35]. These results indicate
that there is a relationship between the items, but that the
correlation is too low for the items to form a robust
measurement scale. In fact, all inter-item correlations were
1
low except C7,C8 (r=0.647) and C3,C5 (r=0.516) and
these pairs lacked face validity to be considered as a
construct. Therefore, items C1-C8 are treated as separate,
2
but correlated, indicators.
BPC
The valid responses were randomly split into thirds. In [50],
Page et al. used the first sample for theory construction, and
the second sample for its validation. For our analysis, we
pool these two samples to test our hypotheses. We refer to
this as the original sample (N=1021). Additionally, we use
the third untouched sample for the validation of our results.
We refer to this as the hold-out sample (N=511). Throughout the paper we cross-validate our results between the
original and the hold-out samples, and only report on results
that are significant in both samples. This ensures the robustness of our results. The reported outcomes are based on the
full sample (N=1532).
C1
I am bothered that others share so much information
with me
C2
I am concerned that if I share too much information,
I would bother others
C3
I worry that I might share information with more
people than I intend to
C4
I worry about feeling compelled to interact with
others online
C5
I worry that what my friends share will reflect badly
on me
C6
I’m worried about knowing the social etiquette of
using LSS (e.g. who to friend, what to share, etc.)
C7
I’m concerned about being able to control who sees
my location
C8
I’m worried others would join me at an inappropriate time if I share my location
Privacy Concern Items
The survey included items that probed on privacy concerns.
As reported in [50], items were developed to represent eight
of the most common online privacy concerns expressed by
interviewees (C1-C8). These ranged from informational
(e.g. disclosing private information) to social (e.g.
impression management) to physical (e.g. physical access)
privacy concerns. Another item was created to represent
concern for preserving relationship boundaries (BPC) when
using location-sharing services. In [50], Page et al. showed
that BPC causes C1-C8. Table 1 lists these items (see [50]
for a detailed explanation of the construction of each item).
Location-sharing concern items (BPC, C6-C8) were worded
in a way that can be answered by everyone, including nonusers. Participants responded to these items on 7-point
scales whose values are -3 (Disagree Strongly), -2
(Disagree Moderately), -1 (Disagree Slightly), 0 (Neutral),
+1 (Agree Slightly), +2 (Agree Moderately) and +3 (Agree
Strongly). Since previous research shows that people’s
location-sharing attitudes and concerns are intertwined with
attitudes towards social media in general [52], items C1-C5
refer to attitudes towards social media that were indicative
of location-sharing concerns.
To check whether privacy concerns C1-C8 could be treated
as one or more privacy concern factors, we went beyond
I’m worried LSS will change my relationship with
others
Table 1. Questionnaire items for boundary preservation and
privacy concerns. LSS=location-sharing services
Propensity to Lie
The survey included items about one’s propensity to lie
(whether lying offline or online), which have not been
previously reported. Here we describe the factor construction and validation. Because lying is a sensitive topic,
we decided to represent propensity to lie as a latent factor
measured by multiple indicators. For content validity, we
drew on the types of lying found from the qualitative data.
In analyzing the phase 1 interviews, we found that those
who spoke of lying often discussed two or more of the
following forms of lying: 1) I lie, 2) Everyone lies, and 3)
1
Correlation of < 0.4 is considered low, 0.4 to < 0.7 is
medium, and 0.7 or more is high
2
Our modeling tool, Mplus, treats single indicators as
single-item latent factors.
Pretending there are technological limitations as a form of
plausible deniability (e.g., pretending to have a bad network
connection as an excuse for not answering or not being
visible in a location-sharing service). Table 2 shows the
items developed to represent each form of lying. We
describe the development of each item in detail.
Everyone
Lies
Everyone lies to get out of doing something
I Lie
Sometimes I tell a lie to avoid something
(e.g. I tell someone I don’t have time to go
out, but then go out with someone else)
Technology
Lie
Sometimes I use technical difficulties as an
excuse (e.g. I pretend I had bad cell phone
reception)
Table 2. Questionnaire items for Propensity to Lie factor
Everyone Lies
Several interviewees thought of lying as a typical behavior
used by everyone. One interviewee elaborated on how he
hid his actions by removing tags that identified him in Facebook pictures: “I don't know if my friends [remove tags]. I
mean, no one wants to get caught in a lie. I'm sure they
must do it.” Asserting that others must untag pictures to
avoid getting caught in a lie betrays an underlying assumption: Others are lying as well. Because others are lying, they
inevitably will run into similar situations. Consequently, we
developed an item to reflect the belief that others also lie.
This item also had a purpose beyond being an indicator for
propensity to lie. We displayed this item before the other
lying items in order to frame the behavior of lying as
commonplace. Framing is a common technique to increase
the likelihood that respondents will be comfortable admitting to what could be perceived as deviant behavior [61].
I Tell Lies
Several interviewees mentioned lying as an offline privacy
management tactic to avoid going out with someone:
I do tell somebody [I’m] not available for something. I
tell them that I’m not feeling too good – I can’t come
out. But I’m actually going out with another group of
people that they don’t necessarily get along with.
This is an example of the most common lie among our
interviewees. Namely, lying to avoid going out with or
being around someone. In general, people used lying as an
avoidance strategy, whether it be avoiding people in the
physical space or avoiding having to converse online. This
is in line with other studies that have found that people lie
as a way to guard against unwelcome conversations [69] or
to extract themselves from an ongoing interaction [27]. This
item therefore probes how much one lies as an avoidance
strategy. We purposefully framed this and other items in the
context of avoidance motives rather than just asking about
lying in general. This was in line with motivations des-
cribed by interviewees and also increased the social acceptability of our questions.
Blaming Technology
As a cover up, people often accused faulty technology. An
interviewee explained how he could blame technology if
location-sharing ever displayed him as being at an unsavory
location: “Oh, there's a library across the street. Latitude
must have been a few feet off.” Making excuses by blaming
technology may be a more socially acceptable type of lie
since it exploits a sense of ambiguity. This is consistent
with past research indicating that technology is a common
scapegoat to create plausible deniability [4, 5, 10, 31, 40].
Several interviewees were not comfortable lying outright,
but content to allege faulty technology. To capture this form
of lying, we developed an item focused on blaming
technology.
Respondents rated the three lying items (Everyone Lies, I
Lie, Blame Technology) on the same 7-point scale used for
privacy concern items (see previous section). Using the
survey results, we established the reliability and validity of
the Propensity to Lie factor. Because the reliability and
validity indices are consistent in both the original and the
hold-out samples, we report the statistics for the full
sample. The factor loadings are significant at the p < 0.001
level and had the following values: Everyone Lies = 0.647,
I Lie = 0.883, Technology Lie = 0.700. The Cronbach’s
alpha of 0.74 indicates a satisfactory level of internal
reliability. The Average variance extracted (AVE) of 0.565
3
indicates acceptable convergent validity.
Controls
The survey had several control questions, including
location-sharing use, social media use (a composite of how
frequently they use various social media including
Facebook, Twitter, Instant Messaging, etc.), smart phone
ownership, and having data and/or texting plans. Demographics included age, gender, education, geographical
location, marital status and parental status.
ANALYSIS
For this analysis, we created a structural model that analyzes the effect of Propensity to Lie on privacy concerns.
Then we proceed to integrate Propensity to Lie into the full
boundary preservation model presented by Page et al. [50]
For both analyses, we use weighted least squares estimator
(WLSMV) with ordered categorical indicators as our esti4
mation method. This technique does not assume normality
of outcome variables. We checked for outliers (+/-3
3
A loading > 0.7 is considered high, and a loading > 0.4 is
considered medium. Accepted cutoff values for the
reliability indices: Cronbach’s alpha > 0.7, AVE > 0.5.
4
Mplus uses probit regression to estimate ordered
categorical outcomes.
standard deviations) and used bivariate scatterplots to check
for homoscedasticity. All data was within normal ranges.
Initially, we modeled LSS users separately from non-users
to see if their attitudes and concerns differ. We found no
difference and hence combined these data for our analysis.
Thus, a single model accounts for both users and non-users.
Lying Leads to Privacy Concerns
In [50], Page et al. showed that boundary preservation
concern (BPC) is a root cause of eight identified privacy
concerns (C1-C8). To test whether participants’ propensity
to lie could also be seen as a root cause of these eight
concerns, we created a structural equation model in which
propensity to lie (Lie) causes these concerns. We fit this
model on both samples, removed effects that were not
significant in either of the two samples, and ran the
resulting model on the full sample. This model had a good
fit (χ2 (12) = 50.151, p < .001; CFI = 0.991; RMSEA =
5
0.046, 90% CI: [0.033, 0.059]; WRMSR = 0.558) . Table 3
summarizes the effects in this model.
Concern
Effect of
Lie  C1-8
I am bothered that others share so much
information with me (C1)
0.183***
I am concerned that if I share too much
information, I would bother others (C2)
no effect
I worry that I might share information with
more people than I intend to (C3)
0.215***
I worry about feeling compelled to interact
with others online (C4)
0.235***
I worry that what my friends share will
reflect badly on me (C5)
0.259***
I’m worried about knowing the social
etiquette of using LSS (C6)
no effect
I’m concerned about being able to control
who sees my location (C7)
0.116***
I’m worried others would join me at an
inappropriate time if I share my location(C8)
0.161***
Table 3. The standardized effect of Propensity to Lie on the
eight different concerns (C1-C8). *** indicates p < 0.001
5
A significant chi-square means that the model shows
some misfit. However, this statistic is known to be very
sensitive in large samples such as ours. As an alternative,
one may consider other fit indices, which have the
following accepted cut-off values: CFI > 0.96, RMSEA <
0.05 (within [0.00, 0.10]), WRMSR < 0.95.
Lying Integrated with Boundary Preservation Model
We subsequently integrated Propensity to Lie into the structural model established in [50], in which social media use
decreases boundary preservation concern, and in turn boundary preservation concern increases all eight of the other
concerns (refer back to Privacy in the Related Works
section for a more detailed description). Specifically, we
modeled Propensity to Lie as a cause of both boundary
preservation concerns (BPC) and the eight individual
privacy concerns (C1-C8).
Although it is possible to conceive a model in which
boundary preservation concerns cause an increase in the
acts of lying, research has shown that the propensity to lie
can be considered a stable predisposition [15, 24] rather
than a situation-dependent activity. This warrants our
choice of the causal direction “Lie  BPC”.
Would the effect of propensity to lie on boundary preservation concerns be positive or negative? First of all, the
results of Lie  C1-C8 suggest that propensity to lie will
increase concerns. This is in line with research results that
lying can cause significant physiological and mental stress
or anxiety [14, 15]; this stress could manifest itself as boundary preservation concerns and privacy concerns in social
media. On the other hand, lying can facilitate maintaining
relationship boundaries [47], which would mean that
propensity to lie decreases boundary preservation concerns.
We tested the integrated model on both the original and the
hold-out samples and confirmed that it is consistent
between the two samples. We also found that age has a
consistent negative effect on the propensity to lie, as well as
on social media use. We ran the resulting model on the full
sample. This model has an acceptable fit (χ2(42) = 175.059,
p < .001; CFI = 0.976; RMSEA = 0.046, 90% CI: [0.039,
0.053]; WRMSR = 0.989). Moreover, all effects in the
model are highly significant (see Figure 1).
The model shows that lying propensity increases not only
various online privacy concerns (C1-C8), but also concerns
that location sharing will affect one’s ability to preserve
relationship boundaries (BPC). This makes boundary
preservation concern a partial mediator that amplifies the
effect of lying on privacy concerns.
The resulting model also shows that there is a direct effect
of age on social media use and propensity to lie. Interestingly, the indirect effects of age on boundary preservation
through social media use and propensity to lie are of similar
magnitude but in opposite directions and hence end up
canceling each other out. The final outcome is that there is
no total effect of age on boundary concerns (p=0.617).
Consequently, age only affects the eight privacy concerns
through Propensity to Lie.
As an aside, we did not find any effects of lying on social
media use or vice versa.
Social media
use
-0.234 ***
-0.251 ***
Age
Boundary
preservation
concern
0.145 ***
-0.352 ***
Propensity
to lie
Concern
BPC
Lie
C1
0.221 ***
0.159 ***
C2
0.237 ***
no effect
C3
0.239 ***
0.190 ***
C4
0.294 ***
0.200 ***
C5
0.269 ***
0.215 ***
C6
0.464 ***
no effect
C7
0.269 ***
0.102 **
C8
0.318 ***
0.119 ***
Figure 1. Propensity to lie and boundary preservation concern (BPC) are root causes of online privacy concerns. Effects of Lie and
BPC on C1-8 are tabulated, e.g. the effect of BPC on C1 is 0.221*** (standardized effect sizes, *** indicates p < 0.001, ** p < 0.01)
DISCUSSION
Lying is often depicted as a common privacy management
tactic in offline relationships to mitigate concerns and to
maintain relationship boundaries. Although the propensity
to lie might alleviate boundary preservation concerns in
other online technologies [10, 27], our model shows that in
location-sharing social media, lying is likely to backfire and
to actually increase privacy concerns. These concerns arise
directly from Propensity to Lie, but also from liars’
increased concerns about preserving relationship boundaries. Several interviewees recognized the risks in mixing
lying with location-sharing social media:
I don't want to tell someone, “I can't go out with you –
I'm at home,” and then I go out. That's dangerous.
[There have] been situations where I tell someone I'm
not going out tonight and then photos will be posted of
me going out… One of my friends was just caught. At
happy hour, she told one of her friends she wasn't
going to go out. And we went out and that friend
happened to show up at the same restaurant.
Location-sharing services change the premise for carrying
out these lies by erasing the physical barriers and making
location easily accessible.
Some location-sharing services can be used to increase the
plausibility of a lie because they allow people to set an incorrect location. However, our qualitative data suggests that
this can be problematic as well. Many interviewees would
not consider using software to lie, and were disturbed by
others who did. One interviewee forsakes using technology
with friends who are not sharing their true location:
So the point about Latitude is to know where your
friends are… And so if I see a person toy with an
application, I just won’t pay attention to them on it. So
it’s like my confidence in how well they use it.… And
so my confidence in the way [person’s name] uses this
application…was [broken]. So from now I just won’t
pay attention to where he is.
Another interviewee explained how he wasn’t interested in
Google Latitude because it allows people to set or type an
incorrect location:
People can just say they're wherever they want to say
they are… It changes my perception of using Latitude
as opposed to showing your accurate location all the
time no matter what the users wanted…[It] becomes
this unreliable source of information, becomes this
messy thing.
Similarly, some interviewees even perceived the practice of
blocking others from seeing location to be a deceptive act.
A number of these interviewees avoided location-sharing
technology and minimized participation in social media in
order to keep themselves from falling into situations where
lying would be a tempting solution. Several seemed to take
an ethical stance against lying. These interviewees serve as
a reminder that features to support lying will not appeal to
everyone and could even drive users away. A value-based
approach [36] would help designers understand core values
such as honesty that should be supported in their design.
For those individuals motivated to avoid bad situations
(rather than by ethics), several studies conclude that
location-sharing technology should support plausible deniability by obscuring or hiding location [4, 5, 33]. For
instance, one interviewee who would not tell blatant lies nor
use technology to do so, was keeping his relationship a
secret from his parents: “I would visit her but wouldn't tell
my parents. I'd say I'm going to Atlanta to visit my friends.
I didn't really fly to Atlanta. Well, I DID fly to Atlanta, but
it was a lay over on the way to another place.” If he had
used location-sharing at that earlier point in time, “I would
have manually set it to ‘break’.” In other words, he wanted
to maintain a partial truth by freezing his location at Atlanta
or going offline and using technology as a scapegoat.
However, we question the extent to which this is possible.
Obscuring location or ignoring location requests at certain
moments or for certain lengths of time may violate relationship boundary expectations. For example, an employee who
calls in sick for four days and whose location is unavailable
the entire period might raise suspicions. Worse yet, even
honest instances of being offline can then be mistaken as
deception. Furthermore, as location-sharing services become more accurate and connectivity improves, and as
users learn the available features, blaming technology
becomes less of an option. Designers will have to better
understand offline relationship dynamics in order to understand what constitutes acceptable interactions online.
Our results did not find any relationship between social
media use and lying. This suggests that Propensity to Lie
does not affect frequency of social media use in general, or
vice versa. However, lying propensity does seem to affect
concerns towards specific social media. In location-sharing
we find that lying propensity increases concerns, but studies
suggest that in other technologies it might decrease
concerns (e.g. Hancock et al. describe the benefits of butler
lies [27]). It may be that this difference is what causes the
relatively slow adoption of location sharing services. We
are actively investigating whether this is indeed the case.
We also did not find a consistent significant effect of lying
on the concern for bothering others with too much
information (C2, see Table 3). Conceptually, one can
quickly see why this may be so. The Propensity to Lie
factor emphasizes lying as an avoidance tactic for selfinterested goals. Thus, it is aimed at limiting communication for the benefit of the liar. This in turn does not result in
sharing too much information, nor should self-preservation
goals necessarily lead to concern for others. So lying should
not impact concerns about bothering others.
Lying also did not have a significant effect on the concern
for not knowing the social etiquette of LSS (C6). Upon
closer inspection, we see that all of the concerns, except C6,
are tangible outcomes that can result from lying. Knowing
social etiquette (e.g. who to friend, what to share, etc.) has
to do with defining what is considered an infraction or problem in the first place, and this may not be affected by
one’s lies.
In contrast to prior deception studies that focused on more
homogenous samples (e.g. [10, 27]), we found that the
propensity to lie significantly decreases with age. This in
turn causes older participants to have lower privacy
concerns. We see this play out in some of our interviews.
One man explained how, as a youth, he would be especially
anxious to control what his parents could see but “not so
much anymore” because he is not lying to his parents about
where he is. Despite common conceptions that teens don’t
care about privacy, research has shown that teenagers
actually are privacy concerned, especially in social media
context [42]. Our findings shed light on one reason younger
users could be more concerned: they tend to lie more.
Our model results were consistent with the hypothesis that
lying propensity impacts boundary preservation concern.
This direction of causality is also in line with existing
theory [15]. Yet, because this is a descriptive study, it is
conceivable that there is a mutual effect between lying
propensity and boundary preservation concerns and thus,
boundary preservation concerns could affect lying
propensity over time. However, reversing the direction of
causality in our model does not yield significant results.
Hence, we are unable to claim a mutual effect, and leave
the question to future research.
This paper uncovered lying as one privacy management
tactic that affects online privacy and boundary preservation
concerns. There may be other traditional social practices
that are in conflict with modern location-sharing social
media. This work is a first step in exploring the interplay of
location-sharing social media, our social practices, and our
social relationships. Although the focus of our study was on
location-sharing technology, the propensity to lie factor was
framed as general practice (offline or online) and many of
the privacy concern items were about social media in
general, not just location-sharing social media. Thus, we
suspect that lying may directly increase online privacy concerns in a broader range of social media than just location
sharing systems. Future research should investigate lying
and boundary preservation concerns in other social media.
Longitudinal studies could also shed light on how privacypreserving social practices instantiate and evolve in new
social media.
CONCLUSION
Our findings bring to the forefront Sir Walter Scott’s
admonition, “Oh! What a tangled web we weave when first
we practice to deceive!” Our results indicate that lying may
complicate matters even more in the online social world.
This begs the question: Should technology pave the way to
more transparent interactions by making it more difficult to
lie? Would this force people to tell the truth, or would
people avoid the technology? Or on the other extreme,
should technology provide fertile grounds for facilitating
lies and aim at reducing people’s resulting concerns? In the
middle of describing a detailed feature idea making it
“convenient” to lie, one interviewee stopped himself and
acknowledged this powerful, and perhaps disturbing, influence of technology:
There are applications which force people to lie, force
people to do wrong things. And I may want to take my
words back. If you're writing it down, just don't write
it down because I don't want to be the reason for such
a bad feature.
Technology can influence social dynamics by facilitating
certain practices and inhibiting others [37]. If one regards
lying as undesirable, do designers have an obligation to
protect people from their own propensity to lie? The answer
to this may depend on one’s ethical viewpoint. Our recommendation is for technology designers and researchers to
think about and study the social and moral impact their
technology will have and does have once it is deployed.
Technological changes can affect social practice whether
we design for it or not [49]. If we understand how social
practices are evolving around technology, perhaps the
social and moral impacts of new technology do not have to
go completely unchecked. In the realm of privacy concerns,
our paper provides a first step in that direction.
ACKNOWLEDGMENTS
This work was funded by NSF grants IIS-0308277, IIS0808783 and CNS-0831526, and by Disney Research. We
thank Bonnie Nardi and the anonymous CSCW reviewers
for their constructive feedback on prior drafts.
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