Psychological targeting as an effective approach to
digital mass persuasion
S. C. Matz
, M. Kosinski
, G. Nave
, and D. J. Stillwell
Columbia Business School, Columbia University, New York City, NY 10027;
Graduate School of Business, Stanford University, Stanford, CA 94305;
Wharton School of Business, University of Pennsylvania, Philadelphia, PA 19104; and
Cambridge Judge Business School, University of Cambridge,
Cambridge, CB2 3EB, United Kingdom
Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved October 17, 2017 (received for review June 17, 2017)
People are exposed to persuasive communication across many
different contexts: Governments, companies, and political parties
use persuasive appeals to encourage people to eat healthier,
purchase a particular product, or vote for a specific candidate.
Laboratory studies show that such persuasive appeals are more
effective in influencing behavior when they are tailored to individ-
uals’unique psychological characteristics. However, the investiga-
tion of large-scale psychological persuasion in the real world has
been hindered by the questionnaire-based nature of psychological
assessment. Recent research, however, shows that people’spsycho-
logical characteristics can be accurately predicted from their digital
footprints, such as their Facebook Likes or Tweets. Capitalizing on
this form of psychological assessment from digital footprints, we
test the effects of psychological persuasion on people’sactualbe-
havior in an ecologically valid setting. In three field experiments
that reached over 3.5 million individuals with psychologically tai-
lored advertising, we find that matching the content of persuasive
appeals to individuals’psychological characteristics significantly al-
tered their behavior as measured by clicks and purchases. Persuasive
appeals that were matched to people’s extraversion or openness-to-
experience level resulted in up to 40% more clicks and up to 50%
more purchases than their mismatching or unpersonalized counter-
parts. Our findings suggest that the application of psychological
targeting makes it possible to influence the behavior of large
groups of people by tailoring persuasive appeals to the psycholog-
ical needs of the target audiences. We discuss both the potential
benefits of this method for helping individuals make better deci-
sions and the potential pitfalls related to manipulation and privacy.
digital mass communication
Persuasive mass communication is aimed at encouraging large
groups of people to believe and act on the communicator’s
viewpoint. It is used by governments to encourage healthy be-
haviors, by marketers to acquire and retain consumers, and by
political parties to mobilize the voting population. Research
suggests that persuasive communication is particularly effective
when tailored to people’s unique psychological characteristics
and motivations (1–5), an approach that we refer to as psycho-
logical persuasion. The proposition of this research is simple yet
powerful: What convinces one person to behave in a desired way
might not do so for another. For example, matching computer-
generated advice to participants’dominance level elicited higher
ratings of source credibility and increased the likelihood of
participants changing their initial opinions in response to the
advice (2). Similarly, participants’positive attitudes and purchase
intentions were stronger when the marketing message for a
mobile phone was tailored to their personality profile (4). While
these studies provide promising evidence for the effectiveness of
psychological persuasion, their validity is limited by the fact that
they were mainly conducted in small-scale, controlled laboratory
settings using self-report questionnaires. Self-reports are known
to be affected by a whole range of response biases (6), and there
are numerous reasons why people’s natural behavior might differ
from that displayed in the laboratory (7). Consequently, it is
questionable whether—andtowhatextent—these findings can be
generalized to the application of psychological persuasion in real-
world mass persuasion (see ref. 8 for initial evidence).
A likely explanation for the lack of ecologically valid research
in the context of psychological persuasion is the questionnaire-
based nature of psychological assessment. Whereas researchers
can ask participants to complete a psychological questionnaire in
the laboratory, it is unrealistic to expect millions of people to do
so before sending them persuasive messages online. Recent re-
search in the field of computational social sciences (9), however,
suggests that people’s psychological profiles can be accurately pre-
dicted from the digital footprints they leave with every step they
take online (10). For example, people’s personality profiles have
been predicted from personal websites (11), blogs (12), Twitter
messages (13), Facebook profiles (10, 14–16), and Instagram pic-
tures (17). This form of psychological assessment from digital foot-
prints makes it paramount to establish the extent to which behaviors
of large groups of people can be influenced through the application
of psychological mass persuasion—both in their own interest (e.g.,
by persuading them to eat healthier) and against their best interest
(e.g., by persuading them to gamble). We begin this endeavor in a
domain that is relatively uncontroversial from an ethical point of
view: consumer products.
Building on recent advancements in the assessment of psycho-
logical traits from digital footprints, this paper demonstrates the
effectiveness of psychological mass persuasion—that is, the ad-
aptation of persuasive appeals to the psychological characteris-
tics of large groups of individuals with the goal of influencing
their behavior. On the one hand, this form of psychological mass
persuasion could be used to help people make better decisions
and lead healthier and happier lives. On the other hand, it could
be used to covertly exploit weaknesses in their character and
persuade them to take action against their own best interest,
highlighting the potential need for policy interventions.
Author contributions: S.C.M. and M.K. designed research; S.C.M., M.K., and D.J.S. per-
formed research; S.C.M. analyzed data; and S.C.M., M.K., G.N., and D.J.S. wrote the paper.
Conflict of interest statement: D.J.S. received revenue as the owner of the myPersonality
Facebook application until it was discontinued in 2012. Revenue was received from dis-
playing ads within the application and charging for a premium personality tes t. The
revenue received by D.J.S. was unrelated to the studies presented in this paper, and there
will be no future revenue generated from the application. None of the authors received
any compensation for working on the marketing campaigns used to collect data for the
studies presented in this manuscript.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives Lic ense 4.0 (CC B Y-NC-ND) .
Data deposition: The data reported in this paper have been deposited in the Open Science
Framework (OSF), https://osf.io/srjv7/?view_only=90316b0c2e06420bbc3a1cc857a9e3c7.
To whom correspondence should be addressed. Email: firstname.lastname@example.org.
M.K. and D.J.S. contributed equally to this work.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1710966114 PNAS Early Edition
Capitalizing on the assessment of psychological traits from dig-
ital footprints, we conducted three real-world experiments that
reached more than 3.7 million people. Our experiments demon-
strate that targeting people with persuasive appeals tailored to
their psychological profiles can be used to influence their behavior
as measured by clicks and conversions. [Click-through rates
(CTRs) are a commonly used digital marketing metric that quan-
tifies the number of clicks relative to number of times the ad was
shown. Conversion rate is a marketing metric that reflects number
of conversions, such as app downloads or online store purchases,
relative to the number of times the ad was shown.] The experi-
ments were run using Facebook advertising, a typical behavioral
targeting platform. As of now, Facebook advertising does not allow
marketers to directly target users based on their psychological
traits. However, it does so indirectly by offering the possibility to
target users based on their Facebook Likes. (Facebook users can
like content such as Facebook pages, posts, or photos to express
their interest in a wide range of subjects, such as celebrities, poli-
ticians, books, products, brands, etc. Likes are therefore similar to
a wide range of other digital footprints—such as web-browsing
logs, purchase records, playlists, and many others. Hence, the
findings based on Facebook Likes are likely to generalize to digital
footprints employed by other advertising platforms.) For example,
if liking “socializing”on Facebook correlates with the personality
trait of extraversion and liking “stargate”goes hand in hand with
introversion, then targeting users associated with each of these
Likes allows one to target extraverted and introverted user seg-
ments (see SI Appendix for a validation of this method).
Studies 1 and 2 target individuals based on their psychological
traits of extraversion and openness-to-experience (18, 19). We
chose these two because they show strong associations with
Facebook Likes (14) and have been frequently investigated in the
context of consumer preferences and persuasive communication
(e.g., ref. 2). We extracted lists of Likes indicative of high and low
levels of each of these traits from the myPersonality.org database
(20). MyPersonality contains the Facebook Likes of millions of
users alongside their scores on the 100-item International Per-
sonality Item Pool (IPIP) questionnaire, a widely validated and
used measure of personality (19). We computed the average
personality trait levels for each Like and selected 10 Likes char-
acterized by the highest and lowest aggregate extraversion and
openness scores (i.e., target Likes). For example, the list of intro-
verted target Likes included Stargate and “Computers,”while the
list of extraverted target Likes contained “Making People Laugh”
or “Slightly Stoopid.”The list of target Likes for low openness
included “Farm Town”and “Uncle Kracker,”while the list for
high openness contained “Walking Life”and “Philosophy”(for
the full lists of target Likes, see SI Appendix, Tables S1 and S7).
Study 3 builds on the findings of studies 1–2 and shows how
psychological persuasion can be used in the context of predefined
Study 1 demonstrates the effects of psychological persuasion on
people’s purchasing behavior. We tailored the persuasive ad-
vertising messages for a UK-based beauty retailer to recipients’
extraversion, a personality trait reflecting the extent to which
people seek and enjoy company, excitement, and stimulation
(18). People scoring high on extraversion are described as en-
ergetic, active, talkative, sociable, outgoing, and enthusiastic;
people scoring low on extraversion are characterized as shy, re-
served, quiet, or withdrawn. Given the specific nature of the
product, we only targeted women. Fig. 1Adisplays 2 (out of 10)
ads aimed to appeal to women characterized by high versus low
extraversion (we refer to the personality of the audience an ad is
aimed at as ad personality). Using a 2 (Ad Personality: Introverted
vs. Extraverted) ×2 (Audience Personality: Extraverted vs. Intro-
verted) between-subjects, full-factorial design, we ran the Facebook
advertising campaign over the course of 7 d—that is, we placed
the ads on viewers’Facebook pages as they browsed freely.
Together, the campaign reached 3,129,993 users, attracted
10,346 clicks, and resulted in 390 purchases on the beauty re-
tailer’s website. Table 1 (study 1) provides a detailed overview of
the descriptive campaign statistics across ad sets (see SI Appendix
for more detailed breakdowns). We conducted hierarchical lo-
gistic regression analyses for clicks (click =1, no click =0) and
conversions (conversion =1, no conversion =0), using the au-
dience personality, the ad personality, and their two-way in-
teraction as predictors. [All of the results reported in this paper
hold when using linear probability models or when testing for
main treatment effects for congruent vs. incongruent conditions
using Chi-square tests, demonstrating the effects’robustness to
model specification (19, 21).] Users were more likely to purchase
after viewing an ad that matched their personality [Fig. 2;
Audience Personality ×Ad Personality interaction; B =0.90,
SE(B) =0.21, z=4.30, P<0.001]. These effects were robust
after controlling for age and its interactions with ad person-
ality. Averaged across the campaigns, users in the congruent
conditions were 1.54 times more likely to purchase from the online
store than users in the incongruent conditions, χ
(1) =17.72, odds
ratio (OR) =1.54 [1.25–1.90], P<0.001. There was no significant
interaction effect on clicks, χ
(1) <0.001, OR =1.0 [0.96–1.04],
Study 2 replicates and extends the findings of study 1 by tai-
loring the persuasive advertising messages for a crossword app to
recipients’level of openness, a personality trait reflecting the
extent to which people prefer novelty over convention (18).
People scoring high on openness are described as intellectually
curious, sensitive to beauty, individualistic, imaginative, and
unconventional. People scoring low on openness are traditional
and conservative and are likely to prefer the familiar over the
unusual. Using the same targeting approach and experimental
design as study 1, we created tailored advertising messages for
both high and low openness (Fig. 1B). The campaign was run on
Facebook, Instagram, and Audience Networks for 12 d.
The campaign reached 84,176 users, attracted 1,130 clicks, and
resulted in 500 app installs. Table 1 (study 2) provides a detailed
Beauty doesn’t have to shout
Aristoteles? The Seychelles? Unleash your
creativity and challenge your imagination with
an ulimited number of crossword puzzles!
Settle in with an all-time favorite! The
crossword puzzle that has challenged players
High Extraversion Low Extraversion
High Openness Low Openness
Dance like no one's watching
(but they totally are)
Fig. 1. Examples of ads aimed at audiences characterized by high and low
extraversion (A) as well as high and low openness (B). Fig. 1A,Left courtesy
of Caiaimage/Paul Bradbury/OJO+/Getty Images; Fig. 1A,Right courtesy of
Hybrid Images/Cultura/Getty Images.
www.pnas.org/cgi/doi/10.1073/pnas.1710966114 Matz et al.
overview of the descriptive campaign statistics across ad sets (see
SI Appendix for more detailed breakdowns). Using the same hi-
erarchical logistic regression analyses as in study 1, we found
significant interaction effects of audience personality and ad
personality on both clicks [B =0.58, SE(B) =0.14, z=4.31, P<
0.001] and conversions [Fig. 2; B =0.72, SE(B) =0.22, z=3.35,
P<0.001]. These effects were robust after controlling for age,
gender, and their interactions with ad personality. Averaged across
the campaigns, users in the congruent conditions were 1.38 times
more likely to click, χ
(1) =26.68, OR =1.38 [1.22–1.56], P<0.001,
and 1.31 times more likely to install the app, χ
(1) =8.34, OR =
1.31 [1.09–1.58], P=0.004, than users in the incongruent condi-
tions. As Fig. 2 illustrates, the significant interaction effect on
installs was mainly driven by the target audiences characterized as
low openness. While people scoring low on openness installed the
app significantly more often when presented with the matching
marketing message, χ
(1) =22.72, OR =0.43 [0.29–0.62], P<
0.001, there was no significant difference in install rates to
matching versus mismatching messages among people scoring
high on openness, χ
(1) =0.97, OR =0.89 [0.70–1.13],
Study 3 builds on the findings of studies 1 and 2 and shows how
psychological persuasion can be used in the context of predefined
audiences (e.g., when marketers have already established a behav-
ioral target group or when health promotions are targeted at a
specific subpopulation at risk). Promoting a bubble shooter game, we
Table 1. Descriptive statistics of studies 1–3 across ad sets
Condition Reach Clicks CTR Conv CR CPConv ROI
762,197 2,637 0.35% 121 0.016% £7.80 409%
791,270 2,426 0.31% 90 0.011% £10.41 300%
814,308 2,573 0.32% 117 0.014% £8.32 410%
762,218 2,710 0.36% 62 0.008% £15.93 219%
Total 3,129,993 10,346 0.33% 390 0.012% £9.85 334%
29,277 427 1.45% 140 0.48% $2.29 —
8,926 112 1.25% 37 0.41% $2.71 —
18,210 296 1.62% 174 0.96% $1.38 —
27,763 295 1.06% 149 0.53% $1.76 —
Total 84,176 1,130 1.34% 500 0.59% $1.85 —
Standard copy 324,770 1,830 0.56% 1,053 0.32% $3.21 —
209,480 1,537 0.73% 784 0.37% $2.91 —
Total 534,250 3,367 0.63% 1,837 0.34% $3.10 —
CPConv =cost per conversion, CR =conversion rate (installs/reach ×100), CTR =click-through rate (clicks/reach ×
100), ROI =return on Investment (profits/spending ×100).
Fig. 2. Interaction effects of audience and ad personality on conversion rates in study 1 (Left) and study 2 (Right).
Matz et al. PNAS Early Edition
followed the company’s preexisting behavioral targeting approach
and targeted the game at Facebook users who were connected with a
selected list of similar games (e.g., Farmville or Bubble Popp).
Mapping these behavioral target Likes onto those available within
the myPersonality database allowed us to identify the psychological
profile of this audience. Building on the finding that the target
audience was highly introverted (Ez=−0.25), we promoted the app
comparing the company’s standard persuasive advertising message
(“Ready? FIRE! Grab the latest puzzle shooter now! Intense ac-
tion and brain-bending puzzles!”) to a psychologically tailored one
(“Phew! Hard day? How about a puzzle to wind down with?”).
Both campaigns were run over the course of 7 d on Facebook.
Together, the campaign reached 534,250 users, attracted
3,173 clicks, and resulted in 1,837 app installs. Table 1 (study 3)
provides a detailed overview of the descriptive campaign statistics
across ad sets. Corresponding to our hypothesis, two χ
showed that the psychologically tailored ad set attracted signifi-
cantly more clicks, χ
(1) =58.66, OR =1.30 [1.22–1.40], P<0.001,
and installs, χ
(1) =9.16, OR =1.15 [1.05–1.27], P=0.002, than
the standard ad set. CTRs and conversion rates were 1.3 and
1.2 times higher when the persuasive advertising message was
tailored to the psychological profile of the preexisting behavioral
audience (see SI Appendix for evidence that this effect is not ex-
clusively due to the fact that the tailored advertising message was
generally more appealing).
The results of the three studies provide converging evidence for
the effectiveness of psychological targeting in the context of real-
life digital mass persuasion; tailoring persuasive appeals to the
psychological profiles of large groups of people allowed us to
influence their actual behaviors and choices. Given that we ap-
proximated people’s psychological profiles using a single Like
per person—instead of predicting individual profiles using peo-
ple’s full history of digital footprints (e.g., refs. 10 and 14)—our
findings represent a conservative estimate of the potential ef-
fectiveness of psychological mass persuasion in the field.
The effectiveness of large-scale psychological persuasion in
the digital environment heavily depends on the accuracy of
predicting psychological profiles from people’s digital footprints
(whether in the form of machine learning predictions from a
user’s behavioral history or single target Likes), and therefore,
this approach is not without limitations. First, the psychological
meaning of certain digital footprints might change over time,
making it necessary to continuously calibrate and update the
algorithm to sustain high accuracy. For example, liking the fan-
tasy TV show “Game of Thrones”might have been highly pre-
dictive of introversion when the series was first launched in 2011,
but its growing popularity might have made it less predictive over
time as its audience became more mainstream. As a rule of
thumb, one can say that the higher the face validity of the rela-
tionships between individual digital footprints and specific psy-
chological traits, the less likely it is that they will change (e.g., it is
unlikely that “socializing”will become any less predictive of ex-
traversion over time). Second, while the psychological assess-
ment from digital footprints makes it possible to profile large
groups of people without requiring them to complete a ques-
tionnaire, most algorithms are developed with questionnaires as the
gold standard and therefore retain some of the problems associated
with self-report measures (e.g., social desirability bias; ref. 22).
Additionally, our study has limitations that provide promising
avenues for future research. First, we focused on the two per-
sonality traits of extraversion and openness-to-experience.
Building on existing laboratory studies, future research should
empirically investigate whether and in which contexts other
psychological traits might prove to be more effective [e.g., need
for cognition (2) or regulatory focus (23)]. Second, we conducted
extreme group comparisons where we targeted people scoring
high or low on a given personality trait using a relatively narrow
and extreme set of Likes. While the additional analyses reported
in SI Appendix suggest that less extreme Likes still enable ac-
curate personality targeting, future research should establish
whether matching effects are linear throughout the scale and, if
not, where the boundaries of effective targeting lie.
The capacity to implement psychological mass persuasion in
the real world carries both opportunities and ethical challenges.
On the one hand, psychological persuasion could be used to help
individuals make better decisions and alleviate many of today’s
societal ills. For example, psychologically tailored health com-
munication is effective in changing behaviors among patients and
groups that are at risk (24, 25). Hence, targeting highly neurotic
individuals who display early signs of depression with materials
that offer them professional advice or guide them to self-help
literature might have a positive preventive impact on the well-
being of vulnerable members of society. On the other hand,
psychological persuasion might be used to exploit “weaknesses”
in a person’s character. It could, for instance, be applied to target
online casino advertisements at individuals who have psycho-
logical traits associated with pathological gambling (26). In fact,
recent media reports suggest that one of the 2016 US presi-
dential campaigns used psychological profiles of millions of US
citizens to suppress their votes and keep them away from the
ballots on election day (27). The veracity of this news story is
uncertain (28). However, it illustrates clearly how psychological
mass persuasion could be abused to manipulate people to behave
in ways that are neither in their best interest nor in the best in-
terest of society.
Similarly, the psychological targeting procedure described in
this manuscript challenges the extent to which existing and
proposed legislation can protect individual privacy in the digital
age. While previous research shows that having direct access to
an individual’s digital footprint makes it possible to accurately
predict intimate traits (10), the current study demonstrates that
such inferences can be made even without having direct access to
individuals’data. Although we used indirect group-level target-
ing in a way that was anonymous at the individual level and thus
preserved—rather than invaded—participants’privacy, the same
approach could also be used to reveal individuals’intimate traits
without their awareness. For example, a company could advertise
a link to a product or a questionnaire on Facebook, targeting
people who follow a Facebook Like that is highly predictive of
introversion. Simply following such a link reveals the trait to the
advertiser, without the individuals being aware that they have
exposed this information. To date, legislative approaches in the
US and Europe have focused on increasing the transparency of
how information is gathered and ensuring that consumers have a
mechanism to “opt out”of tracking (29). Crucially, none of the
measures currently in place or in discussion address the tech-
niques described in this paper: Our empirical experiments were
performed without collecting any individual-level information
whatsoever on our subjects yet revealed personal information
that many would consider deeply private. Consequently, current
approaches are ill equipped to address the potential abuse of
online information in the context of psychological targeting.
As more behavioral data are collected in real time, it will
become possible to put people’s stable psychological traits in a
situational context. For example, people’s mood and emotions
have been successfully assessed from spoken and written lan-
guage (30), video (31), or wearable devices and smartphone
sensor data (32). Given that people who are in a positive mood
use more heuristic—rather than systematic—information pro-
cessing and report more positive evaluations of people and
products (33), mood could indicate a critical time period for
psychological persuasion. Hence, extrapolating from what one
www.pnas.org/cgi/doi/10.1073/pnas.1710966114 Matz et al.
does to who one is is likely just the first step in a continuous
development of psychological mass persuasion.
Ethical approval was granted by the Department of Psychology Ethics
Committee at the University of Cambridge. Given that the group-level tar-
geting approach applied in studies 1–3 is 100% anonymous on the individual
level (all insights provided by Facebook are summary statistics at the level of
target groups), it is impossible to identify, interact with, and obtain consent
from individual participants.
Selection of target likes. The myPersonality dataset was collected via the
myPersonality Facebook app between 2007 and 2012 (20). Mostly free of charge,
the app allowed its users to take real psychometric tests. Among other validated
tests, users could choose between several versions of the IPIP questionnaire, an
established open-source measure of the five factor model of personality (19).
The five factor model has been shown to have excellent psychometric proper-
ties, including high reliability, convergent and discriminant validity, as well as
robust criterion validity when predicting real-life outcomes (18, 19). Users re-
ceived immediate feedback on their responses and were encouraged to grant
the application access to their personal profile and social network data.
Study 1 used a myPersonality subsample that contained 65,536 unique
Facebook Likes alongside the average personality profile of US-based users
connected to those Likes. The extraversion score of the Facebook Like “Lady
Gaga”, for example, was determined by averaging the z-standardized extra-
version scores of all users in the sample who had liked “Lady Gaga”.Tomax-
imize the reliability of personality profiles and limit the biases introduced by
differences in traits other than extraversion, we further restricted the dataset
described above to Likes followed by at least 400 users and only considered
Likes that were neutral with respect to the remaining four traits (jzj<0.20σ).
We finally selected those Likes with the highest (ð
aggregate extraversion scores (ð
zE=−0.20σ,n=8Þthat were available in the
Facebook Interest section at the time. SI Appendix,TableS1displays the Likes
used to target extraverted and introverted audiences alongside their person-
ality scores and sample sizes.
Advert design. Professional graphic designers created five ads aimed at in-
troverts and five ads aimed at extraverts by manipulating the language and
images used in the advert design. The five extraverted adverts were based on
trait descriptions such as “active, assertive, energetic, enthusiastic, outgoing
and talkative,”whereas the introverted adverts reflected trait descriptions
such as “quiet, reserved, shy, silent, and withdrawn”(18). All ads are dis-
played in SI Appendix, Fig. S2. We validated the manipulation of advert
designs by surveying 38 female judges (16 postgraduate students at the
University of Cambridge Psychology Department and 22 students with no
formal training in psychology). Independent ttests confirmed that both
psychologists and laymen perceived extraverted ads to be more extraverted
than the introverted ads—psychologists: t(196) =24.77, P<0.001, d=3.51
[3.07,3.96]; laymen: t(220) =15.30, P<0.001, d=2.05 [1.73,2.38].
Targeting procedure. We created “introverted”and “extraverted”market
segments by entering the selected target Likes displayed in SI Appendix
Table S1 into the “Interest”section of the Facebook advertising platform.
This procedure allowed us to limit the advert recipients to users who were
associated with at least one of our target Likes. At the time the study was
conducted, the Facebook advertising platform only allowed marketers to
enter multiple Likes with OR rather than AND statements. For example, an
extraverted target audience could be created with users who like Making
People Laugh OR “Meeting New People”but not with users who like both.
Therefore, the targeting approach pursued in this paper was based on the
minimum amount of information possible: one single Facebook Like per
person. In addition to the target Likes outlined above, the ad sets were
restricted to female UK residents ages 18–40.
Targeting procedure. Similar to study 1, we selected those Likes with the highest
zO=0.59σ,n=10Þand lowest aggregate openness scores ð
that were available in the Facebook Interest section at the time. In addition to
the targeting specifications outlined in the main manuscript, we restricted our
ad sets to US residents who were connected to a wireless network at the time
of seeing the ads to facilitate app installs. SI Appendix, Table S6 displays the
Likes used to target audiences low and high in openness alongside their per-
sonality scores and sample sizes.
Ad design. Professional graphic designers and copy editors created two adverts
tailored to high and low openness characteristics by manipulating the lan-
guage and images used in the advert design. While the low-openness advert
was based on trait descriptions such as “down to earth, traditional and
conservative,”the high-openness advert reflected trait descriptions such as
“intellectually curious, creative, imaginative, and unconventional”(17). We
validated the manipulation of advert designs by surveying 22 students at the
University of Cambridge (average age =23.5 y, 50% female). An in-
dependent ttest confirmed that participants perceived the high-openness
ad to be more open-minded than the low-openness ad, t(42) =–4.28, P<
0.001, d=1.29 [0.62, 1.96].
Targeting procedure. Following the company’s standard behavioral targeting
approach, ad sets were aimed at women aged 35 and above, living in the US,
who were connected to at least one of the mobile games on the company’s
behavioral target list. We further restricted our ad sets to US residents who
were connected to a wireless network at the time of seeing the ads, to fa-
cilitate app installs.
Ad design. Professional copy writers produced an introverted (personality-
tailored) ad text that we subsequently compared with the standard ad text
(the image that was used to advertise the app was kept constant). The two ad
versions are displayed in SI Appendix, Table S9. We validated the manipula-
tion of ad designs by surveying 22 students at the University of Cambridge
(average age =23.5 y, 50% female). An independent ttest confirmed that
participants perceived the personality-tailored ad to be more introverted than
the standard ad, t(41) =–2.77, P=0.008, d=0.84 [0.20, 1.48].
ACKNOWLEDGMENTS. We thank Vess Popov, Jochen Menges, Jon Jachimowicz,
Gabriella Harari, Sandrine Mueller, Youyou Wu, Maarten Bos, Michael Norton,
Nader Tavassoli, Joseph Sirgy, Moran Cerf, Pinar Yildirim, and Winter Mason for
their critical reading of earlier versions of the manuscript.
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