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Abstract

Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality — people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at 'big data' scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research.
Language-based
personality:
a
new
approach
to
personality
in
a
digital
world
$
Ryan
L
Boyd
and
James
W
Pennebaker
Personality
is
typically
defined
as
the
consistent
set
of
traits,
attitudes,
emotions,
and
behaviors
that
people
have.
For
several
decades,
a
majority
of
researchers
have
tacitly
agreed
that
the
gold
standard
for
measuring
personality
was
with
self-report
questionnaires.
Surveys
are
fast,
inexpensive,
and
display
beautiful
psychometric
properties.
A
considerable
problem
with
this
method,
however,
is
that
self-reports
reflect
only
one
aspect
of
personality
people’s
explicit
theories
of
what
they
think
they
are
like.
We
propose
a
complementary
model
that
draws
on
a
big
data
solution:
the
analysis
of
the
words
people
use.
Language
use
is
relatively
reliable
over
time,
internally
consistent,
and
differs
considerably
between
people.
Language-based
measures
of
personality
can
be
useful
for
capturing/modeling
lower-level
personality
processes
that
are
more
closely
associated
with
important
objective
behavioral
outcomes
than
traditional
personality
measures.
Additionally,
the
increasing
availability
of
language
data
and
advances
in
both
statistical
methods
and
technological
power
are
rapidly
creating
new
opportunities
for
the
study
of
personality
at
‘big
data’
scale.
Such
opportunities
allow
researchers
to
not
only
better
understand
the
fundamental
nature
of
personality,
but
at
a
scale
never
before
imagined
in
psychological
research.
Address
The
University
of
Texas
at
Austin,
Department
of
Psychology,
United
States
Corresponding
authors:
Boyd,
Ryan
L
(ryanboyd@utexas.edu),
Pennebaker,
James
W
(pennebaker@utexas.edu)
Current
Opinion
in
Behavioral
Sciences
2017,
18:63 68
This
review
comes
from
a
themed
issue
on
Big
data
in
the
behavioral
sciences
Edited
by
Michal
Kosinski
and
Tara
Behrend
http://dx.doi.org/10.1016/j.cobeha.2017.07.017
2352 1546/ã
2017
Elsevier
Ltd.
All
rights
reserved.
People
differ
dramatically
in
the
ways
they
think,
feel,
and
behave
in
general,
forming
the
basis
for
what
we
refer
to
as
personality.
Going
back
to
the
ancient
Greeks,
formal
thinking
about
personality
has
relied
on
different
methods
to
measure
and
explain
personality.
Classically,
Galen
posited
four
general
temperaments
sanguine,
phlegmatic,
melancholic,
and
choleric
based
on
his
observations
of
biology
and
the
theories
of
Hippocrates
[1].
Freud
[2]
revolutionized
the
broader
discussion
about
personality
by
arguing
that
inborn
temperament
and
early
experiences
shaped
what
people
were
like
later
in
life.
Temperament
researchers
focused
on
the
activity
levels
and
emotionality
of
infants
to
posit
the
likely
genetic
and
biological
bases
of
individual
differences
[3].
Others,
such
as
Gordon
Allport
[4]
pointed
to
the
enduring
and
stable
behavioral
styles
that
people
possessed
including
the
ways
they
walked,
gestured,
or
chewed
gum.
Even
the
most
nuanced
behaviors
revealed
people’s
basic
characteristics.
Not
until
the
advent
of
modern
social
science
did
psy-
chologists
begin
to
focus
on
the
careful
measurement
of
personality
[5 7].
In
the
last
quarter
of
the
20th
century,
the
trait
approach
emerged
that
effectively
defined
mod-
ern
personality
theory,
ushering
in
detailed
factor
models
of
the
construct
[8,9].
The
new
trait
approach
energized
the
field
of
personality
research,
in
part
because
it
leaned
heavily
on
self-reports
of
participants’
self-concepts
for
understanding
their
general
personality
characteristics.
This
was
a
profound
development
in
personality
research:
widespread
adoption
of
self-reports
meant
that
it
was
now
possible
to
have
very
large
groups
of
people
complete
extensive
personality
scales
rather
than
relying
on
more
time-intensive
and
resource-intensive
approaches.
Paired
with
advances
in
statistical
and
other
computational
methods,
the
adoption
of
self-report
scales
resulted
in
new
ways
of
studying
the
domains
and
correlates
of
traits.
Self-report
questionnaires
can
provide
rich
information
about
peoples’
conscious,
explicit
self-concepts.
How-
ever,
most
personality
experts
have
harbored
occasional
doubts
about
the
degree
to
which
people’s
self-reported
traits
reflect
who
they
really
are
[10].
For
example,
to
what
degree
do
self-theories
map
onto
their
actual
beha-
viors?
Across
thousands
of
studies,
we
know
that
self-
reports
correlate
nicely
with
other
self-reports
from
the
same
people,
yet
often
show
lackluster
overlap
with
more
objective
measures
that
presumably
capture
the
same
underlying
traits.
Researchers
consistently
find
that
widely-used
and
well-validated
self-report
measures
are
insufficient
when
it
comes
to
forming
an
accurate
under-
standing
of
even
basic
human
patterns
such
as
workplace
behaviors
[11],
physical
activity
[12],
and
expressions
of
happiness
[13

]
or
other
emotional
states
[14].
$
Preparation
of
this
manuscript
was
aided
by
grants
from
the
National
Institutes
of
Health
[5R01GM112697 02],
John
Templeton
Foundation
[#48503],
and
the
National
Science
Foundation
[IIS 1344257].
Available
online
at
www.sciencedirect.com
ScienceDirect
www.sciencedirect.com
Current
Opinion
in
Behavioral
Sciences
2017,
18:63 68
Are
we
thinking
about
personality
in
the
right
way?
Are
people’s
self-theories
the
appropriate
gold
standard
for
assessing
personality?
If
not
self-reports,
does
a
gold
standard
exist?
As
we
outline
below,
we
must
move
beyond
the
gold
standard
way
of
thinking.
Self-reports
reflect
one
dimension
of
personality,
while
nervous
sys-
tem
activity
may
serve
as
another,
genetic
factors
may
be
the
basis
of
a
third,
and
so
on.
Beyond
self-reports
and
biological
markers,
recent
research
has
demonstrated
that
a
powerful
reflection
of
personality
can
be
gleaned
from
the
words
people
use
in
everyday
life.
As
an
increasing
number
of
studies
dem-
onstrate,
the
ways
in
which
people
use
words
is
reliable
over
time,
internally
consistent,
predictive
of
a
wide
range
of
behaviors
and
even
biological
activity,
and
varies
considerably
from
person
to
person.
Language,
then,
is
yet
another
fundamental
dimension
of
personality.
Of
great
benefit
to
researchers,
and
unlike
other
standard
personality
markers,
people
do
not
need
to
complete
questionnaires
or
submit
to
invasive
blood
or
genetic
tests
in
order
to
provide
useful
personality
data
in
the
form
of
language.
Language
and
personality
in
the
land
of
big
data
Over
half
of
the
planet’s
population
uses
the
internet,
and
over
80%
of
people
in
developed
countries
are
internet
users
[15].
Every
minute,
more
than
350
000
tweets
are
posted
to
Twitter,
approximately
3
million
Facebook
posts
are
shared,
4
million
Google
queries
are
submitted,
and
over
170
million
e-mails
are
sent
[16,17].
In
more
human
terms,
the
average
office
worker
sees
over
120
e-
mails
per
day
[18],
the
typical
teen
in
the
United
States
sends
over
60
text
messages
per
day
from
their
mobile
phones
[19]
and
the
average
Facebook
user
writes
25
com-
ments
daily
[20].
In
short,
the
amount
of
language
data
generated
by
humans
on
a
minute-by-minute
basis
around
the
world
is
nothing
short
of
staggering.
As
with
the
unprecedented
availability
of
human-gener-
ated
data,
the
field
of
psychology
has
witnessed
a
recent
cascade
in
psychometric
techniques
that
are
well-suited
to
a
big
data
research
culture.
Of
the
more
recent
psy-
chological
assessment
methods,
perhaps
the
most
acces-
sible
and
refined
to
date
is
that
of
automated
language
analysis,
which
is
currently
experiencing
rapid
adoption
and
growth
across
a
wide
range
of
academic
fields.
His-
torically,
psychologists
have
long
believed
that
a
person’s
words
can
be
revealing
of
deeper,
meaningful
psycholog-
ical
constructs
[21 23].
For
example,
classical
research
on
motivation
found
that
the
individual’s
personal
strivings,
such
as
the
needs
for
affiliation
and
achievement,
were
manifest
in
their
everyday
words
[24],
and
it
has
long
been
believed
that
linguistic
cues
can
be
used
to
identify
different
states
of
consciousness
[25].
However,
the
mod-
ern
rejuvenation
of
language
research
in
the
field
of
personality
psychology
has
been
primarily
driven
by
the
adoption
of
modern
statistical
methods
and
techno-
logical
innovations,
such
as
the
boom
of
personal
com-
puting
power
and
data
accessibility
[26].
Unlike
most
classical
research
on
language
and
psychol-
ogy,
which
typically
treated
linguistic
measures
as
indi-
cators
of
a
person’s
transient
mental
state
[14,27],
several
key
studies
were
conducted
early
on
in
the
current
language
analysis
renaissance
which
demonstrated
that
the
properties
of
language-based
psychological
measures
behave
in
much
the
same
way
as
traditional
measures
of
personality.
For
example,
Pennebaker
and
King
[28]
explored
the
psychometric
properties
of
language
as
a
psychological
measure,
finding
that
the
majority
of
mea-
sures
provided
by
the
Linguistic
Inquiry
and
Word
Count
method
[29]
exhibited
all
of
the
hallmarks
of
a
standard
individual
differences
measure:
test retest
reliability,
external
validity,
and
internal
consistency.
A
considerable
amount
research
within
the
LIWC
domain
has
expanded
these
initial
findings,
establishing
the
word-counting
par-
adigm
as
a
robust
tool
for
measuring
stable
individual
differences
[30
,31,32].
In
the
modern
research
world,
where
psychologically-
relevant
data
is
available
in
great
abundance,
psychomet-
ric
techniques
like
language
analysis
allows
researchers
to
indirectly
probe
and
better
understand
how
lower
level
psychological
processes
function
and
interact
to
manifest
in
the
form
of
personality
in
the
real
world.
In
other
words,
techniques
such
as
language
analysis
are
particularly
well-
suited
to
the
proximal
measurement
the
lower
level
processes
that
cohere
to
form
personality,
especially
in
relation
to
traditional
self-report
measures.
Countless
patterns
of
attention,
behaviors,
and
emotions
are
deeply
embedded
in
a
person’s
language
[31],
and
psychologists
now
have
access
to
an
ever-growing
number
of
methods
to
extract
these
patterns
for
deeper
study.
Given
the
modern
surge
of
language
data,
as
well
as
methods
for
extracting
psychological
information
from
such
data,
a
logical
next
step
for
social
scientists
is
to
begin
benefiting
from
the
trait-like
qualities
of
language-
based
measures
in
psychological
research.
In
the
current
climate
of
the
‘big
data’
revolution,
many
of
the
logistical
properties
for
which
self-report
measures
are
often
lauded
ring
even
truer
for
language-based
measures
of
personal-
ity.
While
self-reports
are
relatively
easy
to
collect
com-
pared
to
other
measures
such
as
physiological
data,
lan-
guage
analysis
often
relies
on
data
that
already
exists.
Moreover,
pre-existing
digital
data
from
the
web,
smart
phones,
and
social
media
are
inherently
ecologically
valid,
having
originated
from
thoughts
and
behaviors
that
occur
in
the
absence
of
researcher
intervention.
It
is
vital
to
note
that
the
analysis
of
language
for
person-
ality
research
can
be
performed
at
scale
in
nearly
any
64
Big
data
in
the
behavioral
sciences
Current
Opinion
in
Behavioral
Sciences
2017,
18:63 68
www.sciencedirect.com
context
where
language
data
exists,
bypassing
the
need
to
recruit
and
collect
constrained
self-report
measures.
While
it
is
a
harrowing
and
costly
task
to
collect
self-
reported
neuroticism
from
thousands
of
people,
neuroti-
cism’s
underlying
processes
can
be
measured
in
millions
of
Reddit
users’
language
in
an
afternoon.
As
the
number
of
people
who
use
digital
technology
continues
to
increase
around
the
world,
along
with
the
trails
of
psy-
chologically
actionable
data
that
are
left
behind,
it
is
imperative
that
new
methods
be
adopted
that
are
able
to
make
good
use
of
this
data
by
capitalizing
on
the
growing
technological
infrastructure
(e.g.
text
messages,
institutional
databases,
and
social
media).
In
failing
to
adapt
to
the
new
big
data
world,
many
personality
researchers
will
be
resigned
solely
to
the
study
self-
theories,
and
only
in
samples
that
are
directly
accessible
and
motivated
to
fill
out
questionnaires.
The
language-based
measurement
of
personality
In
contrast
to
most
lexical
theories
of
personality,
which
posit
that
descriptions
of
important
personality
traits
are
embedded
within
language
in
general
[33 35],
it
is
implicit
to
current
psychological
language
analysis
research
that
several
characteristics
of
someone’s
person-
ality
are
embedded
in
their
unique
patterns
of
language
use.
However,
both
approaches
generally
assume
a
taxo-
nomical
structure
of
personality
that
is,
personality
as
a
broad,
abstract
construct
is
composed
of
lower-level
psy-
chological
processes
and
behavioral
tendencies
[36].
The
taxonomical
structure
of
personality,
both
within
a
general
personality
psychology
framework
as
well
as
within
a
language-based
personality
framework,
is
central
to
performing
meaningful
personality
psychology
re-
search.
For
example,
the
underlying
components
of
extra-
version
have
been
well-established
to
date
across
various
methodologies:
relative
to
introverts,
extraverts
generally
engage
in
more
social
activity
[37],
experience
greater
positive
affect
and
well-being
[38],
and
are
reactive
to
external
stimulation
[39 41].
Indeed,
language-based
personality
research
consistently
and
successfully
finds
the
same
basic
underpinning
processes
of
extraversion.
Relative
to
their
introverted
counterparts,
extraverts
tend
to
use
higher
rates
of
social
words,
words
indicative
of
positive
emotions,
and
language
that
is
representative
of
an
external
focused
(i.e.
fewer
1st
person
singular
pro-
nouns)
[42].
The
two
dominant
modes
of
language–
personality
research
Predicting
self-report
measures
Contemporary
language
analysis
research
typically
adopts
1
of
2
overarching
approaches.
In
the
first
approach,
researchers
seek
to
build
language-based
models
of
per-
sonality
that
approximate
the
data
found
in
ubiquitous
self-report
based
studies.
In
simple
terms,
one
of
the
most
common
approaches
to
language personality
research
involves
using
linguistic
measures
to
estimate
how
people
fill
out
personality
self-report
questionnaires.
For
example,
Yarkoni
[43]
explored
LIWC-based
and
word-based
statistical
models
of
personality
in
bloggers’
texts
to
predict
their
self-reported
Big
5
scores
(both
overall
scores
as
well
as
facet-level
measures).
Similarly,
Schwartz
et
al.
[44]
adopted
an
‘open-vocabulary’
approach
to
predicting
Big
5
self-report
measures
from
Facebook
status
updates.
Such
an
approach
is
currently
the
dominant
paradigm
in
language personality
research
and
is
primarily
driven
by
research
teams
that
lean
heavily
on
a
predictive
modeling
background,
crossing
boundaries
from
information
sciences
to
social
sciences
[45,46
,47,48,49
].
Under
the
‘estimate
self-reports
using
language’
model
of
study,
researchers
are
ultimately
seeking
to
maximize
their
account
of
variance
in
questionnaire
scores
via
lexical
features,
and
their
studies
often
yield
impressive
results.
Nevertheless,
it
is
conceptually
problematic
to
treat
personality
as
measured
by
self-report
question-
naires
as
‘ground
truth’
scores
for
personality
research.
In
part,
well-established
limitations
of
such
measures,
such
as
self-knowledge
constraints
and
response
biases
[50],
restrict
these
language-based
models
of
personality
to
self-theories.
More
important
is
that
aggregate
mea-
sures
of
personality
are
distal
abstractions
of
the
very
behaviors,
feelings,
and
thoughts
that
we
seek
to
under-
stand.
In
estimating
peoples’
self-reported
neuroticism
from
language,
for
example,
questionnaire
scores
are
treated
as
a
‘real’
thing
that
can
be
objectively
measured
rather
than
a
collection
of
supporting
psychological
pro-
cesses.
In
other
words,
this
paradigm
treats
self-reported
personality
as
a
‘gold
standard’
while
failing
to
acknowl-
edge
the
flaws
that
they
acquire
as
a
part
of
the
oper-
ationalization
and
data
collection
process.
Measuring
personality
processes
It
is
more
consistent
with
modern
theories
of
personality,
then,
when
the
use
of
language
in
personality
research
adopts
a
relatively
more
atomic
demeanor
to
measuring
personality
processes,
rather
than
predicting
traits
as
a
generalized
whole.
This
alternative
approach
to
lan-
guage-based
research
in
psychology,
while
not
new,
has
begun
to
see
increasing
adoption
among
researchers
in
social
and
personality
psychology.
Recent
research
has
found
that
many
basic
psychological
tendencies
that
give
rise
to
broader
individual
differences
are
deeply
embedded
in
language
use.
For
example,
linguistic
measures
of
various
cognitive
patterns
are
par-
ticularly
predictive
of
objective
outcomes
such
as
college
grades
[51,52],
life
expectancy
[53,54],
and
resilience
to
trauma
[55,56].
Moreover,
language-based
measures
of
personality
processes
have
reliable,
trait-like
properties
[28,30
].
Further
still,
such
measures
are
often
more
Language-based
personality
Boyd
and
Pennebaker
65
www.sciencedirect.com
Current
Opinion
in
Behavioral
Sciences
2017,
18:63 68
predictive
of
specific,
concrete
behaviors
than
traditional
self-report
measures,
providing
both
stronger
and
broader
predictive
coverage
[57

,58].
Finally,
such
low-level
measures
of
personality
processes
may
still
be
aggregated
into
higher-level
abstractions
for
generalized
predictive
purposes,
much
like
the
work
of
Yarkoni
[43],
Schultheiss
[59],
Schwartz
et
al.
[44],
and
others.
Particularly
vital
to
personality
psychology
as
a
field,
language-based
measures
of
personality
processes
allow
researchers
to
better
understand
the
psychological
features
that
underpin
personality,
thereby
addressing
classical
criticisms
of
trait
research
being
primarily
descriptive
rather
than
explanatory
[60,61].
For
example,
Carey
et
al.
[62

]
extensively
debunked
the
widespread
mis-
conception
that
narcissists
are
prone
to
disproportionate
self-focus
by
measuring
rates
of
1st
person
singular
pro-
noun
use.
In
their
work,
the
researchers
noted
that
other
psychological
processes
related
to
a
broader
social
orien-
tations,
including
interaction
style
(e.g.
disagreeable
social
behaviors)
and
disinhibition
(e.g.
impulsivity
and
sensation
seeking),
are
more
central
pillars
of
the
narcis-
sistic
personality
[63,64].
Similarly,
basic
motivational
processes
that
underpin
traits
such
as
political
ideology,
mindfulness,
values,
social
personality,
and
motivation
have
been
identified
and
integrated
into
theoretical
understandings
of
the
constructs
[13

,65 69]
some-
thing
that
is
simply
not
possible
with
an
approach
that
relies
purely
on
self-report
estimation.
Conclusions
While
we
have
known
for
some
time
that
self-report
questionnaires
suffer
from
critical
limitations,
personality
psychologists
have
been
slow
to
adopt
alternatives.
As
personality
and
social
psychology
have
become
increas-
ingly
integrated
[70],
research
from
labs
all
over
the
world
have
found
that
a
person’s
words
say
more
than
what
meets
the
eyes
(or
ears).
Thousands
of
published
studies
have
demonstrated
that
language,
a
powerfully
social
component
of
human
behavior,
contains
deeply
embed-
ded
and
hidden
information
about
not
just
social
pro-
cesses,
but
also
psychological
functioning,
attentional
processes,
behaviors,
and
other
important
psychological
constructs
that
are
absolutely
paramount
to
our
under-
standing
of
personality.
Moreover,
new
methods
of
quan-
tifying
psychological
processes
from
language
are
constantly
being
created.
The
abundance
of
language-
based
methods
designed
to
improve
our
understanding
of
psychological
processes
are
particularly
relevant
and
applicable
to
the
modern
digital
age,
where
human-
generated
data
is
created
a
rate
far
beyond
what
we
can
currently
process.
The
future
of
personality
research
will
continue
to
inno-
vate
with
new
methods
to
capture
the
psychological
processes
that
are
embedded
in
the
massive
digital
trail
of
human
data.
Language
analysis
for
personality
research
is
a
low-hanging
fruit
that
is
ripe
for
the
picking.
In
the
coming
years,
the
integration
of
objective,
multimodal
data
such
as
quantified
images,
language,
audio,
mobile
sensor
data,
and
internet
behaviors
into
more
refined
measures
of
personality
and
its
supporting
psychological
processes
are
likely
to
occur.
Given
that
the
road
has
already
begun
to
be
paved
in
words,
however,
there
has
never
been
a
better
time
to
transition
away
from
self-
reports
and
toward
language
analysis
as
a
foundational
method
in
personality
research.
Conflict
of
interest
statement
Nothing
declared.
References
and
recommended
reading
Papers
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within
the
period
of
review,
have
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highlighted
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interest
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