During
the
2020
US
presidential
election,
conspiracy
theories
about
large-scale
voter
fraud
were
widely
circulated
on
social
media
platforms.
Given
their
scale,
persistence,
and
impact,
it
is
critically
important
to
understand
mechanisms
that
caused
these
spread.
The
aim
of
this
study
was
investigate
whether
retweet
frequencies
among
proponents
Twitter
during
election
are
consistent
with
frequency
bias
and/or
content
bias.
To
do
this,
we
conducted
generative
inference
using
an
agent-based
model
cultural
transmission
VoterFraud2020
dataset.
results
show
observed
distribution
a
strong
causing
users
preferentially
tweets
negative
emotional
valence.
Frequency
information
appears
be
largely
irrelevant
future
count.
Follower
count
strongly
predicts
in
simpler
linear
model,
but
does
not
appear
drive
overall
after
temporal
dynamics
accounted
for.
Future
studies
could
apply
our
methodology
comparative
framework
assess
for
valence
theory
messages
differs
from
other
forms
media.
Abstract
Social
media
users
tend
to
produce
content
that
contains
more
positive
than
negative
emotional
language.
However,
language
is
likely
be
shared.
To
understand
why,
research
has
thus
far
focused
on
psychological
processes
associated
with
tweets'
content.
In
the
current
study,
we
investigate
if
producer
influences
extent
which
their
More
specifically,
focus
a
group
of
are
central
diffusion
social
media—public
figures.
We
found
an
increase
in
negativity
was
stronger
sharing
for
public
figures
compared
ordinary
users.
This
effect
explained
by
two
user
characteristics,
number
followers
and
strength
ties
proportion
political
tweets.
The
results
shed
light
whose
most
viral,
allowing
future
develop
interventions
aimed
at
mitigating
overexposure
Psychological Science in the Public Interest,
Год журнала:
2024,
Номер
25(2), С. 41 - 91
Опубликована: Окт. 1, 2024
Five
years
after
the
beginning
of
COVID
pandemic,
one
thing
is
clear:
The
East
Asian
countries
Japan,
Taiwan,
and
South
Korea
outperformed
United
States
in
responding
to
controlling
outbreak
deadly
virus.
Although
multiple
factors
likely
contributed
this
disparity,
we
propose
that
culturally
linked
psychological
defaults
(“cultural
defaults”)
pervade
these
contexts
also
played
a
role.
Cultural
are
commonsense,
rational,
taken-for-granted
ways
thinking,
feeling,
acting.
In
States,
cultural
include
optimism
uniqueness,
single
cause,
high
arousal,
influence
control,
personal
choice
self-regulation,
promotion.
Korea,
realism
similarity,
causes,
low
waiting
adjusting,
social
regulation,
prevention.
article,
(a)
synthesize
decades
empirical
research
supporting
unmarked
defaults;
(b)
illustrate
how
they
were
evident
announcements
speeches
high-level
government
organizational
decision
makers
as
addressed
existential
questions
posed
by
including
“Will
it
happen
me/us?”
“What
happening?”
should
I/we
do?”
“How
live
now?”;
(c)
show
similarities
between
different
national
responses
pandemic.
goal
integrate
some
voluminous
literature
psychology
on
variation
Asia
particularly
relevant
pandemic
emphasize
crucial
practical
significance
meaning-making
behavior
during
crisis.
We
provide
guidelines
for
might
take
into
account
design
policies
address
current
future
novel
complex
threats,
pandemics,
emerging
technologies,
climate
change.
Media Psychology,
Год журнала:
2022,
Номер
26(4), С. 460 - 479
Опубликована: Дек. 26, 2022
Negativity
bias
predicts
that
individuals
will
attend
to,
learn
from,
and
prioritize
negative
news
more
than
positive
news.
Drawing
from
the
addiction
components
model,
this
cross-sectional
study
conceptualized
measured
“doomscrolling”
as
excessive
thoughts,
urges,
or
behaviors
related
to
consumption
of
on
social
media
platforms.
Participants
were
a
convenience
sample
(N
=
747)
Iranian
users.
The
8-item,
unidimensional
Social
Media
Doomscrolling
Scale
showed
excellent
psychometric
properties.
Men
likely
women
report
doomscrolling.
Most
respondents
reported
arousal
following
was
negatively
associated
with
psychological
wellbeing,
satisfaction
life,
motivation
avoid
unhealthy
behaviors.
positively
impulsivity,
engagement
in
risky
behaviors,
depression,
future
anxiety.
Results
suggest
doomscrolling
is
an
arousing
activity
has
potential
exacerbate
worrisome
thoughts
about
future,
breed
feelings
hopelessness,
cultivate
appetite
for
risk,
stifle
health
consciousness.
Revista Latina de Comunicación Social,
Год журнала:
2024,
Номер
82, С. 1 - 30
Опубликована: Май 21, 2024
Introducción:
El
estudio
aborda
la
problemática
de
los
deepfakes
y
su
efecto
en
percepción
pública,
destacando
evolución
desde
prácticas
antiguas
manipulación
visual
hasta
convertirse
herramientas
avanzadas
construcción
realidades
alternativas,
especialmente
lesivas
para
las
mujeres.
uso
imágenes
como
una
forma
ataque
o
represión
va
a
llevar
considerar
esta
práctica
parte
violencias
contra
mujeres
política.
Metodología:
Este
carácter
exploratorio
adentrarse
el
manipuladas
políticas.
Un
objetivo
que
se
diseñó
metodología
múltiple:
entrevistas
con
políticas,
análisis
falsas
auditadas
por
verificadores
información
búsqueda
simple
plataformas
contenidos
adultos.
Resultados:
Se
pone
manifiesto
un
empleo
fake
atacar
desprestigiar
Dichas
son
fundamentalmente
cheapfakes.
Discusión:
La
limitada
sofisticación
políticas
permite
detección
falsificaciones
audiencia
crítica.
Conclusiones:
Las
conclusiones
resaltan
necesidad
educación
mediática
combatir
desinformación
sesgo
confirmación.
investigación
enfatiza
violencia
género
política,
donde
utilizan
silenciar
desacreditar
mujeres,
perpetuando
así
misoginia
mantenimiento
estructuras
poder
existentes.
Human Behavior and Emerging Technologies,
Год журнала:
2023,
Номер
2023, С. 1 - 16
Опубликована: Март 9, 2023
The
emotional
impact
of
the
COVID-19
pandemic
and
ensuing
social
restrictions
has
been
profound,
with
widespread
negative
effects
on
mental
health.
We
made
use
natural
language
processing
large-scale
Twitter
data
to
explore
this
in
depth,
identifying
emotions
news
content
user
reactions
it,
how
these
evolved
over
course
pandemic.
focused
major
UK
channels,
constructing
a
dataset
COVID-related
tweets
(tweets
from
organisations)
comments
response
these,
covering
Jan
2020
April
2021.
Natural
was
used
analyse
topics
levels
anger,
joy,
optimism,
sadness.
Overall,
sadness
most
prevalent
emotion
tweets,
but
seen
decline
timeframe
under
study.
In
contrast,
amongst
anger
overall
emotion.
Time
epochs
were
defined
according
time
restrictions,
some
interesting
emerged
regarding
these.
Further,
correlation
analysis
revealed
significant
positive
correlations
between
expressed
response,
across
all
channels
studied.
Results
provide
unique
insight
onto
dominant
present
as
unfolded.
Correspondence
tweet
highlights
potential
effect
online
users
points
strategies
combat
health
Humanities and Social Sciences Communications,
Год журнала:
2023,
Номер
10(1)
Опубликована: Сен. 14, 2023
Abstract
During
the
2020
US
presidential
election,
conspiracy
theories
about
large-scale
voter
fraud
were
widely
circulated
on
social
media
platforms.
Given
their
scale,
persistence,
and
impact,
it
is
critically
important
to
understand
mechanisms
that
caused
these
spread.
The
aim
of
this
preregistered
study
was
investigate
whether
retweet
frequencies
among
proponents
Twitter
during
election
are
consistent
with
frequency
bias
and/or
content
bias.
To
do
this,
we
conducted
generative
inference
using
an
agent-based
model
cultural
transmission
VoterFraud2020
dataset.
results
show
observed
distribution
a
strong
causing
users
preferentially
tweets
negative
emotional
valence.
Frequency
information
appears
be
largely
irrelevant
future
count.
Follower
count
strongly
predicts
in
simpler
linear
but
does
not
appear
drive
overall
after
temporal
dynamics
accounted
for.
Future
studies
could
apply
our
methodology
comparative
framework
assess
for
valence
theory
messages
differs
from
other
forms
media.
Journal of Philanthropy and Marketing,
Год журнала:
2024,
Номер
29(2)
Опубликована: Апрель 26, 2024
Abstract
MrBeast
is
the
world's
most
successful
individual
YouTube
content
creator.
Having
made
his
name
with
videos
of
high‐concept
challenges
and
stunts,
he
has
subsequently
produced
a
series
viral
centring
on
acts
philanthropy
–
drawing
both
praise
criticism
in
process.
This
paper
attempts
to
place
MrBeast's
approach
context
wider
historical
current
debates
about
nature
role
philanthropy,
order
ascertain
what
(if
anything)
genuinely
novel
it,
how
we
should
understand
it
relation
models
that
have
gone
before.
The
considers
“Beast
Philanthropy”
through
range
lenses
−
aesthetic,
ethical,
economic
political
these
can
tell
us
key
questions
be
asking
whether,
balance,
view
this
phenomenon
positively
or
not.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 15, 2025
Abstract
This
study
investigates
the
dissemination
of
archaeological
information
on
Twitter/X
through
lens
cultural
evolution.
By
analysing
132,230
tweets
containing
hashtag
#archaeology
from
2021
to
2023,
we
examine
how
content
and
context-related
factors
influence
retweeting
behaviour.
Our
findings
reveal
that
with
positive
sentiment
non-threatening
language
are
more
likely
be
shared,
contrasting
common
negativity
bias
observed
social
media.
Additionally,
authored
by
experts,
particularly
those
or
historical
expertise,
is
frequently
retweeted
than
popular
figures
lacking
domain-specific
expertise.
The
also
challenges
notion
pseudoarchaeology
spreads
rapidly
caution
against
overestimating
its
impact.
results
align
other
studies
spread
misinformation
“toxic”
behaviour
media,
showing
sharing
negative
hostile
a
vocal
minority
users
mediated
pertaining
context
communication.
These
insights
underscore
nuanced
dynamics
archaeology
communication,
emphasizing
importance
expert-led
positively
charged
narratives
in
engaging
public
Journal of Online Trust and Safety,
Год журнала:
2023,
Номер
2(1)
Опубликована: Сен. 21, 2023
Social
media
influences
what
we
see
and
hear,
believe,
how
act-but
artificial
intelligence
(AI)
social
media.By
changing
our
environments,
AIs
change
behavior:
as
per
Winston
Churchill,
"We
shape
buildings;
thereafter,
they
us."Across
billions
of
people
on
platforms
from
Facebook
to
Twitter
YouTube
TikTok,
AI
decides
is
at
the
top
feeds
(Backstrom
2016;
Fischer
2020),
who
might
connect
with
(Guy,
Ronen,
Wilcox
2009),
should
be
moderated,
labeled
a
warning,
or
outright
removed
(Gillespie
2018).These
models
environment
around
us
by
amplifying
removing
misinformation
radicalizing
content
(Hassan
et
al.
2015),
highlighting
suppressing
antisocial
behavior
such
harassment
(Lees
2022),
upranking
downranking
that
harm
well-being
(Burke,
Cheng,
Gant
2020).How
do
understand
engineer
this
sociotechnical
ouroboros
(Mansoury
2020)?As
traditional
critique
goes,
these
challenges
arise
because
are
optimized
for
engagement
Narayanan
2023).But
not
full
story:
help
manage
undesirable
outcomes
engagement-based
algorithms,
have
long
augmented
their
algorithms
1
nonengagement
(Eckles
2021).For
instance,
defeat
clickbait,
began
surveying
users
opinions
specific
posts,
then
building
could
predict
downrank
posts
dislike,
even
if
likely
click
them
Mosseri
2015).To
ensure
all
receive
feedback,
designed
weighing
effect
user
feedback
other
otherwise
get
few
replies
(Eckles,
Kizilcec,
Bakshy
2016).To
diminish
prevalence
violates
community
standards,
gore,
built
paid
moderation
teams
flag
remove
content.This
battery
surveys,
moderation,
downranking,
peer
estimation,
now
components
many
2021).1.In
commentary,
refer
"AI"
"algorithm"
interchangeably
machine
learning
procedures
learn
large-scale
data.We
primarily
concerned
focused
ranking
recommendation,
especially
feed
but
note
play
roles
well,
including
(de)monetization,
tagging,
political
toxicity
judgments.
