arXiv (Cornell University),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Jan. 1, 2023
The
rich
and
dynamic
information
environment
of
social
media
provides
researchers,
policy
makers,
entrepreneurs
with
opportunities
to
learn
about
phenomena
in
a
timely
manner.
However,
using
this
data
understand
behavior
is
difficult
due
heterogeneity
topics
events
discussed
the
highly
online
environment.
To
address
these
challenges,
we
present
method
for
systematically
detecting
measuring
emotional
reactions
offline
change
point
detection
on
time
series
collective
affect,
further
explaining
transformer-based
topic
model.
We
demonstrate
utility
corpus
tweets
from
large
US
metropolitan
area
between
January
August,
2020,
covering
period
great
change.
that
our
able
disaggregate
measure
population's
moral
reactions.
This
capability
allows
better
monitoring
during
crises
data.
Proceedings of the International AAAI Conference on Web and Social Media,
Journal Year:
2024,
Volume and Issue:
18, P. 1900 - 1912
Published: May 28, 2024
The
conflict
between
Israel
and
Palestinians
significantly
escalated
after
the
October
7,
2023
Hamas
attack,
capturing
global
attention.
To
understand
public
discourse
on
this
conflict,
we
present
a
meticulously
compiled
dataset-IsamasRed-comprising
nearly
400,000
conversations
over
8
million
comments
from
Reddit,
spanning
August
to
November
2023.
We
introduce
an
innovative
keyword
extraction
framework
leveraging
large
language
model
effectively
identify
pertinent
keywords,
ensuring
comprehensive
data
collection.
Our
initial
analysis
dataset,
examining
topics,
controversy,
emotional
moral
trends
time,
highlights
emotionally
charged
complex
nature
of
discourse.
This
dataset
aims
enrich
understanding
online
discussions,
shedding
light
interplay
ideology,
sentiment,
community
engagement
in
digital
spaces.
2021 IEEE International Conference on Big Data (Big Data),
Journal Year:
2023,
Volume and Issue:
unknown, P. 413 - 422
Published: Dec. 15, 2023
Effective
response
to
pandemics
requires
coordinated
adoption
of
mitigation
measures,
like
masking
and
quarantines,
curb
a
virus's
spread.
However,
as
the
COVID-19
pandemic
demonstrated,
political
divisions
can
hinder
consensus
on
appropriate
response.
To
better
understand
these
divisions,
our
study
examines
vast
collection
COVID-19-related
tweets.
We
focus
five
contentious
issues:
coronavirus
origins,
lockdowns,
masking,
education,
vaccines.
describe
weakly
supervised
method
identify
issue-relevant
tweets
employ
state-of-the-art
computational
methods
analyze
moral
language
infer
ideology.
explore
how
partisanship
shape
conversations
about
issues.
Our
findings
reveal
ideological
differences
in
issue
salience
used
by
different
groups.
find
that
conservatives
use
more
negatively-valenced
than
liberals
elites
rhetoric
greater
extent
non-elites
across
most
Examining
evolution
moralization
divisive
issues
provide
valuable
insights
into
dynamics
discussions
assist
policymakers
understanding
emergence
divisions.
The
rich
and
dynamic
information
environment
of
social
media
provides
researchers,
policy
makers,
entrepreneurs
with
opportunities
to
learn
about
phenomena
in
a
timely
manner.
However,
using
this
data
understand
behavior
is
difficult
due
heterogeneity
topics
events
discussed
the
highly
online
environment.
To
address
these
challenges,
we
present
method
for
systematically
detecting
measuring
emotional
reactions
offline
change
point
detection
on
time
series
collective
affect,
further
explaining
transformer-based
topic
model.
We
demonstrate
utility
corpus
tweets
from
large
US
metropolitan
area
between
January
August,
2020,
covering
period
great
change.
that
our
able
disaggregate
measure
population's
moral
reactions.
This
capability
allows
better
monitoring
during
crises
data.
International Journal of Pattern Recognition and Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
38(03)
Published: March 2, 2024
Fake
news
is
widely
spread
on
social
media.
Much
research
works
have
been
done
automatic
fake
detection
in
single
domain.
However,
exists
various
domains,
so
the
model
based
domain
less
effective
multiple
scenes.
To
improve
ability
of
multi-domain
news,
we
propose
a
perspective
collaboration
for
(PCMFND)
method
to
detect
across
domains
by
combining
powerful
feature
extraction
expert
systems.
The
extracts
features
different
perspectives
from
content
separately,
then
interactively
combines
perspectives,
and
ultimately
achieves
adaptively
aggregating
each
through
knowledge.
effectiveness
proposed
demonstrated
comparison
experiments
with
traditional
methods
Chinese
English
datasets.
