►
Description
Presenter: Dr. Ugur Kursuncu
Bio: Ugur Kursuncu is an Assistant Professor at Georgia State University. He received his Ph.D. in Computer Science from the University of Georgia, and previously was a Postdoctoral Fellow at the Artificial Intelligence (AI) Institute, University of South Carolina His research is at the intersection of humans, society, and computers, designing socially responsible human-centered intelligent systems by integrating knowledge to enhance contemporary AI and data science methods.
A
I'm
going
to
give
you
an
overall
introduction,
our
motivation
and
our
problem
statements
and
what
kind
of
challenges
that
we
have
and
possible
solutions,
but
we
are
ongoing
to
more
I'm
going
to
focus
more
on
the
contextual
nature
of
the
extremism.
Extremism
is
a
problem
in
the
space
of
cyber
social
threats
and
specifically
I'm
going
to
talk
about
the
malevolent
creativity
and
its
impact
on
the
or
one
extremism
conversations
on
social
media.
A
So
as
you
as
you
know
that
the
social
media
has
been
very
useful
too,
not
only
for
people
to
express
their
opinion
and
to
have
a
conversation
in
their
on
the
topics
in
their
interest,
but
all
it
is
also
very
useful
tool
for
the
researchers
to
be
able
to
understand
the
human
behavior
or
why
people
behave
the
way.
The
way
they
behave.
So
specifically,
organizations
are
also
taking
advantage
of
this
tool
to
be
able
to
understand
their
audience.
For
example,
the
companies
are
able
to
understand
their
audience
through
social
media.
A
A
So
these
can
be
actually
like
a
naturally
organically
evolving
on
the
platforms,
but
they
can
be
exacerbated
by
the
organizations
by
the
extremist
groups
or
the
body
terrorist
organizations
or
even
by
other
organizations
as
well,
so
so
they
can
be
unintended
but
but
unintended,
but
they
can
be
also
intended
too.
So,
in
this
case,
what
we
can
do
is
first
to
understand
what
kind
of
problems
we
are
tackling
here
and
to
to
try
to
detect
and
then
counter
control
the
harm
using
social
Big
Data.
A
A
On
the
other
hand,
so
the
online
toxicity
is
cyber
bullying.
Our
harassment
and
also
extremism
is
just
one
example,
but
all
of
them
sharing
are
sharing
one
characteristics,
and
that
is
context,
and
the
context
is
a
context-
is
quite
critical
to
be
able
to
develop
reliable
and
robust
analysis
approaches
and
also
the
models
for
detection
and
countering
these
problems
on
social
media.
A
When
you
look
at
the
Spectrum
to
demonstrate
the
harm
on
social
media
in
different,
there
are
different
spaces
where
people
are
working
on
using
social
media
data,
but
specifically,
some
of
them
are
more
harmful
than
others.
For
example,
extremism
or
disinformation,
harassment
or
illicit
drugs
that
we
want
to
Monitor
and
all
of
these
more
harmful
Communications
are
sharing
one
characteristics,
and
that
is
a
context
and
what
is
the
importance
of
context
in
these
domains
in
these
domains?
A
Even
for
humans,
it
would
be
very
difficult
to
distinguish
what
is
extremist
from
what
is
not
experience
or
what
is
disinformation
from
what
is
not
disinformation
or
what
is
harassment
or
what's
not
harassment,
most
of
the
time?
So
when
you
develop
some
analysis
or
when
you
develop
a
models
that
is
going
to
detect
this
type
of
conversations
on
social
media
or
the
users
that
is
disseminating
this
type
of
conversations
on
social
media
most
of
the
time,
the
the
models
that
we
develop
is
going
to
confuse.
A
What
is
what
is
a
bad
conversation
or
hard
conversation?
What's
not
a
harmful
conversation,
for
example,
when
you
consider
a
communication
between
the
two
high
school
friends,
so
let's
say
one
is
harassing
the
other
one.
One
student
is
harassing
the
other
one
in
high
school,
and
that
is
happening
on
social
media
too,
and
they
are
using
some
some
curse
words
or
some
harmful,
harmful
language
between
each
other.
A
So
in
this
case,
then,
the
problem
that
we
are
tackling
with
here.
What
is
the
impact
of
context?
What's
the
impact
of
context-
and
we
are
also
focusing
on
the
malevolent
creativity
in
the
content
as
well,
and
we
are
looking
at
the
the
impact
of
context
and
malevolent
creativity
in
content
on
individuals
or
groups
online
in
the
Eco
Chambers
for
cyber
social
threats
and
then
how
can
we
mine
and
leverage
some
actionable
insights
from
social
media
to
address
the
above
question?
A
So
in
our
methods?
So
we
are
incorporating
knowledge,
external
knowledge
in
machine
learning,
algorithms
and
our
question
in
this
case,
from
a
method
methodology
perspective.
Can
the
external
views
like
it
can
be?
External
knowledge,
help
address
these
challenges
and
we
are
using
a
method,
knowledge,
infused
learning
that
is
leveraging
external
knowledge
in
the
form
of
Corpus,
Corpus
or
corpora
or
lexicons,
or
knowledge
base
ontology
and
knowledge
graphs,
and
we
are
using
this
approach
in
our
in
our
method.
Here,
foreign.
A
Contextual
nature
of
online
extremism
here
is
going
to
be
also
very
important
because
the
meaning
of
the
content
is
going
to
change,
depending
depending
on
the
perspective
of
the
of
the
user
and
and
the
power
of
extremism.
Online
has
been
quite
influential
so
far
and
so
far
1
000
Americans
between
1980
and
2011.
A
So,
on
the
other
hand,
the
there
are
challenges
to
be
able
to
detect
this
type
of
conversations,
because
the
contextual
nature
of
this
problem
has
been
actually
quite
challenging
and
first
of
all,
this
is
a
persuasive
process.
Over
time.
The
radicalization
and
the
these
type
of
communications
are
constructed
to
and
also
intended
to
shape
and
reinforce
or
change
the
response
of
of
another
or
others,
or
it
has
been
constructed
to
change
the
behavior
of
the
other
person
so
to
change
the
point
of
view
of
the
other
person
or
the
police
system.
A
So
they
are
using
most
of
the
time
domain
knowledge
for
proceeding.
Individuals-
since
we
are
talking
about
here,
extremism
or
a
specific
type
of
extremism
which
is
Islam,
is
extremism.
Then
domain
knowledge
has
been
critical
because,
most
of
the
time,
the
extremist
groups
or
organizations
and
individuals
on
social
media,
they
are
using
knowledge
in
the
domain
of
the
religion
of
Islam.
So
to
be
able
to
understand
what
kind
of
person
is
a
process
or
what
kind
of
contextual
nature
of
the
problem
has.
A
We
need
to
know
specifically
about
a
specific
knowledge
about
the
religion
of
Islam,
so
that
we
can
incorporate
the
this
type
of
knowledge
in
our
analysis
and
also
the
model
that
we
are
developing
and,
more
importantly,
whatever
solution
that
we
come
up
with.
It
has
to
be
a
fair
solution
in
this
case.
A
A
So
these
are
most
of
the
time
shared
with
the
non-extremists
individuals
as
well
or
the
organizations,
because
most
of
the
time
Muslims
are
talking
about
these
topics
like
religious
topics
and
this
type
of
conversations
and
the
the
ones
in
the
in
Red.
So
they
are
mostly
about
harming
another
person
or
another
group.
A
So
here
when
we
so
as
we
as
we
see
before
the
extremist
content
is
a
multi-dimensional
Content
here
and
the
dimensions
of
the
context
of
this
content
is
religion,
ideology
and
hate,
as
we
identified
before,
and
the
distribution
of
prevalent
terms
in
each
Dimension
is
different
here.
So
the
meaning
is
going
to
be
very
different
in
each
context
as
well.
A
So,
for
example,
the
the
keyword
for
for
Jihad,
for
example,
the
meaning
of
jihad,
is
going
to
be
very
different
in
a
religion
domain
than
the
the
meaning
in
ideology
or
in
hate.
So
for
this
reason
we
need
to
use
these
Dimensions
contextual
Dimensions
to
the
same
big
weight.
Common
diagnostic
terms.
Jihad
is
a
very
important
critical
term
that
we
need
to
use
in
our
model,
but
we
we
also
need
to
disambiguate
the
meaning
of
this
term,
to
be
able
to
to
be
able
to
create
a
reliable
model.
A
A
A
So
in
this
case,
when
we
consider
an
external
knowledge
in
the
form
of
a
Knowledge
Graph,
where
we
Define
different
meanings
of
jihad,
so
Jihad,
for
example,
on
the
right
hand,
side
is-
is
related
to
violence,
killing
paradise
and
hate
attack
or
and
on
the
other
hand,
the
Jihad.
Another
meaning
of
jihad
is
related
to
prayer,
God
and
Paradise
right.
A
So
then,
as
we
use
this
mouse
graph
as
an
external
resource
that
is
going
to
give
us
the
opportunity
to
distinguish
the
meaning
of
the
of
the
of
the
term
in
an
explosive
Manner
and
incorporate
that
animation
learning
model,
and
that
is
going
to
give
us
more
more
explicit,
explicitly
separated
meanings
of
the
same
keyword
as
we
said.
The
same
liquidity
these
this.
This
content.
A
C
A
As
as
it
is
a
kindness
It's
about
kindness
and
just
helping
other
people,
for
example,
it
says
kindness
is
a
language
which
the
blind
can't
see
and
the
death
can't
hear
mind.
Jihad
be
kind
always
so
being
kind
to
others
is
is,
is
a
Jihad
right,
and
this
is
a
religious
meaning
in
that
in
that
content.
In
that
context,
on
the
other
hand,
at
the
bottom,
so
it
talks
about
the
the
Jihad
from
a
political
perspective,
and
it
talks
about
the
nation
of
jihad
and
martyrdom
can
never
be
defeated.
A
So
it
talks
about
the
political
perspective
of
jihad
and
martyrdom
can
never
be
defeated.
Martyrdom
can
be
done
for
the
sake
of
a
national
Jihad,
which
is
referring
to
a
political
protocol
organization
or
political
meaning,
and
that
is
about
ideology,
but
on
the
other
hand,
the
right
hand
side
it's
about
harming
others
right,
so
it
is
about
killing
other
people
and
it
they
they
call
it
Supreme
Jihad.
A
A
On
the
other
hand,
we
can
also
can
get
the
context
capture
the
context
from
launch
graph,
as
I
mentioned
before,
and
from
a
Knowledge
Graph.
That
is
going
to
be
a
solution
through
the
distance
between
Concepts
in
the
mouse
graph
and
for
each
Concept
in
the
content.
Here
we
are
going
to
have
an
entity
in
the
knowledge
graph
and
in
an
outs
graph.
We
have
the
relationships
between
these
entities
and
these
relationships
are
going
to
give
you
the
contextual
information
of
the
of
the
content.
A
A
Of
course,
when
we
use
when
we
develop
a
machine
learning
model,
then
when
we
use
the
three
discussions,
all
together
is
performing
better
than
other
models
that
we
develop
with
only
one
dimension
or
only
two
dimensions.
So
we
are
specifically
using
precision
as
a
metric
here.
We
because
we
want
to
emphasize
the
reduced
risk
classification
of
non-extremist
content,
because
we
don't
want
to.
A
So
on
top
of
this,
so
we
are
also.
We
are
also
trying
to
understand
the
malevolent
creativity
in
extremist
content,
and-
and
that
is
why
we
are
studying
this
to
understand
why
extremist
groups
are
very
efficient,
effective
and
efficient
in
influencing
others
online.
Because
when
you
look
at
the
statistics,
so
they
are,
they
are
very
successful
to
perceive
other
people
to
influence
other
people
so
that
other
people
can
actually
be
persuaded,
maybe
over
time
in
a
year
or
two
and
they
attempt
to
travel.
A
A
This
seminal
work
actually
was
done
by
the
researchers
at
MIT
and
they
what
they
found
was,
and
then
they
they
actually
trained
an
LDA
former,
a
big
social
media
data
and
they
identified
200
topics
and
they
compared
the
false
news
and
the
True
News,
using
the
metrics
of
KL
Divergence
information,
uniqueness
and
semantic
distance,
and
what
they
found
was
the
falsehood
or
the
false
news.
They
spread
significantly
farther
faster
and
deeper
on
social
media
compared
to
the
True
News
and
falseness
they
found.
A
False
news
are
more
novel
than
the
True
News,
and
people
are
more
likely
to
share
false
needs
and
and
and
and
humans.
Inherently
we
are,
we
are
more,
we
are,
we
tend
to
share
or
be
influenced
by
novel
content
compared
to
compared
to
a
Content.
That
is
not
that
novel,
so
so
that
is
all
that
is
a
novelty
in
that
they
find
in
the
content,
but
that
is
also
malevolent
computational
creativity.
C
A
The
researchers
in
social
science,
specifically
in
educational
psychology,
they
identify
four
different
characteristics
for
malevolent
creativity
and
one
is
novelty,
originality,
surprise
and
Divergence,
and
so
creativity
is
about
usefulness
and
a
novelty
of
the
ideas,
but
it
has
to
be
relevant
too.
So,
for
example,
if
someone
is
talking
about
different
topics
and
talking
about
some
new
topic,
new
need
giving
need
information
all
the
time,
but
it
has
to
be
relevant
too.
A
A
B
A
To
one
is
scale
Divergence,
another
one
is
cosine
similarity
and
the
other
one
is
entropy
and
we
are
measuring
semantic
distance
using
those.
So
we
are
looking
at
the
novelty
and
originality
in
the
content
and
KL
Divergence
is
a
measure
of
difference
between
two
probabilities
distributions.
Okay
and
if
two
distributions
were
similar,
the
KL
Divergence
for
both
of
them
would
be
small
right.
A
A
C
A
For
the
money
extremist
group,
it
is
95,
so
the
difference
is
not
actually
very
high,
but
still
experience.
People
are
more
original
extremists.
Then
we
look
at
the
hate.
The
extremist
group
is
at
40
originality,
and
that
is
compared
to
one
percent
in
the
non-experience
group
and
still
extreme
screws
is
actually
more
original
and
they
have
more
novel
content
from
a
hate
perspective.
A
So
when
we
look
at
the
when
we
look
at
the
the
two
groups,
how
different
they
are
from
different
metrics-
and
we
are
looking
at
the
KL
and
the
cosine
similarity
here
and
and
cosine
similarity
is
a
difference.
Cosine
as
it
is
higher,
it
is
more
similar
for
KL.
It
is
when
it
is
higher.
It
is
more
dissimilar.
So
in
this
case,
for
for
extreme
screw,
it
is
for
the
four
percent
similarity
meaning
the
rest
is
actually
dissimilar.
A
A
The
entropy
we
are
using
the
Shannon's
atrophy
here.
Entropy
is
a
measure
of
surprise
right.
So
when
we
look
at
the
entropy,
we
find
that
the
extremist
users
have
more
unique
content
compared
to
the
non-experienced
users
and
based
on
the
statistics
that
we
have
and
the
distribution
here
we
see
that
the
minimum
entropy
is
a
number
of
non-exchange
groups
here
in
in
Orange,
and
the
maximum
entropy
is
a
member
of
extremist
group
in
blue.
So
that
may
compare
the
distribution
of
those.
A
A
So
going
back
to
context,
so
we
are
modeling
a
a
detection
model
using
the
con
using
a
contextual
model
and
where
we
learn
the
the
representations
using
sequential
model
using
lstm
and
wireless
DM.
As
you
know,
The
stm's,
Preserve
long
dependencies
in
the
in
content,
preserving
the
capturing
context
as
well,
and
also
we
are
extracting
Knowledge
from
a
Knowledge
Graph.
As
we
as
you
see
here
and
in
another
approach.
A
We
are
also
modeling
using
a
creativity
approach
in
this
creativity,
as
we
as
I
mentioned
before,
the
variation
in
language
is
important
and
how
unique
the
content
is
is
very
important
as
well,
and
we
see
between
the
extremist
and
non-excused
groups.
There
is
actually
a
difference
between
the
between
these
two
groups
from
a
originality
and
Novelty
perspective,
and
for
this
reason
we
are
using
here
A
variation,
Auto
encoder
to
better
capture
variations
in
language.
A
Okay,
so,
as
you
see
here,
B
with
three
dimensions,
the
random
force
is
actually
achieving
at
93
percent.
When
we
use
lstm
and
biostrium,
it
is
actually
going
to
94
and
95
percent
with
the
three
dimensions
and
we
incorporate
the
knowledge
embedding.
It
is
going
up
96
percent,
but
it
is
very
interesting
to
see
with
the
variation
Auto
encoded,
it's
actually
even
going
up
to
98.
A
So,
overall
from
the
study,
what
we
learned
was
extremist
users
employ
different
tactics
to
perceive
their
target,
and
we
we
found
that
another
one.
Creativity
is
actually
an
important
tactic
that
they
use
in
their
content
creation
so
that
they
are
able
to.
They
are
able
to
influence
other
people
more
effectively
and
each
Dimension
has
different
creativity,
characteristics
based
on
linguistic
and
somatic
cues
on
characteristics
as
you,
as
we
saw
before,
for
different
dimensions.
The
creativity
characteristics
is
coming
up
actually
differently
from
a
religion
perspective
from
ideology
and
hate.
A
A
And,
of
course,
domain
specific
knowledge
is
reducing
false
alarm,
and
that
is
helping
to
prevent
a
potential
social
discrimination,
and
that
is
specifically
an
important
point,
because
when
we
deploy
such
a
model
on
a
platform
being
used
by
millions
of
people,
it
is
likely
that
an
unfair
mistreatment
is
going
to
occur
just
because
of
the
misclassification
in
the
model.
And
that
is
going
to
be
that's
going
to
affect
mostly
non-extreme
individuals
that
are
not
extremists
or
not
harmful
in
any
way.
A
And
as
a
conclusion,
so
social
media
data
is
giving
us
a
tool
to
understand
this
type
of
Dynamics
as
to
how
these
malicious
groups
and
organizations
are
providing
their
content
or
their
messages
to
Their
audience
and
what
kind
of
tactics
they
are
using
and
we
are
able
to
understand
these.
Using
this
data
and,
more
importantly,
the
malevolent
creativity
has
been
actually
very
effective
for
extremism
as
it
was
affected
for
misinformation
and
for
other
online
toxicity
problems
as
well
and
prior
knowledge.
C
C
There
were
a
couple
of
points
where
I
was
a
bit
curious
and
one
of
them
was
at
the
very
beginning
when
you
were
mentioning
kind
of
the
the
context-
and
you
add
some
data
by
cyber
bullying,
which
have
also
done
some
research
on
I
was
wondering:
are
these
typically
sort
of
like
surveys,
self-reporting
kind
of
kind
of
data,
or
are
there
any
like
computational
tools
like
toxicity
or
anything
like
that?
Do
you
know
how
this
was.
C
Look
at
the
yeah
I
understand
like
a
lot
of
citations
at
the
bottom.
I
was
yeah
just
just
curious.
If
these
are
mostly
surveys-
yes,
okay,
because
I'm
always
like
curious
about.
If
you
know
they
have
a
new
completion
mode
to
look
at
this
yeah,
okay
and
I'm,
sorry,
but
sorry,
you're
I
think
you
did
a
set
is
mostly
Twitter.
Did
you
look
at
a
particular
time
frame
or
geolocation,
or
was
this
mostly
a
hashtags?
Only.
C
A
Even
though
these
tweets
were
actually
verified
by
Twitter,
so
we
found
some
Outlets
outpiers
meaning,
so
these
were
verified
as
extremist
twists
or
extremist
individuals.
But
looking
at
their
looking
at
their
content
from
a
ex
domain
expert
perspective,
they
were
not.
They
would
not
be
considered
as
extremists.
So
we
identified
these
outliers
as
like.
Ten
percent
about
10
percent
of
the
content
of
the
users
were
actually
outliers
and
they
were
labeled
as
extremists,
but
they
were
not
extremists
in
the
first
place
and
we
removed
those
from
the
content
from
our
data
sets.
A
After
we
identified
those
outliers
and
looking
at
the
content
of
the
extremist
groups,
so
they
were
mostly
trying
to
promote
their
their
ideology
and
the
ideology.
Meaning
I
mean
Isis,
ended
up
being
able
to
hold
some
territories
and
they
claimed
themselves
as
a
state
right
and
they
were
trying
to
promote
their
state
and
their
ideology,
using
some
ideological
statements
from
some
ideologues
that
they
that
they
follow,
for
example,
for
example,
Imam
and
War
a
lucky
for
example.
A
So
this
person
was
the
number
one
ideologue
followed
by
ISIS
and
many
other
terrorist
organizations
as
well,
and
they
were
promoting
their
ideas.
They
were
promoting
their
sayings
or
what
they
what
they
said
in
at
one
time
before
or
some
other
ideologues
as
well,
and-
and
they
were
talking
about
some
some
attacks-
that
Isis
Isis
did
at
the
time
or
or
some
air
strikes
or
killing
other
people
or
or
this
type
of
content
just
being
exchanged
all
the
time.
A
On
the
other
hand,
they
were
also
attributing
this
type
of
content
to
some
religious
resources
or
some
religious,
for
example.
They
are
also
talking
about
Quran
they're,
also
talking
about
Hadith
or
they're,
also
talking
about
Allah.
So
these
are
all
shared
concepts
with
the
extremist
groups
with
among
extremist
groups,
and
it
it
it.
It
is
creating
a
lot
of
confusion
around
these
Concepts,
so
we
had
to
also
disambiguate
the
meaning
of
these
Concepts
as
well.
C
B
A
So
these
are,
these
are
the
extremist
and
non-extremist
groups
and
how
original
their
content
from
each
other.
So
these
groups
were
actually
the
x-axis
on
the
y-axis.
They
are
users
and
how
these
users
are
being
original
from
each
other,
whether
they
are
repeating
each
other
or
they
are
providing
some
novel
content
or
some
novel
information
to
Their
audience.
So
if
they
are
repeating
each
other,
then
it
is
not
actually
original
or
it's
not
novel
right,
but
if
they
are
providing
some
novel
original
information,
so
that
is
novel.
B
A
A
B
Of
them
yeah,
okay,
gotcha
yeah,
it
makes
sense.
Okay,
good
now,
that's
yeah,
very
interesting
concept
here
in
a
way
you're
using
that
the
other
question
I,
guess
somewhere
related
to
that.
In
order
to
do
that
comparison,
are
you
extracting
that
text
through
some
kind
of
topic
modeling
or
what
language
model?
How
are
you
extracting,
and
even
back
in
the
previous,
so
we
were
just
discussing
for
Thomas's
question
where
you
had
all
the
all
of
the
phrases
and
topics
extracted
modeling
and
what
are.