I'll present at Nara 1st Dec, 2012

1. You
can
download
this
slide
here:
hHp://slidesha.re/TwzFrf
LOD
genera*on
of
Social
and
Mass
media
data:
Apply
to
media
comparisons
Interna*onal
Asian
LOD
Challenge
Day
1st
Dec,
2012
Presenter:
Kenji
Koshikawa
Co-­‐Researcher(Adviser):
T.
Kawamura,
H.
Nakagawa,
Y.
Tanaka,
A.
Ohsuga
Affilia*on:
Department
of
Social
Intelligence
and
InformaBcs
Graduate
course
of
InformaBon
Systems
The
University
of
Electro-­‐CommunicaBons

2. About
Our
Project
2

3. Project
Abstract
We
do
just
two
things
on
the
project:
1.
Building
seman*c
networks
from
media
informa*on
2.
Comparing
with
different
media
using
the
networks.
3

4. Represen*ng
events
informa*on
using
seman*c
network
(RDF)
1/2
Example1:
昨日太郎は秋葉原でiPhone5を購入したので、幸せそうだった。
(Yesterday,
Taro
bought
a
iPhone
5
at
Akihabara,
so
he
looked
happy.)
Event
1
Event
2
Conver*ng
natural
language
into
seman*c
networks
Cause
Event
2
Event
1
太郎(Taro)
秋葉原
Ac*vity
Status
(Akihabara)
Loca*on
購買
Time
昨日
Time
幸福
Object
(Buying)
(Yesterday)
(Happiness)
iPhone
5
4

5. Outpu[ng
Linked
Data
as
RDF/XML
format
e.g.
“Taro
bought
a
iPhone
5
at
Akihabara,
so
he
looked
happy.”
5

6. Represen*ng
events
informa*on
using
seman*c
network
(RDF)
2/2
Example
2
(from
real
media):
a
fall
accident
April
an
accident
to
occur
the
southern
state
a
poor
maintenance
of
Florida
June
the
state
of
Florida,
U.S.
6

7. Project
Abstract
We
do
just
two
things
on
the
project:
1.
Building
seman*c
networks
from
media
informa*on
2.
Comparing
with
different
media
using
the
networks.
Mass
media
Social
media
7

8. A Case of media comparison
Topic: Introduction of Osprey in Japan
About Dataset :
Period:
1st April – 16th Aug, 2012
Condition:
Media textual information have
a word “オスプレイ”(Osprey).
Dataset of Social media:
Twitter: 3,084 tweets
A
photo
of
Osprey
Dataset of Mass media:
Asahi digital news paper: 116 articles
MSN Sankei news: 231 articles
Nippon News Network(NNN): 110 articles
Fuji News Network(FNN): 78 articles

9. Consideration throughout visualizing
network
• the
difference
of
diversity
of
topic
between
each
media
•
easy
to
access
minority
opinion
•
the
existence
of
2
kinds
of
osprey
(introduce)
•
the
Laterality
of
dependence
on
user
loca*on
9

10. Summary
of
the
existence
of
2
kinds
of
osprey
On mass media there are NOT information about following:
• The existence of other variants (of Osprey)
• The relation between the variants and the accident rate
• The fact that the accident rate of a variant, be deployed in
Japan is Lower than other rotorcraft ※
※ The
V-­‐22's
accident
rate
is
the
lowest
of
any
Marine
rotorcrab
[Ref
01]
By visualizing, we found the existence of 2 kinds of osprey and
the relation between the variants and accident rate.
Thus, we could notice a doubt of media bias on mass media.
A doubt of media bias
“Mass media hardly report about such information intentionally,
and they was in a mood in the press fomenting the contrary
opinion about introduction of osprey in Japan.”
10

11. Example
of
Considera*on:
the
existence
of
2
kinds
of
osprey
Look
around
a
“deploying”
node
deploying
CV-­‐22
osprey
A Color of node means
the occurrence rate on each media.
Social
Mass
MV-­‐22
osprey
a common
concept
This Figure has been showing that
there are 2 kinds of variants of osprey according to
the network built by social media dataset.
11

12. Example
of
Considera*on:
the
existence
of
2
kinds
of
osprey
CV-­‐22
Osprey
deploying
Be
nothing
like
MV-­‐22
Osprey
Lower
for
transport,
original
requirement
Harmful
rumor
There are the difference of use of each variant of osprey,
It can be read from this figure.
e.g. MV-22: for transporting / CV-22: for ?
12

13. Example
of
Considera*on:
the
existence
of
2
kinds
of
osprey
Accident
rate
Copter
low
Pilot
error
Look
around
a
“accident
rate”
node
13

14. Look around
Example
of
a “Accident rate of Osprey” node
Considera*on:
the
existence
of
Low
2
kinds
of
osprey
Accident
rate
of
Osprey
Look around a “1.93” node
Look around a “13.47” node
Accident
rate
Accident
rate
Accident
rate
of
CV-­‐22
Accident
rate
of
MV-­‐22
for
the
Special
Opera*ons
Command
Accident
rate
of
Osprey
Accident
rate
of
Osprey
14

15. Look around
Example
of
a “Accident rate of Osprey” node
Considera*on:
the
existence
of
Low
2
kinds
of
osprey
The
rela*on
between
the
variants
ate
of
Osprey
Accident
r
and
Look around a “1.93” node
reflected.
(from
a ocial
node
the
accident
rate
was
Look around s “13.47”
Accident
rate
media
dataset)
Accident
rate
Accident
rate
of
CV-­‐22
Accident
rate
of
MV-­‐22
for
the
Special
Opera*ons
Command
Accident
rate
of
Osprey
Accident
rate
of
Osprey
15

16. Summary
• Introduced
our
project:
– To
generate
LOD
from
media
informa*on
– To
compare
with
different
media
using
the
Linked
Data
• We
are
looking
for
solving
below:
– en*ty
resolu*on,
instance
matching
problem
– connect
to
other
Linked
Data
• In
future
work,
we
will
concentrate
on
improving
LOD
visualiza*on
for
knowledge
discovery.
• If
you
know
interes*ng
topic
for
media
comparison,
let
me
know.
16

17. Reference
[Ref
01]
"V-­‐22
Is
The
Safest,
Most
Survivable
Rotorcrab
The
Marines
Have."LexingtonInsBtute.org,
February
2011.
Retrieved:
16
February
2011.
[Ref
02]
(Japanese)
越川 兼地,
川村 隆浩,
中川 博之,
田原 康之,
大須賀 昭彦:
CRFを用いた
メディア情報の抽出とLinkedData化 -­‐
ソーシャルメディアとマスメディアの
比較事例 -­‐
,合同エージェントワークショップ＆シンポジウム(JAWS
2012),
2012.
Slide
(wriHen
in
Japanese):
hHp://slidesha.re/11pf0qR

18. Appendix

19. Goal
/
Mo*va*on
1. To
generate
Linked
Data
from
Media
Informa*on
– Mo*va*on:
• to
organize
abundance
informa*on
•
to
make
us
recognize
real
events
easily
2. To
compare
with
different
media
using
the
Linked
Data
(we
generated)
– Mo*va*on:
• to
discover
knowledge
from
the
difference
of
informa*on
between
media
•
to
understand
real
events
from
mul*ple
points
of
view
19

20. Our
System
Overview
20

21. Visualizing
the
Network
Size of node/Thickness of edge:
are calculated based on
the frequency information.
Color of node:
expresses the occurrence rate of
Social
Mass
concept between each media
a common
using 5 colors.
concept
Color of edge:
expresses kind of relationship between two concepts.
subject
object
time
status
quoted
source
activity
location
target
cause
※we used a visualization Application: Gephi 0.8.1 beta
21

22. Future
Work
• At
this
stage
we
just
visualize
the
network,
so
users
have
to
discover
knowledge
themselves.
– We
are
developing
tools
to
support
for
knowledge
discovery
from
the
network.
• To
es*mate
important
node/sub-­‐network
in
the
network.
• to
evaluate
our
system
and
to
be
needed
to
experience
other
topic
• We
are
looking
for
solving
below:
– en*ty
resolu*on,
Instance
matching.
• We
will
go
up
for
LOD
Challenge
2012
Japan.
– But,
I’m
not
sure
which
sec*on
is
the
best
for
our
project.
Dataset
Idea
Applica*on
Visualiza*on
22

23. 整理: MV-22 / CV-22 英語にする
オスプレイの型番と事故率の関係
型番
用途
事故率
MV-­‐22
輸送用
1.93
(日本配備)
米海兵隊所属
-­‐
2.45
航空機平均
CV-­‐22
特殊作戦用(空軍)
13.47
日本に配備される(た)機種 「MV-22」の事
故率は低い．
23

24. 事象の表現方法
事象情報を表現するために，[Nguyen 12]の
行動属性を拡張し9つの事象属性を定義した．
Event
descripDon
describe
property
Subject
Subject
of
an
event
Ac*vity
Ac*vity
of
an
event
Object
Object
of
an
ac*vity
Target
(new)
Against
whom
(e.g.
people,
country,
…)
Status(new)
Status
of
a
subject
Loca*on
Loca*on
where
an
event
occurred
Time
Time
informa*on
when
an
event
occured
Cause
(new)
Cause
what
an
event
occurred
Quoted
source
(new)
Source
of
a
quote
[Nguyen
12]
The-­‐Minh
Nguyen,
Takahiro
Kawamura,
Yasuyuki
Tahara,
and
Akihiko
Ohsuga:
Self-­‐Supervised
Capturing
of
Users’
Ac*vi*es
from
24
Weblogs.
Interna*onal
Journal
of
Intelligent
Informa*on
and
Database
Systems,Vol.6,
No.1,
pp.61-­‐76,
InderScience
Publishers,
2012

25. End