[International Asian LOD Challenge Day 2012]LOD generation of Social and Mass media data: Apply to...

LOD genera*on of Social and Mass media data:

Apply to media comparisons

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

電気通信大学大学院情報システム学研究科社会知能情報学専攻

大須賀・田原研究室

Interna*onal Asian LOD Challenge Day 1st Dec, 2012

You can download this slide here: hHp://slidesha.re/TwzFrf

About Our Project

2

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.

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Project Abstract

when

what ac*on

status 太郎

幸せ

iPhone5 入手

昨日

JAWS時点

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

Example1:

Represen*ng events informa*on using seman*c network (RDF) 1/2

太郎(Taro)

iPhone 5

Event 2 Cause Event 1

Ac*vity Status

Time Time Object

昨日 (Yesterday)

幸福 (Happiness)

購買 (Buying)

Loca*on

秋葉原 (Akihabara)

4

2012-‐11-‐30

Conver*ng natural language into seman*c networks

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

5

an accident

a fall accident

to occur

a poor maintenance

the state of Florida, U.S.

the southern state of Florida

June

April

Represen*ng events informa*on using seman*c network (RDF) 2/2

Example 2 (from real media):

6

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.

7

Project Abstract

Mass media Social media

A photo of Osprey

About Dataset : Period:

1st April – 16th Aug, 2012 Condition:

Dataset of Social media:

Twitter: 3,084 tweets

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

A Case of media comparison Topic: Introduction of Osprey in Japan

Media textual information have a word “オスプレイ”(Osprey).

Consideration throughout visualizing network

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•  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

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Summary of the existence of 2 kinds of osprey

※ The V-‐22's accident rate is the lowest of any Marine rotorcrab [Ref 01]

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 ※

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.”

Example of Considera*on: the existence of 2 kinds of osprey

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This Figure has been showing that there are 2 kinds of variants of osprey according to the network built by social media dataset.

deploying CV-‐22 osprey

MV-‐22 osprey

Look around a “deploying” node

Mass Social

a common concept

A Color of node means the occurrence rate on each media.

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CV-‐22 Osprey

MV-‐22 Osprey

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 ?

deploying

for transport, original requirement

Be nothing like

Harmful rumor Lower

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Look around a “accident rate” node


Accident rate

low Copter Pilot error

Look around a “Accident rate of Osprey” node

Look around a “1.93” node Look around a “13.47” node

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Accident rate Accident rate

Accident rate of MV-‐22 Accident rate of CV-‐22

Low

Accident rate of Osprey


for the Special Opera*ons Command


Look around a “Accident rate of Osprey” node

Look around a “1.93” node Look around a “13.47” node

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Accident rate Accident rate

Accident rate of MV-‐22 Accident rate of CV-‐22

Low



for the Special Opera*ons Command


The rela*on between the variants and the accident rate was reflected. (from social media dataset)

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.

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[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

Reference

Appendix

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

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Our System Overview

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Visualizing the Network

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Mass Social

subject

activity

object

location

time

target

status

cause

quoted source

※we used a visualization Application: Gephi 0.8.1 beta

Color of edge: expresses kind of relationship between two concepts.

Color of node: expresses the occurrence rate of

concept between each media using 5 colors.

Size of node/Thickness of edge: are calculated based on the frequency information.

a common concept

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.

22 Dataset Idea Applica*on Visualiza*on

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整理: MV-22 / CV-22オスプレイの型番と事故率の関係

型番用途事故率

MV-‐22 (日本配備)

輸送用 1.93

米海兵隊所属航空機平均

-‐ 2.45

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．

英語にする

事象の表現方法

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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 property

describe

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 Weblogs. Interna*onal Journal of Intelligent Informa*on and Database Systems,Vol.6, No.1, pp.61-‐76, InderScience Publishers, 2012

[International Asian LOD Challenge Day 2012]LOD generation of Social and Mass media data: Apply to...

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