Community as a New Communication Layerin the Internet
Hideaki Takeda
National Institute of Informatics
andThe Graduate University for Advanced Studies
2-1-2 Hitotsubashi, Chiyoda-ku
101-8430 Tokyo, Japan
E-mail takeda@nii.ac.jp
This work is done with Masaharu Hamasaki, Ohmukai Ikki, and Toru Takahashi.
Hideaki Takeda / National Institute of Informatics

The Information Flood on the Internet
The information flood on the Internet
 The amount of information on the Internet has been increasing
 How to ensure the quality of information within the enormous
amount of informationz The key to escape from the information flood is human relationshipz

Hideaki Takeda / National Institute of Informatics

1

Human relationship for the information flood
People can act intelligent agents for each other to collect, filter andassociate necessary information.
z They can help not only themselves but also other people Four eyes see more than two.(三人寄れば文殊の知恵) Scratch me and I'll scratch you (魚心あれば水心)
z But, it can work only via reliable human networkz

Hideaki Takeda / National Institute of Informatics

Human relationship for the information flood
Only reliable human network can help us to keep quality of
information
 Which do you believe recommendation of TV programs by yourfriends or TV guide books?
 The degree of reliability
 The degree of closeness The degree of shared interests
…
z Community or human network is the key to enable information
sharing and exchange with qualityz

Hideaki Takeda / National Institute of Informatics2

Roles of communities
for information sharing/exchanging
Communities or human network can serve as a layer ofcommunication via computer network
z The distinction
 Personal human network: the graph where a person as a node and a relation between
persons as a link Community: the structure upon personal human networkz

Hideaki Takeda / National Institute of Informatics

Roles of communities
for information sharing/exchangingz

Roles of communities
 provide channels for information exchange on specific topics
 Becoming a member of a community is obtaining a channel tosend and receive information related on the community E.g., Mailing list work as filtering information
 Members of communities collect and distribute information
specific to some topics either explicitly or implicitly. Once a
consensus can be formed in a community, information specificto the topics can be easily collected or selected by members of
the community.
 offer a field for collaboration to produce new information
 Communication in communities can yield new information
 E.g., discussion group on BBS
Hideaki Takeda / National Institute of Informatics

3

Tasks to realize “better” online communitiesz

Two directions
 Make online communities natural like real-world communities
 Online communities are still by far un-mature in comparison
with real-world communities.
 Exploit characteristics of online communities Reduce real-world constraintszTime, Space, etc Add new communication waysz
Agents, asynchronous communication, etc Both directions are needed
Balance is important
Hideaki Takeda / National Institute of Informatics

Tasks
z

z

Forming communities
 How to know relationship among people?
Red: Research Themes
 Relating people to each other
Blue: Applications to communities
Re-configuration of personal human network
 How to form communities from relationship among people?
 Finding common needs, interests, topics, etc.
Finding relationship via WWW bookmarks
Collaborative Scheduling Support System for Conferences
Facilitating activities in communities
 How to make communities better?
 Exploit merits of online communities and compensate their demerits
Expressive Media for Online Communities
e-kyoshitsu: Application to Distance Learning for Children
 How to utilize information in communities?
 Filtering, extraction, summarization, etc.
(…)
Hideaki Takeda / National Institute of Informatics

4

Re-configuration of personal networks
by the neighborhood matchmaker method

M. Hamasaki, H. Takeda

PurposePersonal network is usually “ad hoc”
 We may miss better friends nearbyz We need better networkz One Solution:
 Collect data for all people, then generate the “best” network
 Disadvantage:z Scalabilityz Privacyz Our approach: Neighborhood Matchmaker Method (NMM)z

Hideaki Takeda / National Institute of Informatics

5

Neighborhood Matchmaker Method (NMM)
A iterative approach to optimize the networkz Every node works as a matchmaker for neighborhood nodes toimprove the network
z The basic idea
 In our real life, introducing new friends by the current friends is apractical way to optimize personal networksz We can know persons who you have not known before
z Your friend can filter people for you
z Advantages
 No need for central servers
 Applicable to any size of community Less computational costz

Hideaki Takeda / National Institute of Informatics

Algorithmzzz

1. A node calculates connection values between its neighbor nodes
 We call that node “matchmaker”
2. If the matchmaker finds a pair of nodes which has a good enough connection
value, it selects this pair for recommendation. The matchmaker introduces both
nodes of recommended pair to each other
3. The node that receives recommendation decides whether it accepts or not. If
it accepts, it adds a path to the recommended node!?

Good !!

matchmaker

matchmaker

matchmakerOKOK

Calculating connection values

Introducing each nodes

Adding a new path

Hideaki Takeda / National Institute of Informatics

6

Sample Networksz

We compare two networks, (2) and (3)1. initial(generated randomly)

2. converged(NMM)

3. best(central server model)

Hideaki Takeda / National Institute of Informatics

Results: Cover-Rate w.r.t. Nodeszz

The path size is fixed as three times as the node size
All cases were converged
The average of cover-rate and the turn of convergence vary with the node size

1

20node0.9

40node0.8
0.7

60node0.6

80node0.5

Cover-Ratez

100node0.4
0.3

20node40node60node80node100node

0.2
0.1
0
0

50

100

150

200

Turn

250 300 350 400 450 500
Hideaki Takeda / National Institute of Informatics

7

Results: Average of Convergence Turnzz

The number of convergence turn is linearly increased with thenode size
Computational cost
 NMM: O(N)
 Central Server Model: O(N2)

Convergence Turn

600

5path/node

500

4path/node

400

3path/node 1p/node300

2path/node

2p/node3p/node
4p/node
5p/node200

1path/node

100

0
10

20

30

40

50

60

70

80

90

100

The node size

Hideaki Takeda / National Institute of Informatics

Conclusion
Proposal of optimization of “ad hoc” networkz Good news for the Internet communities No need for central servers
 Applicable to any size of community
 Anytime AlgorithmzHideaki Takeda / National Institute of Informatics

8

Discovery of Shared Topics Networks
among People
A Simple Approach to Find Community Knowledge from WWW Bookmarks

H. Takeda, M. Hamasaki, T. Matsuzuka, Y. Taniguchi

PurposeGeneration of human network guiding individual information
activities An example
 I want to watch sports programs on TV. What yourrecommendation?
 Who and Whatz Shared Topics Network among Users (STN)z

Hideaki Takeda / National Institute of Informatics

9

Our approachz

Combination of manual and automatic methods
 Identification of topic
Use of bookmark files as users’ knowledge

To overcome knowledge acquisition problem
 Discovery of inter-topic relations
Text analysis to calculate inter-topic relations

Hideaki Takeda / National Institute of Informatics

Bookmarks as Knowledgezzx

WWW page(informationon the topic)= Examples of the topicab

Shared Topics Network
c

f

5
4

URLs in a bookmark folder

2

1
g

z

= A topic interested by the user

User

Folder(topic)A bookmark folder

e

d

3

y

Bookmark (person’s interest)
Hideaki Takeda / National Institute of Informatics

10Procedure to discovery shared topics
Calculation of Similarity among WWW pages↓
Estimation of Similarity among folders for different users

Hideaki Takeda / National Institute of Informatics

Calculation of Similarity among WWW pages

z

x

z

2

z

bbb,
b aaa,
ccc, dddz

1iii, jjj,
eee, fff

c

f

5
z

a

g

4

Extract words in texts
Pick up some top words in frequency
Find shared words: count how many
these words are overlapped in two
pages
If there are enough shared words,
these pages are relatededaaa, ggg,
ccc, hhhaaa, bbb,
eee, fff

3

yHideaki Takeda / National Institute of Informatics

11

Discovery of common topicszCount pairs of related pages forevery pair of foldersz

If they are enough related pairs,
these two folders are related.

x
2

1
a

g

c

f

5
z

b

4

e

d

3

y

Shared Topics Network
Hideaki Takeda / National Institute of Informatics

kMedia Interface

Browser
window

SharedTopicsNetwork

Bookmark withrecommendation
Hideaki Takeda / National Institute of Informatics

12

Bookmark with recommendationz

Two pages are related
Each page is a recommended page to the otherAcademic Related

Lab. in Universities

Yamada Lab.
AI Meeting RoomThe original data
Recommended data

Hideaki Takeda / National Institute of Informatics

Discovered Shared Topics Network

C

A

B
Hideaki Takeda / National Institute of Informatics

13

Discovery of topic relations
Common relations
 (search, IR), (academia, research-related)
 similar but words themselves are differentz Un-common relations
computer-related…(Unix, academia)
research-related Speciality of thecommunity
searchz

B

A

Information
retrievalacademiaC

Hideaki Takeda / National Institute of Informatics

Discovery of relationship among people
What are common topics with others?
z Who is good at this topic?
z

computer-related
research-relatedBrc hsearch resea
A

rese
arch

Information
retrievalunix
academiaC

Hideaki Takeda / National Institute of Informatics

14

Experimental Evaluation (1)Subjects of Experiment

12 subjects
z 3 persons from 4 communities (lab. = community)z Two tasks for subjects
 Submit their bookmark files
 Evaluate recommendations generated by STN
z Two types of groups to generate STN In-community: Belongs to the same laboratory
 Cross-community: Comes from different laboratoriesz

Hideaki Takeda / National Institute of Informatics

Experimental Evaluation (2)Items for User Evaluationsz

The evaluation ranges from 1 to 5 (5 is the best)

Page

Folder

PersonAre the recommended pages similar?
Are the recommended folders similar?
Are the recommended folders useful?
Do you want to contact the person?
Do you want to meetthe person?

Hideaki Takeda / National Institute of Informatics

15

Analysis of Effects of Community (1)the Relation Between Page and Folder Relevancez

High correlation between folder relevance and page relevancefor in-community case

Folder Relevance16
14

Incommunity

12
10

Crosscommunity

8
6
4
2
0
10

20

30

40

50

60

70

80

Page Relevance
Hideaki Takeda / National Institute of Informatics

Category Resemblance (1)Categorization Is Human Relation?
z

Human relation can be measured by resemblance of folder structure
Nfij × Rfij Cij : Category resemblance
Cij =Nfij : No. of recommended folders
Npij
Rfij : Folder relevance
Npij : No. of recommended pages

Folder structure is similar

Not similar

Hideaki Takeda / National Institute of Informatics

16

Effects of Category Resemblance (2)Correlation Coefficient of the Parameters to Evaluations of Personz

The category resemblance is the highest of all parameters inthis experiment
To contact

To meet

0.49

0.55

Num. of recommended pages

0.42

0.30

Ave. of page relevance

-0.13

-0.19

Num. of recommended folders

0.45

0.30

Ave. of folder relevance

0.38

0.30

Avg. of Evaluation of page

0.29

0.40

Avg. of Evaluation 1 of folder

0.28

0.32

0.09

0.20

Category Resemblance

Avg. of Evaluation 2 of folder

Hideaki Takeda / National Institute of Informatics

Effects of Category Resemblance (4)for Page Recommendation
Better page recommendation results for new group made fromcategory resemblance (CR)

z

Score

2.8
2.7

Page Recommendation
New groups

2.6
2.5
2.4
2.3
2.2

All
In-community
Cross-community

2.1
2
Hideaki Takeda / National Institute of Informatics

17

Summary
Proposal of shared topic network to enhance user’scommunicationz Proposal of algorithm of discovery of shared topic networks with
WWW bookmark files
z Validity of our approach by an experimentz Proposal of category resemblance as measurement for community
effectsz

Hideaki Takeda / National Institute of Informatics

Collaborative Scheduling Support System forConferences(on-going project)

H. Takeda, M. Hamasaki
In cooperation with Yutaka Matsuo and Takuichi Nishimura

18

PurposezzzzSystem Aim: Support people to find their friends in a specific groupResearch Theme: Investigate different human networks in the same group
Three human networks
 Human network in the activity: I worked with him
 Human network by communication: I know him
 Human network by behavior: I meet him
Scheduling on conferences Plan and Action

Planning

action“I worked with him”

“I know him”

“I meet him”

Hideaki Takeda / National Institute of Informatics

System Functions
Easy-to-use scheduling system for the conference Just add presentations what you want to watch
z Can refer schedules of other people Manually collaborative scheduling Can only see schedules of who know you
z Can recommend schedules (not yet)
 Automatically collaborative schedulingz On-site support of schedules (not yet)
 Small communication device with sensorsz

Cobit
Takuichi Nishimura, Hideo Itoh, Yoshinobu Yamamoto and Hideyuki
Nakashima. ``A compact battery-less information terminal (CoBIT) for
location-based support systems," In Proceeding of SPIE, number 4863B12, 2002.

Hideaki Takeda / National Institute of Informatics

19

The current status of the system

Hideaki Takeda / National Institute of Informatics

TelMeAShow Me What You Mean Expressive Media for Online Communities

Toru Takahashi, Yasuhiro Katagiri, H. Takeda

20Introduction of TelMeA2002z

What is TelMeA2002?


TelMeA2002 is an asynchronous community system like bulletin board
system (not internet chat system).



TelMeA2002 employs character agents as personal conversational media
among users.



We call such personal agents as personified media



In TelMeA2002 community, users can make messages in combination offull body expressions and pointing to web contents with personified
media

Hideaki Takeda / National Institute of Informatics

TelMeA2002

Hideaki Takeda / National Institute of Informatics

21

Conversation Process in TelMeA2002
Hello!

1. Edit a message
in terms of ascript language

EditorParticipant of a
Personified
TelMeA CommunityMedia

2. Submit the message
to the community
server

6. The message isasynchronously
enacted

Other Participantsof the Community

Hello!

Screen Shot of
TelMeA2003

4. Request for seeing themessage from others
5. The required message
is downloaded

Conversation Log of the
TelMeA Community

3. The massage is accumulated
in the conversation log

Hideaki Takeda / National Institute of Informatics

Our Goalz

Is to find pragmatic rules of social and nonverbal interactions
 Supporting social and nonverbal interactions
 Archiving the logs of long-term community activities Analyzing usages and effects of nonverbal expressivity

Make a model of multimodal social interaction
 Calculate social evaluations for involved information
 Summarize or make reutilize the involved information


Hideaki Takeda / National Institute of Informatics

22

Challenges 1 - Identificationz

Unique embodiment is necessary for quick identification.


z
z

z

Because users are represented by their personified media.

For analysis, however, each personified medium needs to have same set of
expressions.
Making same animations for various personified media puts a heavy load on thedevelopment.

We first focus on an analysis of usage of various type of animation before
expanding the grade of identification.
(56 kinds of animations for all 8 types of personified media)

Hideaki Takeda / National Institute of Informatics

Challenges 2 - Communication Featuresz

Personified media should cover all 4 features of humancommunication
 Facts – enable through the spoken content
 Relationship – expressed through the relative spatial

distance and position
 Appeal – expressed through the selection of various

performative verbs
 Self-revelation – communicated through the emotional

expression

Hideaki Takeda / National Institute of Informatics

23

Challenges 3 - Expressive Repertoirez

Personified media need to cover the entire scale of expression for the
believability.
 35 performative verbs(explains, agrees, complains, etc.)
 48 affective expressions(likes, sadly, worries, etc.)
 13 interpersonal attitudes(yes, I know, forgotten, etc.)
 Direct attentionz

Pointing, interpersonal distances

Some essential conversational expressions such as glance and nods are lessof importance because of nature of asynchronous conversation.

Hideaki Takeda / National Institute of Informatics

Trial Use: e-教室(e-classroom) Projecte-教室(e-classroom) Project: Run by NPO
 Distance learning for children (mainly junior-high school, 1215yrs)
 Several classrooms (math, economics, CG, etc)
z TelMeA for e-教室
 Experimental use of TelMeA
 Classroom for Leaning “agent” as new technologies by using
 Communicating to each other (“BBS” for participants) (demo)z

Hideaki Takeda / National Institute of Informatics

24

TelMeA for e-教室

Hideaki Takeda / National Institute of Informatics

The current status of “TelMeA for e-教室”
Period: c.a. 4 month (2003.1.16-)
z Login users: 64
z Posted users: 24
z Post No.: 297, Post thread No.: 22zHideaki Takeda / National Institute of Informatics

25

Summary
Information technologies, in particular AI can offer newopportunities for communities Reducing constraints of the real world
 Time, space, etc
 new communication ways
 Knowing new related people, communication via agents etc
z They will change meaning or roles of communities
 e.g,
 Very weak communities
 Quick life cycle of communities Belonging so many communitiesz

Hideaki Takeda / National Institute of Informatics

Summaryz

Challenges
 Support of life cycle of communities Create, maintain, diverse, merge, disappear Trust Trust is very difficult
 Trust may be more complicated than the real world…

Hideaki Takeda / National Institute of Informatics

26