Method of Evaluating Contents on the Basis of Community’s
Interest Using Data from Social Bookmark Services∗Y, Fukami
Graduate School of Media and Governance, Keio University
5322 Endo Fujisawa-city, Kanagawa, Japan
yofukami@sfc.keio.ac.jp
T, Sekiya†
Faculty of Engineering, The University of Tokyo
7-3-1 Hongo Bunkyo-word, Tokyo, Japant50861@mail.ecc.u-tokyo.ac.jp
I, Ohmukai and H, Takeda
National Institute of Informatics
2-1-2 Hitotsubashi Chiyoda-word, Tokyo, Japan{i2k|takeda}@nii.ac.jp

Abstract
This research aims toward the development of multifarious information search technique by
utilizing contents which are accumulated to SBM and tags. With the time series analysis oftagging conduct, the method can extract contents which are appraised as sprouting of SBM
users’ interest, as central interest of them, and as the topic of communication between them
respectively. In other words, tags of SBM can bring us appropriate information according to
our intent. Since tags tell us major interest and cutting edge of the users’, the way extractingfrom tag has potential working for profiling users and making use of the information formarketing activity toward them. We conduct approach to make use of actual data to searchfor polyphenic situation which has never been conducted. We think this way must be improved
when you recognize mechanism of SBM as communication platform and the function of tags.
Keywords: SBM, tag, time series variation, new tag ratio, search

1 BACKGROUND
1.1 INFORMATION OVERLOAD ON THE INTERNET
Recently, due to the popularity of full-time internet connection, the increase of networking speed,
expansion of storage capacity, CPU processing ability and decreasing prices makes the amountof data being stored or transferred on the internet to increase (Ministry of Internal Affairs andCommunications, Japan, 2006). On the other hand, the amount of information a person canprocess have its limit as what have been mention by Simon (1996) in past times and, recently
Davenport and Beck (2001). From a user’s perspective, it’s necessary for individuals to accurately
select the data or information that are effective to them. However, paralleling to the increase indata, there emerged fluctuation in data quality. In fact, it is becoming more difficult for us to findnecessary information from the internet.
∗ The data for the analysis for this thesis was provided by EC Navi Company. The authors would like to express
their deepest gratitude.
† Present contact address: Hakuhodo Incorporated

1.2

INTRODUCTION OF INFORMATION ORGANIZATION TOOLS

When gathering information on the internet, most frequently used tool are the search engines.
In Japan, there are some book guiding effective way to find appropriate information using searchengines, especially about advanced search technique using Google: c.f. Tsuda (2004) and other
titles. Like this, many efforts to effectively use the search engines can be seen. However, searchengine shows only a list of the sites and ranking that are relevant to the query. The user needs
to access all the listed sites and locate relevant contents to find the contents meeting his/her own
need. Even if the user can bookmark and save the URL of interesting sites, there are limits on
the amount of bookmarks. It will be also difficult to find information from the overpopulated
bookmarks.
Several technologies, like weblog and social networking service (SNS) have been developed tosimplify the process of information exchange over the internet. Much of consumer’s voice andexperience has been distilled on these kind of sites, then they are collectively called as CGM(Consumer Generated Media). Collecting and organizing massive information on the internet is atime consuming process. On the other hand, recently new tools have been developed and introduce
to ease this process. One of it is the social bookmarking service (SBM).

1.3

CHARACTERISTICS OF THE SOCIAL BOOKMARKING SERVICE (SBM)

Social bookmarking service (SBM) is a service where a website’s URL can be saved on the server.
Basically, it is an implementation of bookmarking function available on the browser over at theserver side. The following is what differs from the normal browser based bookmarking functions:
1. When saving the URL, can provide annotation such as keywords or comments.
2. The URL list and annotation would be publicly accessible.
However, if the URL was just saved only, utilizing this massive accumulated URL later would be
difficult. The bookmarking functions on the browser basically managed by separating data into
folders. On the other hand, SBM, instead of using folders to manage the data, metadata thatwere saved as keywords or comments can be used as keys during search process. Additionally, theaccumulated URL and metadata are publicly available, and possible for other users to use it.

1.4

CLASSIFICATION BY FOLDER AND TAGGING

To organize files and data on a computer, we create folders and classify by directories. Whenclassifying by folders, understanding the hierarchy will be a premise. If the hierarchy was notunderstood properly, it is difficult to search (Marlow et al., 2006) and if multiple folders werecreated, it will be difficult to locate the folder (Jones et al., 2005).
Recent years, the tagging classification method have been introduced and widely used on web
services. The idea of tagging is adding some words (mostly one word as a keyword) as a metadata
to the targeted data or URL. The words used for tagging are called tag. When a tag was issued,
a link automatically formed with the respective list. By tagging, the respective file to be saved
will be grouped and classified. Tags are being used in a variety of areas for classification purpose.
For example, Technorati1 uses tags to classify weblog, and Gmail2 uses tags to classify emails.
With Technorati, it classifies individual’s writes blog articles. While with Gmail, it classifies emails sent and received. With SBM, you can tag websites (URL) and the tagged information
can be shared with other users. In other words, SBM is a massive database for websites withmetadata.

1 Technorati:http://technorati.com/
2 Email service provided by Google Inc (URL: https://mail.google.com)

2 DESIGN TO ANALYSIS
2.1 USER COMMUNITY FORMATIONSWith SBM, the accumulated data is publicly available. The history of all user’s bookmarking andtagging are calculated frequency to display at ranking page. Individual’s collection of accumulatedsites reflects the individual’s preferences. Data aggregated in this service which consists of URL andtags, is a reflection of the user’s preferences. SBM was designed to use other people’s accumulated
information, and we assume that users with similar preferences can come together, forming acommunity with similar preferences.

2.2

VOCABULARY USED ON TAGS AND RECOMMENDED INFORMATION BY TAGSOn the SBM the vocabulary used for tagging represent the classification of the users. The collections of tags are a reflection of their preferences. It is an indication and visualization of the users’
information management system. Collection of the tags is a representation of all the users’ information management system by preferences. Based on this concept, ”folksonomy” a classification
system from bottom up emerged (Mathes, 2004)．The ideas to aggressively utilize tag groups orclassification system as folksonomy were established as information distribution system.
Niwa et al. (2006) proposed the approach to recommend information with using accumulated
tags and relation between tags and users. First, gather information from whole system of the SBM,
and calculate affinity between vocabulary used for the tag and user respectively. Then cluster
vocabulary on the basis of affinity to avoid fluctuation. Lastly, calculate the sense of affinity ofeach keyword cluster to each user, and then recommend user bundle of sites scaled with growthof attention. On the other hand, Ohmukai et al. (2006) proposed collaborative recommendation
method on the basis the classification system not of network allover, but between users with close
relationship, to bring more precise information according to individual’s preference.

2.3

VOCABULARY CONCENTRATION

There were multiple proposals on information search supporting method, as mentioned. However,there were problems with using tags as a medium for sharing information. The vocabulary usedon tags tend to be concentrated (popular vocabularies) to small numbers, and it could be hard
to reach information suited variety of perspectives. For this analysis, we used data from SBM
service, Buzzurl3 operated by EC Navi Company. The top 1.1% of the popular vocabularies usedcovers 50% of all the tags (Figure.1)．It is too concentrated in small number of vocabulary to
make use of tags for the query of searching because to lead disproportionate recommendation.

2.4

CHANGE IN THE TIME SERIES OF THE VOCABULARY BY
TAGGINGTo prevent these concerns, we looked at the frequency used, and also the change in time seriesof the vocabulary used. There is a research to visualize the time series change of the vocabularyused (Dubinko et al., 2006). In contrast, we did not only follow the change, but also checked the
new vocabulary used. We framed a hypothesis that users adopt new vocabulary for tag when
the tagged contents contain something new aspect for them, and we emphasized on these new
vocabulary used.
Tags are used to organize the accumulated contents. Therefore, users tend to use similar tags
to annotate similar contents. Users must not increase the variation of tags more than necessary.
New tags are used on contents that differ from ever accumulated sites, or on contents accumulated
from a different perspective. We want to apply this thinking to all the users. By implementing
this, sites many users annotate with new tags must contain information many users think are new.

3 Buzzurl:http://buzzurl.jp/. The service name was EC Navi Popular News when data was provided

number of usages
tags

Figure 1: Frequency distribution of vocabulary for tags

2.5

NEW TAG RATIO

Newly used vocabularies by the user are named as new tags. Whether the vocabulary is alreadyused or not by anyone, we determine individual tag as new tag or not, according to the each user’s
history of usage. In other words, if other users have ever used the vocabulary, and even if the user
has never adopted it before, we will treat it as a new tag (Figure.2)．

Vocabulary that was newly used = new tag

User B/C already used the tag “food”, No. 7/8, will not considered as a
new tag.
User A, used “food” at No.9, for the first time, hence it will become anew tag.
Figure 2: Definition of new tag.
We classified stored sites as total number of whole tags issued, and ratio of new tags issued byeach URL. We name the ratio as new tag ratio. Therefore, new tag ratio is indicated by formula
below (1), c: contents (site), R: new tag ratio, n: the total new tags, and t: all the tags.
P(c)R(c) = Pn
(c)
t

(1)

We consider site catching the attention of many users can be bookmarked by many users, andcontents that have high new tag ratio include new information many users are interested in.

3 ANALYSIS SUBJECT
3.1 SELECTING SBM ANALYSIS SUBJECT
Based on the assumption that we have showed up to now, we will initiate a verification analysisof actual stored data on SBM: accumulated URLs and tags.
We used Buzzurl(Figure.3)4 ，for analysis, as stated previously. The concept of this service,similar to digg5 is to register news related site, and extract contents with new information. Thisis suitable for our intent for the analysis. Buzzurl is operated by a Japanese corporation called
EC Navi Company. This site is in Japanese and majority of the users, are using Japanese.

Figure 3: Buzzurl

3.2

CHARACTERISTICS OF BUZZURL(FORMERLY EC NAVI POPULAR NEWS)3.2.1 FUNCTION TO GENERATE FURTHER COMMUNICATION
Buzzurl does not only specialize in genre of contents, but also provide users functions whichcharacterize the site suitable for handling news contents. The first user to register a new sites isdisplayed largely (Figure.4), and this promote other users to register new sites.
They provide a service to promote communication between users, too. Users can register
someone with similar interests or gathering many contents you valuate good as a favorite user.
The list of sites registered by Favorite Users is displayed in one’s individual page. Also, thesite displays the users who tend to register similar sites to one’s accumulation as Synchro-user.
Users can simply find other users with similar interests and preferences. Buzzurl generates further
communication among users with using these functions.
3.2.2 TAGGING SUPPORTWhen registering a site, candidates for tag are displayed. These consist of a list of vocabularyused on similar sites or one’s own history of usage (Figure.5)．These tagging supports lead users
to select vocabularies ever used by themselves or other users.
4 Buzzurl:http://buzzurl.jp/
5 digg:http://www.digg.com/SBM, specialize in news related contents, providing services in English

ranking of popular site ：the user who bookmarked the site first isindicated.

Figure 4: Site ranking of the highest registered number: user that registers first on the respective
site will be displayed

candidate other user has ever used for the site

candidate the user him/herself has ever used

Figure 5: Tagging support

3.3

SUMMARY OF THE ANALYZED DATA

Data used for analysis is the registered bookmark data from October 2005 to December 2006.
During this time frame, users registered to this site were 1,221 users. There were 74,128 typesof registered URL, and registered number was 139,602. Out of 139,602 bookmarks, there were
264,058 tags. 31,076 types of vocabularies used, per 1 bookmark the average number of tags were
1.9.

3.4

USER CHARACTERISTICS

The type of users that registered the most number of URLs were professionals of planning &marketing (11.7%), researchers, developers and technicians (10.8%), followed by engineers (9.9%).
Top 3 categories of business registered over 30% of the total users. From the user population
perspective, the dominant were the housewives6 (18.6%), however they did not use the SBM functions aggressively. The core users were those professionals involved with planning and marketing
6 The reason for having many housewives was that EC Navi Company, which operates Buzzurl, originally was a
price comparison site. Furthermore, it was due to the fact that the members from the price comparison site were
issued a Buzzurl account without processing a new membership registration.

or engineering.
occupation

bookmarked
sites(%)

number ofusers(%)

allplanning, marketing
technical expert
engineerpart time job
homemaker
student
PR, advertising, design
sales person
self-employed individualcompany executive
managerial worker
clerical pos
general secretary, personnel worker
civil officer
accountant
specialist personnel
fringe worker
teacher
Agriculture, Forestry and Fisheries
Others16382sites
11.7
10.8
9.9
8.8
7.8
7.6
4.7
4.6
3.4
3.2
2.3
2.0
1.4
0.5
0.5
0.4
0.4
0.1
0.0
20.1

130586users
1.7
9.1
3.9
9.0
18.6
5.4
1.1
8.9
5.6
1.9
2.3
5.0
2.7
2.8
2.2
2.1
2.9
0.9
0.2
13.6

averagenumber ofbookmarking
56.3
9.4
20.4
7.8
3.4
11.3
33.7
4.1
4.8
13.5
7.8
3.2
4.1
1.5
1.8
1.5
1.1
0.7
0.7
11.7

Table 1: number of sites bookmarked by Buzzurl user by occupation

4 TAG ANALYSIS
4.1 URL REGISTRATION DISTRIBUTION
The frequency distribution of the registered URL is shown in Figure.1 before, a small amount ofsites were registered intensively and a large number of sites were registered by small number ofusers, representing a long tail distribution.
On the tags of SBM, there were problems with ”polysemy,” ”synonymy”, ”basic level variation”(Marlow et al., 2006). Yet for this analysis, if strings differed, we treated as a different
vocabulary. For example, ”EC navi” and ”EC Navi”, were counted as separate vocabulary.

4.2

TREND OF THE VOCABULARY USED FOR THE TAGSWhen analyzing a large number of tags that are being used, ”Google”, ”Web2.0”, ”YouTube”,”Microsoft”, computer or internet related vocabularies were frequently used in most cases. Also,”advertising” came in 14th, responding to the characteristics of the people who are actively registering the sites. In other words, shows the interests of people that belong to planning & marketingand engineering business categories.

4.3

SITE DISTRIBUTION BY NUMBER OF REGISTRATION AND
NEW TAG RATIOThe average tag per site was 8.4, and among that the amount of tags with new vocabulary, were3.0. In other words, the new tag ratio was 33.1%. Next, we looked at the relationship of the total
amount of tags per site and the amount of new tags. Figure.6 shows a comparison of total amountof tags per site to the ratio of new tags.

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

tag
googleneta (troll)
funny
professional wrestlinggraphical content
SNS
Blog
showbizfootballFIFA World Cup.
music
web2.0
youtubeadvertisinggame
motion picturesocialnews(in Japanese)
sportsEC navi
news(literal)
Microsoft
mobile phonemovie
securitymixi(SNS in Japan)
convenient
searchgourmetprofessional baseballnumber of usage
4,004
3,041
2,493
2,419
2,278
2,086
1,826
1,773
1,685
1,603
1,594
1,580
1,513
1,426
1,355
1,344
1,282
1,262
1,199
1,164
1,131
1,115
1,053
1,050
1,011
1,002
897
888
880
856

%
1.52
1.15
0.94
0.92
0.86
0.79
0.69
0.67
0.64
0.61
0.60
0.60
0.57
0.54
0.51
0.51
0.49
0.48
0.45
0.44
0.43
0.42
0.40
0.40
0.38
0.38
0.34
0.34
0.33
0.32

%(accumulation)
1.52
2.67
3.61
4.53
5.39
6.18
7.54
7.54
8.18
8.79
9.39
9.99
10.56
11.10
11.62
12.13
12.61
13.09
13.54
13.98
14.41
14.83
15.23
15.63
16.01
16.39
16.73
17.07
17.40
17.73

Table 2: Top 30 vocabularies used for tags
Using the total amount of tags and new tag ratio on the site, we would like to extract themain interest and the new concerns of the users. Regarding the extraction, the avobe standard
was applied based on the average and standard deviation for the total tags and new tag ratio(Table.3).
(1) Sites with many total tags, and little amount of new tags:
the standard was a total amount of tags, over 20, and the new tag ratio, less than 20%.
(2) Sites with many total tags, and new tags:
the standard was total amount of tags, over 20, and the new tag ratio, over 50%.
(3) Sites with little amount of total tag, and many new tags:
the total amount of tags, less than 8, and new tag ratio, over 50%.
According to each standard, we will look at the top 20 registered sites and the top 20 frequently
used vocabulary on the tags.

5 RESULTS
5.1 SITES WITH MANY TOTAL TAGS AND LITTLE AMOUNT OF
NEW TAGSFirst, we will look at (1)sites with many total tags, and little amount of new tags. On Table.4, islisted the top 20 sites in total tags which the total was over 20 and new tag ratio was less than

AverageSDnumber of all tags annotated
8.42
9.20

number of tags with new vocabulary
2.97
7.34

new tag ratio (%)
33.1
27.3

Table 3: average and standard deviation: number of annotated tags
nu 120
m 100
be
r 80
of 60
al
lt 40
ag 20
s
0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

new tag ratio

Figure 6: number of tags and new tag ratio
20%. Most sites within the top 20 were ITC or marketing related topics. We can assume that itcovered the contents of interest of the active users.
Then, what kinds of vocabularies for tagging on these sites are used? On Table.5 the top 20
most used vocabularies as tags are listed. When compared to the top tags of the overall registered
URL(Table.2), the majority of the registered with low new tag ratio has 2 characteristics.
First, it is mainly covered by ITC or marketing related vocabularies and other contents arefew. While there are vocabularies as ”neta (troll)”, ”funny”, ”professional wrestling”, ”graphicalcontent” in the list of the top 30 sites overall, you cannot find these words in the list of this segment.
In the top 5, internet related vocabularies such as ”Google”, ”YouTube”, ”SNS”, ”Web2.0”, and”Yahoo!” were listed.
Second, the top vocabularies used hold a high ratio on tagging. Of the overall tags, thevocabularies that covered the top 30 had a ratio of 18%, a little short of 20%. Sites on registered
new tag ratio that are low, of the top 20, it was 31%, and exceeded 30%. Therefore, it can be said
that sites with many tags and low new tag ratio, and the tags annotated to them reflect the coreof the active user’s interest and concerns.

5.2

SITES WITH MANY TOTAL TAGS, AND MANY NEW TAGS

Next, we will look at (2)sites with many total tags, and many new tags. The contents of the
top 20 sites (Table.6) were mainly ITC related, however there were contents like ”taste rankingof favorite Umai Bo (Traditional Japanese molded confection)”, ”What is your favorite Miyazaki
movie?”, ”Doraemon Finale, read in comic”, including contents from daily life or culture. Then,
what kinds of vocabularies were used on the tags for these sites?
On Table.7, the top 20 vocabulary most frequently tagged is listed, and the number 1 was theoperating company ”EC Navi(in Japanese)”. When compared to Table.6, EC Navi related titles,
it shows the concern and interests in respect of the operating company. In other words, loyalties
are starting to form for companies that provide SBM platform and this service. From number 2,
vocabularies like ”neta (troll)” ”funny”, ”news”, ”ranking”, are listed. These words are used tohave fun for the moment, or in order to share a topic with others, therefore it cannot be considered
as users prior concerns. In other words, these tags were a medium for communication between
users, rather than individual’s interest and preferences.
Additionally, when site and contents of the tags are considered, a platform called SBM, forms
loyalty, which promotes information accumulation. Furthermore, SBM through sites and tags is
considered to be a medium of communication. Henceforth, we believe that there is a need to

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

title of site
Google pledges $900 million for MySpace honors
Japan.internet.com column/ search engine marketing
ITmedia News：mixi (SNS in Japan) and YouTube affect websphere in Japan
Google Moves to Sell Space for Video Spots on Network of Web Sites–New YorkTimes reported - CNET JapanNTT DoCoMo cooperate with Google to provide mobile search- CNET Japan
misconception about amazon.com and ”long tail” - guide to new economy on theInternet - nikkeibp.jp
Ameba (blog service in Japan) redesign of services - CNET Japan
Any: VideoSNS for mobile phone increase of capital - CNET Japan
ITmedia News：banner ad in mixi (Japanese SNS), number of impressed overtimes second to Yahoo! Japan
Japan.internet.com Web marketing - Can alliance of Yahoo! and eBay threat
Google ？
Buzzurl has new feature - CNET Japan
Any: Japanese SNS start to compatible for video - CNET Japan
4 requirement of Web2.0 site - GIGAZINE
ITmedia News：Microsoft report to kick around taking over eBay
ITmedia D mobile：mobile phone featuring Gachapin (Japanese TV character)released
media and PR (blog site): digg and YouTube lounch new service promised tosuccess
Using ”Google”(verb) in Japanese, OK? - CNET JapanEC navi company launch SBM compatible for video - CNET Venture View
way to access Buzzurl via mobile phone｜What is Buzzurl? (blog site)
Google’s antisocial downside - CNET Japan

number of all tags
81
52
50
47

new tag ratio(%)
18.52
11.54
16.00
14.89

47
46

19.15
13.04

45
45
42

6.67
17.78
19.05

40

17.50

39
39
38
37
36

7.69
17.95
5.26
16.22
19.44

35

14.29

34
34
34
33

17.65
14.71
14.71
18.18

Table 4: Top 20 sites title of total tags: sites with many total tags and little new tags
investigate and verify the user’s awareness.

5.3

SITES WITH LITTLE AMOUNT OF TOTAL TAG, AND MANYNEW TAGS

Finally, we will verify (3)the sites with little amount of total tags, and many new tags. On Table.8,
that shows the top 20 of the total tags, internet related contents were at number 10, and marketing
related at number 19, each being included once.
The top vocabulary that was used in these sites (Table.9) were mainly dealing with hobbies
and culture, such as ”movie”,” entertainment”, ”music”, ”world cup (football)”, ”football”, and”sports”. The subdivisions of these various genres were based on the individual’s interest andpreferences. It is also a field where it constantly transmits the newest trends. The vocabulary in
the 3rd place was ”new products”. We can consider that the contents in these sites are evaluatedas new by individual’s interests and preferences.

6 CONCLUSION
6.1 SUMMARY OF ANALYSIS
In this research, by analyzing the accumulated data and metadata in the SBM, we attempted todevelop a new information search method. By initiating implemented metadata equal to tagging
history time series analysis, and using a new tag ratio as an indicator, we classified the accumulatedsites of the SBM into the following 3 categories, not only to extract interest and preference of
the user community, but also show us the possibility to extract contents based on our target inaccordance with the intended use.
(1) Sites with many total tags, and little amount of new tags
⇒Contents centered on active user’s interests and concerns(2) Sites with many total tags, and many new tags
⇒Topics related to medium of communication between the users

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

tag
GoogleYouTubeSNS
Web2.0Yahoo!
advertising
video (in Japanese)
CNET
Yahoo
searchMobileEC navi
Google(in Japanese)webvideo (literal)
video delivery
ITmedia (Japanese ITC news site)
mobile phone
search engine
ranking

number of usage
26
21
17
16
14
12
11
11
11
10
9
8
7
6
6
6
6
5
5
5

rate(%)
3.75
3.03
2.45
2.31
2.02
1.73
1.59
1.59
1.59
1.44
1.30
1.15
1.01
0.86
0.86
0.86
0.86
0.72
0.72
0.72

accumulation（%）
3.75
6.77
9.22
11.53
13.54
15.27
16.86
18.44
20.03
21.47
22.77
23.92
24.93
25.79
26.66
27.52
28.39
29.11
29.83
30.55

Table 5: Top 20 tags: sites with many total tags and little new tags(3) Sites with small amount of total tags, and many new tags
⇒Contents that were evaluated as new by individual’s interests and preferences

6.2

POTENTIAL OF WAY TO SEARCH ON PARTICULAR PURPOSE

SBM is mainly used for recognizing latest topic among the users’ community by ranking on the
top page, or searching useful contents screened by other users now. However, the result of analysis
bring the possibility that you can select way to find contents to meet one’s end among 1) tocatch up with the latest fashion of the community, 2) to find contents being useful for medium ofcommunication, or 3) find novel topic faster. The value created by SBM is extended from just thefunction of filtering contents based on wisdom of crowd (Surowiecki, 2004) to vitalization of thecommunity by provide topic to stimulate communication among the members.

6.3

REMAINED TASKSAt present, this research shows the results from only analyzing the accumulated data in the SBM.
There is a need to analyze results in accordance with the interest and concerns of the users, andto consider on what degree users’ loyalty regarding the SBM platform is formed through user’s
survey. Through these surveys, by analyzing from various aspects the mechanism of gathering
loyalty SBM will accumulate data and we believe that this can lead us to the understanding of
the sophisticated tagging function and the developing of its application method. Our research wasan unprecedented attempt to use SBM’s actual data and develop a versatile information searchmethod. We need to analyze the user’s information search organization activity and behavioral
psychology of communication to understand the effect of the platform design. Also the tag, etc
and characteristics of the metadata was not fully analyzed. With these perspectives, and as theunderstanding for the SBM at all levels advance, making it possible for the method we propose inthis research to be brushed up.
Finally, we would like to start by thanking Toshiyasu Tsushima and Akiyo Sugawara from EC
Navi Company, for providing the data that was the foundation of our analysis, and everyone from
EC navi labs: Buzzurl development operation representative. With this data, we were able toapply this actual data of SBM, which there are only few cases world wide, to develop a method.

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

title of site
Cyber Agent inc. hire Horie (ex-CEO of Livedoor inc.) as CTO: nikkansports.com
ranking of popular flavor of ”Umai-bo”(Japanese molded confection) - goo ranking
list of software which adverse Windows OS works - GIGAZINE
Whit is your favorite movie directed by Hayao Miyazaki?
the last episode of Doraemon in catoon
CEO of EC navi compare Kakaku.com to Yahoo!, EC navi to Google - CNETJapan
EC navi redesign SNS service - CNET Japan
Opt developed new way of creating advertising on the basis of classificating consumer in 3 segments - CNET Japan”SaaS” ”social DB” ”long tail”: keyword of Drecom’s strategy of development CNET Japan
jig.jp provide tool to modify schedule in mobile phone via PC
ITmedia News：Cyboze Inc. to IPO in the first section of the Tokyo Stock Exchange
Amazon Japan start commission sale - CNET Japan
nakata.net – official site of Hidetoshi Nakata
Netage group’s use new brand name ”Saaf” for all service line up and integrateuser account with RSS Ads, Inc - CNET Japan
Japan.internet.com Web business - how to title and write explanatory text
EC navi / Vote; Do you use mixi？
ITmedia News：NTT break into video share service like YouTube
careers to study appropriate fee for connecting the Net - CNET Japan
Hatena inc. to foray into U.S and develop services in English: IT-PLUS
scene like fiction

number of all tags
121
77
72
71
67
66

new tag ratio(%)
52.9
68.8
61.1
57.7
68.7
51.5

65
59

50.8
55.9

58

55.2

55
54

67.3
72.2

53
52
52

58.5
57.7
59.6

52
51
51
49
47
47

75.0
54.9
51.0
77.6
70.2
66.0

Table 6: Top 20 sites of total tags: sites with many total tags and many new tags

References
Davenport, T. H. and Beck, J. C. (2001). The Attention Economy: Understanding the New
Currency of Business. Harvard Business School Press.
Dubinko, M., Kumar, R., Magnani, J., Novak, J., Raghavan, P., and Tomkins, A. (2006). Visualizing tags over time. In WWW ’06: Proceedings of the 15th international conference on World
Wide Web, pages 193–202, New York, NY, USA. ACM Press.
Jones, W., Phuwanartnurak, A. J., Gill, R., and Bruce, H. (2005). Don’t take my folders away!:organizing personal information to get ghings done. In CHI ’05: CHI ’05 extended abstracts on
Human factors in computing systems, pages 1505–1508, New York, NY, USA. ACM Press.
Marlow, C., Naaman, M., Boyd, D., and Davis, M. (2006). HT06, tagging paper, taxonomy,
Flickr, academic article, to read. Proceedings of the seventeenth conference on Hypertext andhypermedia, pages 31–40.
Mathes, A. (2004). Folksonomies-cooperative classification and communication through shared
metadata. Technical Report, University of Illinois Urbana-Champaign.
Ministry of Internal Affairs and Communications, Japan (2006). White book on information andcommunication technology 2006.
Niwa, S., Doi, T., and Honiden, S. (2006). Web page recommender system based on folksonomy
mining. Journal of Information Processing Society of Japan, 47.
Ohmukai, I., Matsuo, Y., Matsumura, N., and Takeda, H. (2006). Realizing Community Web
Platform. In The20th Annual Conference of the Japanese Society for Artificial Intelligence.
Simon, H. A. (1996). The Sciences of the Artificial. MIT Press, Cambridge, Massachusetts, third
edition.

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

tag
EC Navi(in Japanese)
neta (troll)
funny
web2.0news
rankingEC
entertainment
ECnaviPC
gourmetfar-outsocialPC(in Japanese)
ITC
advertisingSNS
Blog
Web service
The Internet / ITC

number of usage
11
9
8
7
7
6
6
6
5
5
5
4
4
4
4
4
4
4
4
3

rate(%)
1.33
1.09
0.97
0.85
0.85
0.72
0.72
0.72
0.60
0.60
0.60
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.36

accumulation（%）
1.33
2.42
3.38
4.23
5.07
5.80
6.52
7.25
7.85
8.45
9.06
9.54
10.02
10.51
10.99
11.47
11.96
12.44
12.92
13.29

Table 7: Top 20 vocabularies used on new tags: sites with many total tags and many new tags
Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and
How Collective Wisdom Shapes Business,Economies, Societies and Nations. Doubleday.
Tsuda, D. (2004). Doing Google - Searching Information on the Web by Google; the Search Engine.
Mainichi Communications Inc.

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

title of siteextinct and threatened specie: Humboldt penguin increase too much in Japan :science : YOMIURI ONLINEBADSTRING【SPA! (magazine for young men in Japan】kuma-eri (a woman arrested on arson)
recruiting for government-paid secretary for Taizo Sugimura (House of Representatives member in Japan)
kawasaki’s dialy - meet shokotan (Japanese idol)for beautiful human life(blog) - spy photo put on the magazine without permission
and to abuse human rights of otaku-woman
you see something on Travolta’s hairline : ABC（Amarican baka commedy）commity
remark of arrested announcer sneaking camera shots of the woman
Happa-tai get tobe world famous
Google to offer free analytics service - CNET Japan
Digg View
discovered bags in the soft of mobile phone manufactured by sharp inc and to becorrected
sites for downlording wallpaper for high difinition monitor - GIGAZINE
Emily Chang - eHub Interviews Lingr
code.mfac.jp - Trac
topics for blog except meal nor readings, you can make lots of entries (Lifehacks)：
Nbonlinenantara kantara (blog): Pluto,,,,,,,not planet
Also the capacity 10G byte appears! –The web mail competition which is converted keenly - CNET Japan
access hours for ”CGM”; blog, SNS, BBS extends - - Internet advertising workshop
investigation
Drawing 3D picture with Acrobat Reader

number of all tags
7

new tag ratio(%)
71.4

7
7
7

57.1
57.1
71.4

7
7

57.1
57.1

7

71.4

7
7
7
7
7

85.7
85.7
57.1
71.4
57.1

7
7
7
7

71.4
71.4
57.1
57.1

7
7

71.4
71.4

7

100.0

7

100.0

Table 8: Top 20 sites: sites with little total tags and many new tags

rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

tag
movie
googlenew productentertainment
neta (troll)mobile phone
Microsoft
Blog
socialthe Internet
music
game
SNS
Blog
world cup (football)
footballsecurity
sport
computer
ITC

number of usage
47
46
44
39
37
37
32
32
32
31
31
27
27
27
27
26
25
24
24
24

rate(%)
0.43
0.42
0.40
0.36
0.34
0.34
0.29
0.29
0.29
0.28
0.28
0.25
0.25
0.25
0.25
0.24
0.23
0.22
0.22
0.22

accumulation（%）
0.43
0.85
1.25
1.60
1.94
2.28
2.57
2.86
3.15
3.44
3.72
3.97
4.21
4.46
4.70
4.94
5.17
5.39
5.61
5.83

Table 9: Top 20 vocabularies used on tags: sites with little total tags and many new tags