東海大学紀要情報通信学部
Vol.11,No.2,2018,pp.34-44
Proc.
Schl. ITE Tokai Univ.

論文

Vol. xx,No.xx ,20xx,pp.xxx -xxx

A Study on Cluster Variation of Twitter Hashtags during a Whole Day
byNartlada BHAKDISUPARIT *1 and Iwao FUJINO*2( received on Sep. 28, 2018&

accepted on Jan. 10, 2019 )Abstract
Our study aims to verify the cluster variation of Twitter hashtags according to their word distributions. By gathering tweets
from Twitter sample data and saving tweets in a time period of each hour separately, we extract hashtags and calculate theirword distributions for each time period. Furthermore, by using Ward’s hierarchical method, we cluster hashtags to a structureof groups according to the Jensen-Shannon divergence between any two hashtags and show the clustering results withdendrogram, which represents the hierarchical structure of all hashtags. Finally, we verify the differences of clustering results
between each time period to analyze the details of cluster variation. The achievement of this study will contribute to develop
marketing campaign or advertisement on Twitter.
Keywords: Twitter hashtags, word distribution, Jensen-Shannon divergence, Ward’s hierarchical clustering method, marketingon Twitter

1. Introduction

million Euros for cancer research in only one week. This
hashtag didn’t start for charity project at the beginning, but
after it went on trend its social media team decided to involveand ask people to donate via this hashtag text by responding
rapidly and creating a sense of community around the
movement [3]. This story told us how efficient that using
hashtag for advertise campaign is.
However, because hashtags are created freely fromworldwide, without any specific standard and role, it is not
simple to understand the content of each hashtag. As there isno any hashtag dictionary, normally Twitter’s users may haveto read hundreds of tweets in order to understand a newhashtag in Twitter trend. A disadvantage that reduces the
chance of effectiveness in online social network marketing
advertisement is an enormous data flow in online social media.
Because many trends in online social media came and gone
rapidly, time becomes another remarkable factor in thedeveloping process. In addition, if we spend too much time on
research and development campaign phrase, we may lose achance to catch the relate hashtag in time. How can we
respond rapidly to catch up online social network interesting
trend?
Today, especially in online marketing, new approaches areneeded to advertise campaign, and propaganda. Our research
focuses on analyzing hashtags gathered from tweets. We aim
to understand hashtags’ dynamic meaning and find similar
hashtags in terms of meaning, which will be significant forproviding an advertisement to target customer group before
they leave the topic trend on Twitter. We also concern that
hashtags have their own life cycle in this paper. Based on ourprevious study [4], we implement the comparing procedure in
addition to our last process procedures. Hashtag has a deep
association with real-time event, which is the main concept ofTwitter. Marketing development usually takes a time toresearch an associate hashtag and then launch a marketing
campaign. However, many campaigns were failing to catch

Big data is a term applied to data sets whose size or typeis beyond the ability of traditional relational databases tocapture, manage, and process the data with low-latency. It
has one or more of the following characteristics: highvolume, high velocity, or high variety. Big data comes frommany sources e.g. sensors, video/audio, log files,
transactional applications, web, online social media, andweb browser cookie data. Analysis of big data will help us
to provide more accurate prediction for to support
decision-making process [1].
Nowadays the online social network is typical place that big
data playing a significant role in many ways in human life.
Twitter is a microblogging service that allows people tocommunicate with a short message called Tweet. Twitter had
grown from 30 million users in 2010 to 335 million users in
the first quarter of 2018 [2]. Twitter is different from other
online social network. Without any relation between each pairof users beforehand, Twitter relationship model provides asimple but powerful tool to create and maintain a specificcommunity. In the community, users can use hashtag tomention to their specific topic, where hashtag is a specificstring beginning with symbol # (sharp) on Twitter.
Twitter’s relationship model allows user to keep up with thelatest happening of other users. By using hashtag users canshare their comments and information with Twitter’s online
community. Twitter also helps people raise campaign for theircharity events. One of a successful campaign which stared
from hashtag was #nomakeupselfie. This hashtag raised 8*1 Graduate School of Information and Telecommunication,
Course of Information and Telecommunication Engineering,
Master’s Program*2 School of Information and Telecommunication Engineering,
Department of Communication and Network Engineering,
Professor

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Nartlada BHAKDISUPARIT and Iwao FUJINO

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the relate hashtag before it fades out from Twitter’s hashtag
trend list. In fact, as Twitter users may use different hashtag toreach into the same topic, the related hashtag for one topic canbe more than one hashtag. Problem is how can we know thesimilar of each hashtag’s content. After finding out thisproblem, the objective in our study aim to know more about
the variation of Twitter hashtag in a minimum time, so we canmanage and develop proper campaign or advertisement on
Twitter.
The rest of the paper is organized as follows. Section 2
briefly reviews current works related to Twitter influence andclustering Twitter hashtags. Section 3 shows the data processprocedures and explains each procedure. Section 4 shows asummary of gathered tweets, words distribution of hashtags
and clustering results of hashtags with dendrogram. Finally,
section 5 concluded our work and gives a few directions for
future work.

2. Related Works
Yan Mei, Weiliang Zhao and Jian Yang mentioned how to
select seed user or influential users to help the propagation of
the advertising information [5]. There were two ways forsocial media advertising as (1) advertisers can take theadvantage of various users’ information, including theirinterests, demographics (such as gender, age, race, level of
education, etc.), and their behaviors in the social networks, todeliver the advertisements directly to the target audiences; (2)advertisers can identify some influencers in the social
networks as the seeds and propagate the advertisements
through these seed users’ social circles. In this study they told
us about influence maximization problem which was: (1) the
influence probability model, which determines how influence
probabilities between users are calculated; (2) the influence
diffusion model, which reflects how influence propagates in
the networks; (3) the seed nodes selection algorithm, which isused to select the influential users from the social network inorder to maximize the expected influence spread. When acompany wants to launch a new advertising campaign on
Twitter, it is needed to select some influential users (i.e. seedusers) to help the propagation of the advertising information.
In this paper, a new influence probability model and the
diffusion model have been proposed. Comparing with existing
works, these models can better reflect the real situations ofadvertising information spread on Twitter. The cumulative
probabilities are calculated according to users’ action history.
Lotfi A. Zadeh, Ali M. Abbasov and Shahnaz N. Shahbazova
interested in an analysis of hashtag from the point of view of
their dynamics [6]. They identified groups of hashtags thatexhibit similar temporal patterns, look at their linguistic
descriptions, and recognize hashtags that are the mostrepresentative of these groups, as well as hashtags that do not
fit the groups very well. They used a fuzzy clustering algorithm

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to gain insight related to temporal trends and patterns ofhashtags popularity concluded that the changes of hashtags
popularities are an attempt to look at the dynamic nature of theuser-generated data. The application of fuzzy clustering shown
here provides several interesting benefits related to the fact thatcategorization of hashtags is not obvious.
Also, Orianna Demasi, Douglas Mason and Jeff Ma studied
about understanding communities via hashtag engagement [7].
In this study, they told us that hashtag had the semantic
meaning and this meaning can also be ambiguous due totemporal dynamic, e.g. the city of a sports team is often used
to refer to the team during the match and not the geographic
location. They separated hashtag into 4 dynamic classes byused K-means clustering algorithm. Also, they considered
about bot and spammer in their experiment which are entitledthat send out an abnormally high number of Tweets with title
value and often not receptive audience. In result stage wasshown in 4 global clusters. The cluster was validated byextensive manual inspection of a randomly selected subset ofhashtags. 4 classes of clustering results were stable (daily
chatter), periodically recurring, single event and irregularstochastic patterns. Tweet with hashtag received a Retweet ora favorite, human engagement. Higher engagement means thata large portion of Tweets with that hashtag received somehuman engagement and implies that the hashtag more
successfully engages users on a broad scale.

3. Process Procedures
3.1 Software environment
The R environment is a convenient software facility fordata manipulation, calculation and graphical display. R
developer prefers to present R as an environment within
which statistical techniques are implemented. R can beextended with the package [8]. Package streamR and stringR
are the main packages we used in this study. Package streamR
provide a function to access Twitter API via R and gather
tweets sample [9]. This package was not only gatheringfunction for our study. Because streamR also provides animport function which can import gathered data into R data
frames. Package stringR provides a function to operate string
format data [10]. This package plays the main role in the data
cleaning procedure, extracting string data with assign format
e.g. URL, stop word, encoded characters which can beprocessed in the program and replace this matched format
with a blank data. Also, this package has been using in thehashtag extracting procedure. By assigning a hashtag format
which starts with # (sharp) following with a string of word
and number. In addition to these two packages, we also usedsome other R packages, which will be mentioned in thefollowing subsections.
3.2 Summary of process procedures
Our study purpose was achieved by the following process

A Study on Cluster Variation of Twitter Hashtags during a Whole Day

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procedure.
1.
Collect tweets sample for 24 time periods and clean
these tweets
2.
Extract hashtag and create top hashtag frequency list
3.
Extract tweet samples for each top hashtag
4.
Calculate word distribution for each top hashtag
5.
Calculate Jensen-Shannon divergence between eachhashtag
6.
Cluster hashtags by means of Ward’s hierarchicalclustering method and generate dendrogram fromclustered results
7.
Compare the result for each time period and makeconclusion.
3.3 Collect tweet samples and clean data process
The sampleStream is a function in streamR package. Thisfunction use for collected tweets via Twitter’s API and savedata in to json file. Because of the limited in hardware
resource, we collected tweet data in small json files. Each json
file is contained tweet during the one hour collecting process.
After gathering process, we import and extract only tweets
which posted in English language. Next, we implemented thedata clean process simplify data. This process will prevent
unexpect character encoding problem during the analyzing
procedure. After creating a data frame, we also create a Rdata
file as known as .Rda file to backup gathered data. Rdata fileis a binary file in a compact size and can be stored any time of
R structure and fast to import again by R language.
3.4 Extract hashtags and create top frequent hashtag list
We used the data frame obtained from the previous
procedure to extract all hashtags from tweet samples. Hashtag
has an assigned format. All hashtag will begin with character# (sharp) and then followed by a string of characters or
numbers. According to this format, we can extract all hashtagsfrom tweets sample. The stringR is a function use to extract
string data which has a matching format. In our study we use
stringR to extract all hashtag from gathered tweets. After
extracting all hashtag string data, we convert these data into
term document matrix format. Then we calculate thefrequency of each hashtag string data by summary same
hashtag in the term-document matrix. We used function
TermDocumentMatrix [11] from package tm [12] toimplement the calculation frequency of each hashtag. Then we
sorted all hashtags by frequency and make a hashtags list data
frame for each json file of time periods for preparing for the
next procedure.
3.5 Extract tweet samples for each top hashtag
According to the hashtag list, we created each hashtag
subset data frame from tweet data frame. We used function
grep from package base for searching matching argument
pattern within the element character vector in data source[13]. A tweet which has a matching hashtag in the list will

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be pasted into a new data frame. Each hashtag data frame
will contain tweet with itself hashtag preparing for the next
process.
3.6 Calculate word distribution for each top hashtag
From each hashtag’s data frame, we calculated its word
probability distribution for every word in each hashtag’s data
frame and save the result into new column in its own data
frame. By counting the appearance number of each word in all
tweets and calculate its probability as following Eq. (1).
��� ���������������

���� � ��� ������ �� �����������������������

(1)

3.7 Calculate Jensen-Shannon divergence and generate
dendrogram from clustered results
This procedure will calculate divergence between eachpair of hashtag’s word probability and then cluster themaccording to the results of divergence.
For two given probability distribution P and Q, thedefinition of Jensen-Shannon divergence (JSD) is shown in
Eq. (2).

��� �� � �� �
where

�

� � �� � ��
�1�� �� � ���
2 ��
� ��� ��� � ���(2)

It is a measure of how much one probability distribution
diverges from another probability, or in reverse words, it isa measure of how much one pair of the probability
distribution is similar to each other. We calculated JSD
between all pair of distribution of hashtag. However,Jensen-Shannon divergence is a symmetrized and smoothedversion of the Kullback-Leibler divergence [14], defined asshown in Eq. (3). Result calculated by Kullback-Leibler
show an asymmetrical result.
������� �� � �� � ∑� ���� log ����(3)

First, word distribution data frame of top 15 hashtags
which prepared from the previous procedure are merged
together into the same data frame. The divergence willcalculate from each pair of hashtag’s word distribution,
result from this process will be given in the 15x15 matrix
with 225 results of divergence. These divergence results
calculatebyJensen-Shannon
divergencewith
CalcJSDivergence function. CalcJSDivergence function isa function used to calculate the Jensen-Shannon divergencefrom the row or column of the numeric matrix or for two
numeric vectors [15].
This procedure of JSD and clustering hashtags according
to its value and show the result with dendrogram.
Dendrogram in this procedure creates by function plot frompackage graphics. This function can provide many types ofNartlada BHAKDISUPARIT and Iwao FUJINO

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graph according to R object. Combining with function
hclust from package statscalculate a hierarchical cluster on
a set of similarities [16].

Table 1. The number of gathered tweets in each period

3.8 Compare results for each time period and conclude
the results
From the previous procedure, we can understand hashtag
clustering by Jensen-Shannon divergence in hierarchicalcluster dendrogram. In our study we considered in 4 points
from experimental results shown in the following:
1.

2.

3.

4.

The volume of each time period shows us a peak andvalley of tweets online traffic. These data can use to
analyze when to lunch a campaign on Twitter peakusage time.
Hashtag top list uses to compare each period help us
to understand the flow of Twitter trend via hashtag top
list. In different a period shows us a different trend.
Jensen-Shannon divergence clustering show group ofhashtags which clustered by its own word distribution.
Each group clustered and show how similar of eachhashtag. Hashtag is a dynamic generate content or
meaning of each hashtag can be change anytime. In
this point, we can determine hashtag contain or
meaning in that analyze time. Considering of hashtag
meaning is a significant process, we cannot use
hashtag as a tool for advertisement or marketing plan
if we use wrong hashtag that does not match withadvertisement categories the advertisement or
campaign may not reach to target customer groups.
The last point we considered is about the hashtag life
cycle. Many hashtags always appear in gathered data.
We determined these hashtags as a hashtag which has
a long-life cycle. We defined hashtag life cycle as acycle that hashtag still in part of top hashtag list.

4. Experimental Results
4.1 Summary of gathered tweets
We gathered 974,967 tweets which use English languagefor 24 hours on 17th September 2019. Unfortunately, we
found a problem in period No.15 gathering procedure, thenetwork connection during the gathering process had been
cut out after this section start 15 minutes. The number ofgathered tweets in each section is shown in Table 1. From
this table, we can analyze the variation of the tweets
amount for each time period, except for period No.15. Also,a time chart in this table is shown in Fig.1. From this chart,
we found that the lowest tweet amount started around 16:00
o’clock and the highest tweet volume started to around
19:00 o’clock. According to this result, we can choose abetter time to launch a new online social media
advertisement or campaigns during the peak usage time. So
that we may expect to increase the percentage ofadvertisement to reach the target customer group.

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Period No.

JP Time

US TimeUK Time

Tweets

1

02:56

13:56

18:56

52005

2

03:56

14:56

19:56

50171

3

04:56

15:56

20:56

49827

4

05:56

16:56

21:56

49920

5

06:56

17:56

22:56

47913

6

07:56

18:56

23:56

47420

7

08:56

19:56

24:56

45279

8

09:56

20:56

01:56

45619

9

10:56

21:56

02:56

48455

10

11:56

22:56

03:56

47630

11

12:56

23:56

04:56

44069

12

13:56

24:56

05:56

38167

13

14:56

01:56

06:56

33636

14

15:56

02:56

07:56

28296

15

16:10

03:10

08:10

26268 1

16

17:10

04:10

09:10

25745

17

18:10

05:10

10:10

24687

18

19:10

06:10

11:10

26139

19

20:10

07:10

12:10

30437

20

21:10

08:10

13:10

36860

21

22:10

09:10

14:10

43052

22

23:10

10:10

15:10

48343

23

24:10

11:10

16:10

51220

24

01:10

12:10

17:10

52710

Generally, people read the tweet’s text or check usernameof the tweet. But tweet’s data also has many other attributes
that can be very useful for data analysis. In our study, we
created a data frame in our program of R language forallocating all attributes of gathered tweet’s data. In this data
frame, there is an attribute called “country_code” whichshows the country name where the tweet is posted from.
According to this country code data we identified thecountries on the tweets and created the country list of eachtime period. The top 20 countries in period No.1, No.16, andNo.23 are shown in Table 2, 3, and 4.
From this result, we noticed that time is another of thesignificant factor to consider target customer group. The
countries in the list change during the different time period.
According to human habit, day-time and night-time effectonline social network user’s behavior. Country list focusing

1

This is a modified data. The original data is 6567 tweets for 15 minutes. We use
6567x4=26268 tweets for one hour as in other sections.

A Study on Cluster Variation of Twitter Hashtags during a Whole Day

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Table 2. Top 20 countries identified from gathered
tweets in period No.1
No.
Country
Frequency
Percentage

Table 4. Top 20 countries identified from gathered
tweets in period No.23
No.
Country
Frequency
Percentage

1

United states

675

35.5

1

United states

2

United Kingdom

135

7.1

2

United Kingdom

82

4.3

3

Canada36

1.9

3Philippines

32

1.7

4

South Africa

26

1.4

4

South Africa

31

1.6

574

30.1

5

India

21

1.7

5

India

24

1.3

6

Nigeria

15

0.8

6

Canada19

1

7

Malaysia

8

0.4

7

Nigeria

15

0.8

8

Philippines

8

0.4

8

Malaysia

12

0.6

9

Pakistan

7

0.4

9

Ghana

9

0.4

10

Ireland

6

0.3

10

Indonesia

6

0.3

11

Arab Emirates

5

0.3

11

Turkey5

0.3

12Deutschland

5

0.3

12

Singapore

5

0.3

13

Ghana

5

0.3

13

Kenya

5

0.3

14

Indonesia

4

0.2

14

Pakistan

4

0.2

15

Kuwait4

0.2

15

Ireland

4

0.2

16

Germany

4

0.2

16

Germany

3

0.2

17

Brazil3

0.2

17

Saudi Arabia

3

0.2

18

Spain

3

0.2

18

Brazil3

0.2

19Finland

3

0.2

19

Italy

3

0.2

20

Nederland

3

0.2

20

Spain

3

0.2

on Unites States in period No.1 is relatively similar to thatin period No.23. The difference of period No.16 is the time.
In this time period, tweets were gathered while UnitedStates was an early morning time. The highest percentage
from United States is in period No.1. However, if focusing
result on Philippines. The highest percentage is in period
No.23. According to this result, country is also importantfactor for Twitter’s advertisement. Therefore, the time andcountry are the considering factors in the advertisement ormarketing campaigns.

Table 3. Top 20 countries identified from gathered
tweets in period No.16
No.
Country
Frequency
Percentage
1

United states

137

7.2

2

United Kingdom

81

4.3

3

South Africa

25

1.3

4

Nigeria

22

1.1

5

India

18

0.9

6

Australia

17

0.9

7

Philippines

11

0.6

8

Malaysia

9

0.4

9

Arab Emirates

8

0.4

10

Ireland

7

0.3

11

Pakistan

7

0.3

12

Kenya

5

0.3

13

New Zealand

5

0.3

14

Indonesia

4

0.2

15

Belgium

3

0.2

16

Spain

3

0.2

17

Canada3

0.2

18

Thailand

3

0.2

19

Ghana

3

0.2

20

Uganda3

0.2

4.2 Results of extracted hashtags
We gathered enormous Twitter data during 24 hours.
Unfortunately, we have no efficient computer hardware
resource to analyze these data. By this reason, we decided
to choose gathered data from 3 different time periods. The
selected sections were period No.1, No.16 and No.23.
The hashtag list contains top 15 hashtags from eachsection and these lists are shown in Table 5,6, and 7. The
first column of the table is a hashtag in lower casecharacter and the second column is frequent of eachhashtag gathered in each time period.
By comparing these tables, we can confirm the variationof hashtag usage in each time period. In period No.1 wefound hashtag #job listed in our list, but it had gone from listin period No.16 and it appeared again in period No.23. Thispattern can be found for #careerarc and #hiring too. A list inperiod No.16 has a new hashtag which not appeared in other

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Nartlada BHAKDISUPARIT and Iwao FUJINO

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section e.g. #jungkook, #blackpink, #jimin, #love and #jin.
This result shows us how the rapid changes of hashtag top
list in the Twitter’s social network flow. Some hashtag
usages from user decrease in a short period but usage volume
can be increased back into hashtag top list. In another way
some hashtags fade out from the top list and didn’t comeback again like hashtag #nct127 in period No.1.

No.

Table 5. Top 15 hashtags in period No.1
HashtagFrequency

1

#got7

105

2

#lullaby

84

3

#mpn

82

4

#bts

78

5

#selenagomez

66

6#anitters

47

7#nct12746

8#regular

46

9

#job

37

10

#maiareficco

36

11#btsarmy

32

12#hiring32

13#larissamanoela

30

14#careerarc

29

15

#nct

27

HashtagFrequency

1

#bts

67

2

#got7

52

3

#mpn

52

4

#lullaby48

5#btsarmy

33

6#jungkook30

7#anitters

24

8#selenagomez

24

9#blackpink

21

10#jimin

20

11

#love

18

12

#emmys

17

13

#exo

17

14

#maiareficco

16

15

#jin

14

1

#got7

114

2

#bts

93

3

#mpn

87

4

#lullaby84

5#selenagomez

54

6#anitters

52

7#larissamanoela

51

8

#job

46

9

#exo

43

10

#mark

43

11

#hiring4312#careerarc

35

13#btsarmy

35

14#marktuan

29

15#jackonwang

29

4.3 Results of word distribution for hashtags
In this study, we were focusing on hashtag #got7 tocheck the variation of hashtag word list in Twitter trend
during our monitored time. The following Tables 8, 9 and
10 are shown the word frequency for hashtag #got7. Fromthese tables, we found many changes of word position in
the hashtag word list. Differences between word list show
the change of how user used this hashtag in Twitter. We
assume that “lullaby” is a new album from brand nameGot7. In word list of Table 8 and Table 9, we can see theword “album”. But in Table 10, the word “album” has gonefrom the word list. We assume that user used the same
hashtag #got7 but the content in tweets has been changed.

Table 6. Top 15 hashtags in period No.16
No.

No.

Table 7. Top 15 hashtags in period No.23
HashtagFrequency

Table 8. Top 15 words for hashtag #got7 in period
No.1

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No.

Word

Frequency

1lullaby171

2

presentyou

55

3

7official

52

4

hard

45

5

album

41

6

love

37

7

really37

8

finally

36

9

guys

36

10

hope

36

11

itgot7

36

12

work

36

13

check

35

14

itwe

35

15

outgo

35

A Study on Cluster Variation of Twitter Hashtags during a Whole Day

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4.5 Results of clustering hashtags for each time period
Finally, we clustered these hashtags by means of Ward’s
hierarchical clustering method according to theJensen-Shannon divergence matrix of 15 hashtags in eachsection. Lastly, we represented the hierarchical structure of
the associated hashtag group with dendrogram. Here, as forexample, we only showed 3 dendrograms, which were theclustering results of period No.1, No.16 and No.23. In
Fig.2, we had two group hashtag which disappeared in
Fig.3. The first group was a group of hashtags #nct,#nct127 and #regular. The second group was a group ofhashtags #job, #hiring and #careerarc. The difference
between these two hashtag groups was their hashtag life
cycle. From Fig.4 we noticed the second group in the result
again with the same grouping form. But in the first hashtag
group, all the hashtags from the first group faded out fromthe top hashtag list in the same time. This means eachhashtag life cycle associated with other hashtags in thesame group. Short hashtag life cycle was represented by the
first hashtag group. And long hashtag life cycle wasrepresented by the second hashtag group. Hashtag which
had a long hashtag life cycle will keep coming back in thehashtag top list event it fades out from the list sometime. In
the sense, hashtag which had a long-life cycle could be
defined as stable hashtag too. The content or meaning ofuse in these hashtag types is rarely to change.

Table 9. Top 15 words for hashtag #got7 in period
No.16
No.

Word

Frequency

1lullaby73

2

presentyou

40

3

7official

34

4

album

28

5

love

16

6

guys

15

7

hard

15

8

itgot7

14

9

work

14

10

check

13

11

finally

13

12

hope

13

13

itwe

13

14

outgo

13

15

really13

Table 10. Top 15 words for hashtag #got7 in period
No.23
No.

Word

Frequency

1lullaby

107

2

18091865

3preview

57

4

presentyou

56

5

7official

50

6

0904

38

7

mark

37

8

yugyeom

30

9marktuan28

10bambam26

11

review180918

25

12

dance

22

13

pratice

21

14

jacksonwang

20

15

Live9320

5. Conclusions and Future Works

4.4 Result of JSD matrix of Twitter hashtags
As an example, a calculated result of Jensen-Shannon
divergence with a 15x15 matrix is shown in Table 11. This
result was calculated from the word probability distributionof hashtags in period No.1.We can see that JSD result
between same hashtags will always provide 0. From thisfact, we can conclude that as small as the JSD value
between a pair of different hashtags are, they will moresimilar to each other, or in reverse words, as big as the JSD
value between a pair of different hashtags are, they willmore dissimilar to each other.

―
7 ―
− 40
−

In this study, we used the word probability distribution
for understanding the meaning contained in hashtag andthen used Jensen-Shannon divergence for clustering
hashtags. We represented the hierarchical clustering resultswith dendrogram. We implemented the comparing processprocedure to study more deeply about the hashtag life cycle.
With the results of our study, we can understand more
about each customer behavior. By including the related
hashtags into advertisement campaign, we can improve thepercentage of tweets reached to the target customers. So
that Twitter users can access to the advertise campaigneasier. Comparing with Ref.5, our study presented a simpler
algorithm for clustering hashtags on Twitter. Comparing with
Ref.6, we used Jensen-Shannon Divergence as a measure tocluster hashtag, which reflects precisely the dissimilarity
between two probability distributions.
For future work, we will continue our study on hashtag
life cycle in a relatively long time period. For thisobjective, we must continually gather long-term, e.g. manydays ar many months, tweets from Twitter. From gathereddata we expect to discover the factor that effect change inhashtag life cycle and the patterns of hashtags.

Nartlada BHAKDISUPARIT and Iwao FUJINO

ヘッダー

Available: https://www.r-project.org/about.html

References[9] Pablo Barbera. streamR: Access to Twitter Streaming API[1] IBM website, Big data analytics. (2018) [Online].

via R. [Online]. Available:

Available:

https://cran.r-project.org/web/packages/streamR/index.ht

https://www.ibm.com/analytics/hadoop/big-data-analytics

ml. Accessed on: Sep. 24, 2018.

[2] Statista website, Number of monthly active Twitter users[10] RDocumentation website, stringr. (2018) [Online].

worldwide from 1st quarter 2010 to 2nd quarter 2018 (inmilions). (2018) [Online].

Available:

Available:

https://www.rdocumentation.org/packages/stringr/versions

https://www.statista.com/statistics/282087/number-of-mon/1.1.0thly-active-twitter-users[11] RDocument website, TermDocumentMatrix, (2018)[3] The Guardian website, Five social media charity[Online]. Available:

campaigns you need to know about. (2018) [Online].

https://www.rdocumentation.org/packages/tm/versions/0.7

Available:
https://www.theguardian.com/voluntary-sector-network/20
14/apr/03/five-social-media-charity-campaigns.

-5/topics/TermDocumentMatrix[12] RDocument website, tm, (2018) [Online]. Available:
https://www.rdocumentation.org/packages/tm/versions/0.7[4] Nartlada Bhakdisuparit and Iwao Fujino, “Understanding-5

and clustering hashtags according to their word

[13] RDocument website, base, (2018) [Online]. Available:

distributions,” in IEEE 5th International Conference on

https://www.rdocumentation.org/packages/base/versions/3

Business and Industrial Research, 2018.

.5.1/topics/grep[5] Yan Mei, Weiliang Zhao and Jian Yang, “Maximizing the[14] Hiroya Takamura, Introduction to Machine Learning for

Effectiveness of Advertising Campaigns on Twitter,” inNatural Language Processing (In Japanese), Corona

IEEE 6th International Congress on Bigdata, 2017.

Publishing Co.,LTD, Tokyo, Jpn, 2015.

[6] Lotfi A. Zadeh, Ali M. Abbasov and Shahnaz N.

[15] RDocument website, textmineR, (2018) [Online],

Shahbazova, “Analysis of Twitter Hashtags: Fuzzy

Available:

Clustering Approach,” in North America Fuzzy

https://www.rdocumentation.org/packages/textmineR/versi

Information Processing Society, 2015.

ons/2.1.3/topics/CalcJSDivergence[7] Orianna Demasi, Douglas Mason and Jeff Ma,[16] RDocument website, hclust, (2018) [Online], Available:“Understanding Communities via Hashtag Engagement: A

https://www.rdocumentation.org/packages/stats/versions/3

clustering Based Approach” in Proc. ICESM,2016.

.5.1/topics/hclust[8] R Project website, What is R?, (2018) [Online].

―
8 ―
− 41
−

#lullaby

0.136

0.000

0.546

0.475

0.560

0.564

0.557

0.566

0.506

0.564

0.540

0.518

0.567

0.528

0.548

#got7

0.000

0.136

0.535

0.475

0.556

0.560

0.553

0.563

0.504

0.560

0.538

0.516

0.562

0.526

0.542

#got7

#lullaby

#mpn

#bts

#selenagomez#anitters#nct127#regular#job# ma i a r e f i c c o#btsarmy#hiring#larissamanoela#careerarc#nctProc. Schl. ITE Tokai Univ.
Vol. xx,No.xx ,20xx,pp.xxx -xxx

− 42 −

0.565

0.552

0.338

0.547

0.429

0.242

0.536

0.580

0.575

0.246

0.201

0.445

0.000

0.546

0.535

#mpn

0.515

0.514

0.529

0.509

0.284

0.528

0.493

0.539

0.529

0.528

0.521

0.000

0.445

0.475

0.475

#bts

0.577

0.573

0.269

0.568

0.531

0.152

0.557

0.590

0.584

0.154

0.000

0.521

0.201

0.560

0.582

0.577

0.224

0.571

0.539

0.211

0.561

0.594

0.590

0.000

0.154

0.528

0.246

0.564

0.560

rs

omez

0.556

#anitte#selenag

― 9 ―

0.132

0.577

0.593

0.571

0.578

0.590

0.561

0.093

0.000

0.590

0.584

0.529

0.575

0.557

0.553

#nct127

0.155

0.580

0.596

0.575

0.583

0.593

0.564

0.000

0.093

0.594

0.590

0.539

0.580

0.566

0.563

ar

#regul

0.553

0.152

0.563

0.141

0.529

0.560

0.000

0.564

0.561

0.561

0.557

0.493

0.536

0.506

0.504

#job

0.582

0.576

0.315

0.571

0.541

0.000

0.560

0.593

0.590

0.211

0.152

0.528

0.242

0.564

0.560

eficco# ma i a r

Table 11. Result of calculated JSD matrix of hashtags in period No.1

0 .5 6 4

0 .5 4 5

0 .5 3 8

0 .5 4 1

0 .0 0 0

0 .5 4 1

0 .5 2 9

0 .5 8 3

0 .5 7 8

0 .5 3 9

0 .5 3 1

0 .2 8 4

0 .4 2 9

0 .5 4 0

0 .5 3 8

my#btsar

0.564

0.099

0.574

0.000

0.541

0.571

0.141

0.575

0.571

0.571

0.568

0.509

0.547

0.518

0.516

#hiring

0.585

0.579

0.000

0.574

0.538

0.315

0.563

0.596

0.593

0.224

0.269

0.529

0.338

0.567

0.562

manoela#larissa

0.569

0.000

0.579

0.099

0.545

0.576

0.152

0.580

0.577

0.577

0.573

0.514

0.552

0.528

0.526

rarc

#caree

0.000

0.569

0.585

0.564

0.564

0.582

0.553

0.155

0.132

0.582

0.577

0.515

0.565

0.548

0.542

#nct

A Study on Cluster Variation of Twitter Hashtags during a Whole Day

Nartlada BHAKDISUPARIT and Iwao FUJINO

Proc. Schl. ITE Tokai Univ.
Vol. xx,No.xx ,20xx,pp.xxx -xxx

Fig.1 Time chart of Tweets gathered during one day

Fig.2 Cluster dendrogram of top 15 hashtags in period No.1

―
―
− 10
43 −

A Study on Cluster Variation of Twitter Hashtags during a Whole Day

ヘッダー

Fig.3 Cluster dendrogram of top 15 hashtags in period No.16

Fig.4 Cluster dendrogram of top 15 hashtags in period No.23

―
―
− 11
44 −