Detection of Missing Tweets based on Browsing Interval
and Topic Granularity
Hiromitu Ohara

Yu Suzuki

Akiyo Nadamoto

Konan University
Okamoto 8–9–1
Higashinada–ku Kobe, Japan

Nara Institute of Science andTechnology
891605 Takayama-cho, Ikoma,
Nara, 630-0192, Japan
m1424003@center.konan-u.ac.jpysuzuki@is.naist.jp

ABSTRACTTwitter users who browse tweets can follow other users inwhom they are interested. They can obtain interesting information from other users’ tweets on their timeline. If they
follow many users, then they can expect numerous tweets on
their timeline. However, if users do not browse their timeline
for some time, they can lose interesting and important information. Therefore, a system that automatically presents
a summary of lost information can be extremely beneficial.
As described herein, we propose a method of extracting lost
information automatically based on a user’s browsing timeinterval and the topic structure of a followee’s tweets. First,
we classify a followee’s tweets that contain the user’s missing information, and assign topics to the groups. Next, we
generate a topic graph based on the semantic structure from
Wikipedia. We decide whether the tweet groups are missedusing the followee’s topic graph based on the browsing timeinterval. Finally, we extract missing information and present
it to the user.

Categories and Subject Descriptors
H.4.m [Information Systems]: Miscellaneous; I.7.m [Document and Text Processing]: Miscellaneous

Keywords
Microblog, Twitter, Topic Granularity

1. INTRODUCTION
Microblogs have become extremely popular: users caneasily post and obtain various information from the internet.
Twitter, an extremely popular microblog, accumulates anddistributes vast amounts of information. A Twitter user who
browses tweets can follow other users, called followees, inwhom the user is interested. The user can then receive interesting information from followees’ tweets on the user’s own
timeline. When a user follows many followees, the user can
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iiWAS ’15, December 11-13, 2015, Brussels, Belgium
c 2015 ACM. ISBN 978-1-4503-3491-4/15/12. . . $15.00⃝
DOI: http://dx.doi.org/10.1145/2837185.2837233

Konan University
Okamoto 8–9–1
Higashinada–ku Kobe, Japannadamoto@konanu.ac.jp

browse numerous and diverse tweets on the user’s own timeline. However, a user who cannot browse for some periodof time loses interesting and important information. Uninteresting information and known data are also included in
the great numbers of missing tweets. Therefore, large numbers of missing tweets can be expected to include important,unimportant, known topic, and unknown topic information.
To obtain the interesting and important information that islost, but included in the large amount of missing tweets, auser must search for the tweets that were posted while theuser was unable to browse. Then a user must read and comprehend the contents of the missing tweets. That is diﬃcult
work for users. For such cases, we consider a system thatcan be beneficial for users: it browses a summary of lost
information automatically. We propose a method to extract
this lost information, which is important but unknown information. It browses a summary of these numerous tweets
automatically. As described in this paper, we designate thetweets which a user has lost and important information as“missing information,” the target user for whom we extract
missing information as a “browsing user,” and the person
whom the browsing user follows as a “followee.”
As described in this paper, as a first step of presenting
missing information of tweets, we propose a method to extract missing information and to present contents based on
types of missing information. The user interface used forpresenting the missing information is left as a subject for
future work.
This paper is organized as follows: Section 2 discusses
related work; Section 3 presents definition of browsing interval and topic granularity; Section 4 presents a method
to extract missing information based on topic granularity
and time interval; Section 5 discusses experiment results.
Section 6 presents conclusions of our study.

2.

RELATED WORKS

Topic detection on Twitter has been studied by many researchers. Mathioudakis et al.[12] propose a system detecting emerging topics on twitter by detecting bursty keywords
in tweets. Becker et al.[1] propose a method for identifying
tweets about real world events and detecting trending topics. Lee et al. [9] try to detect trending topics on twitterby using text-based classification and network-based classification. These method attend emerging topics, but our
method target not only emerging topics but also normal
topics like authors hobby,job and experience in daily life.
Hong et al. [6] propose topic modeling in Twitter using

LDA(Latent dirichlet allocation[3]) and the Author-Topic
model. Michelson et al. [13] try to detect Topics using author information of tweets with categories in Wikipedia. Xu
et al.[19] try to extract topics in tweet with article and link
histories in Wikipedia. Kasiviswanathan et al. [8] detect
topics from Twitter using the dictionary learning method.
Bernstein et al.[2] try to extract relevant topics of tweets
based on number of search engines result. However, thesestudies do not examine topic granularity and time information. Cataldi et al.[4] propose a topic detection method withrelation of topics by author information of tweets and the
topic life cycle. They specifically examine time information
for all of Twitter, but we particularly examine time information only for the viewer timeline to facilitate extractionof more specific information.
In our method, we extract topic granularity by clustering tweet and detecting topics for each clusters. Sriam etal. [17] propose classify tweets into categories such as News,
Events, Opinions, and so on. They use author informationand features, but their method is not address tweets topic.
Rosa et al. [15] verify supervised clustering methods andunsupervised clustering methods. Their results show thatis diﬃcult to cluster information accurately using unsupervised methods such as standard LDA or K-means clustering[10] because tweets are short and Twitter contain to many
noise. We use Repeated-Bisection clustering. It is an unsupervised method, but it is regarded as an eﬀective method
for short texts.
In recent years, many studies specifically examine time
information related to Twitter. Ren et al.[14] propose a topic
model for summarization of personalized useful information
related to Twitter based on the association between users
and time-aware summarization. Sasaki et al.[16] emphasizes
the study of variation in topic trends by time, proposing a
topic model improved on Twitter-LDA[20]. Magdy et al.[11]present a searching relevant summarized information fromtwitter by posted time and events in real world. However,these methods do not address topic granularity.

3. DEFINITION OF BROWSING INTERVALAND TOPIC GRANULARITY
3.1 Definition of browsing interval
We consider that there are two types of missing information that are partially-known information and complementaryunknown information. We call the former “known topic” thelatter “unknown topic.” The definition of “known topics”
and “unknown topics” as follows.
• Known topic
A known topic is a topic of tweets. Some tweets areposted during a browsing user browsing time; sometweets are posted during a user non-browsing time.
Then, a browsing user knows part of the topic.
• Unknown topic
An unknown topic is a topic of tweets posted only
during the user non-browsing period. A browsing user
does not know information at all.
Figure 1 presents an image showing the known topic andunknown topic. In Figure 1, the followee tweets about partof “baseball” and “research” during a browsing user’s browsing time and the browsing user know part of “baseball”

Figure 1: Image of the known topic and unknown
topic.
and “research” topics. They become known topics. On
the other hands, the followee tweets about “football” during a browsing user’s un-browsing time, the browsing user
does not know about information of “football” which is followee tweeted at all. The topic of “football” becomes unknown topic. We consider, a browsing user can understandroughly known topic information that are tweeted his/her
un-browsing time, because he/she already browsed the same
topic of tweets posted during the user browsing time. In thiscase, we consider it is good for the browsing user to present
outline of the topics, then we present the topic structure of
the missing information to him/her. On the other hands,
for an unknown topic, it is diﬃcult for a browsing user tounderstand all contents of topic clearly because the tweets
were tweeted during a time when the browsing user cannot
browse. We consider that it is necessary to present information that has greater detail that a user can discover the fullbreadth of the topic than in the case of a known topic.

3.2

Definition of topic granularity

It is important to consider topic granularity because missing information that should be presented for browsing users
is based on browsed topic details. “Ichiro” and “Masahiro
Tanaka” are Japanese major league baseball players. Tweets
related to “Ichiro” and tweets related to “Masahiro Tanaka”
are classified as “Japanese Major Leaguers,” including also
“Major Leaguers” such as “Jose Altuve” and “John Donaldson.” When classifying these tweets according to greater
granularity such as “baseball,” the case includes tweets about
topics such as “baseball team,” “baseball stadium,” and soon. However, we can classify these tweets according to finer
granularity such as “Japanese major leaguer,” but the caseincludes only “Ichiro” and “Masahiro Tanaka.” For example,
the browsing user did not browse all the followee’s tweets related to “Ichiro” and “Masahiro Tanaka,” but the browsing
user browses some tweets about “John Donaldson.” In thiscase, when we classify the followee’s tweets to “Japanese Major Leaguers,” it becomes an unknown topic. When we classify them to “Major Leaguers” or “Baseball,” they becomes
known topics. Because the topic diﬀers from granularity of
the classification (see Fig. 2), we must consider the granularity of the topic. When extracting missing information
automatically, it is important to consider topic granularity.

4.

EXTRACTING MISSING INFORMATION

Figure 2: Granularity of topics.

We consider both the user’s browsing time and topic granularity to extract missing information. As described herein,
we propose a method to extract this missing information automatically based on the browsing time interval and topic
granularity. First, we calculate the topic granularity. Next
we divide nodes of a topic graph based on the browsing timeinterval. Then we extract missing information of two types
and browse it.
The following and Fig. 3 present details for the flow of
extracting missing information.
1. Considering topic granularity
1–1 Extracting tweets that the followee tweeted during the time when the browsing user did not browse
and before and after that time
1–2 Clustering tweets and extract topics in each cluster
1–3 Creating the topic graph using a semantic structure in Wikipedia
2. Divide the topics in the topic graph based on the browsing time interval
2–1 Extracting the tweet time for each tweet in the
topic graph
2–2 Dividing the topic into known and unknown topics based on the browsing time interval
3. Extracting missing information and dividing it into
known topics and unknown topics based on topic graphs
4. Presenting missing information in each type

4.1 Topic granularity
4.1.1 Clustering tweets
We extract tweet topics using clustering tools. When wecluster the tweets, we use feature words of the tweets.
Feature words
To classify and detect the topic from tweets, we use featurewords, which are proper nouns, general nouns, and unknown
words. The reason for using unknown words is to adapt
new words and abbreviated words that are used on Twitter.
Candidates of feature words have more general words such

Figure 3: System flow.

as “game” and “player,” which are not specific to the topic
of kinds of sports. They also have newly coined words thatare unique to a community. We delete newly coined words,
more general words, and those from candidates of featurewords. More general words are used very often on the internet. Newly coined words are not used outside the community. We consider more general words as those which areoften used in Wikipedia articles. In contrast, newly coined
words are not used in Wikipedia articles. We regard moregeneral words as those used in more than 100 Wikipedia
articles. Newly coined words are those which are not usedin any Wikipedia article. Consequently, feature words areproper nouns, general nouns, and unknown words used in
1–100 Wikipedia articles. The vector of feature words is thevalue of IDF when a tweet is regarded as one document.
Clustering
When classifying tweets using feature words to extract tweets
topics, we use Repeated Bisection[18], which is a method

used for bayon1 and CLUTO[7]; it is a kind of K-means
method. Repeated Bisection is suitable for short sentences[5].
Topics of tweets diﬀer from diversity and granularity. Actually, it is diﬃcult to ascertain the number of clusters. We use
the number of clusters automatically using a threshold: thedividing point of bayon. Furthermore, Repeated Bisectionis hard clustering; some garbage clusters exist, which aresparse clusters. We delete such garbage clusters, which are
under the threshold of similarity between the center vectorof a cluster and the vector of tweets. After deleting garbage
clusters, we designate the remaining clusters as “topic clusters” for these analyses.

4.1.2 Creating Topic graph
After dividing the tweets into topic clusters based on the
topics, we transform topic clusters into the topic graph using
the semantic structure. As described in this paper, we use
the category structure of Wikipedia as a semantic structure
because the category structure of Wikipedia includes newwords of many kinds. Tweets also use new words of manykinds.
(a) Creating the smallest topic graphFirst, we transform a topic in a topic cluster into a smallest
graph, which consists of a topic in a cluster and its highlevel semantics. If a topic cluster has multiple topics, wetransform a topic cluster into a multiple topic graph. As
described in this paper, we call this topic graph “the smallest topic graphST Gj .” j is a number of a topic. A leaf nodeof all ST Gj is the topicC = {c1 , c2 , . . . , cn } (show Fig. 4).
ST Gj is a labeled graph. The label of the leaf node is a
topic of the topic cluster. Non-leaf nodes in ST Gj are highlevel semantics of a leaf nodecx . We regard the semantics
of cx as a category of Wikipedia, which means that a labelof non-leaf nodes in ST Gj are semantics of cx . When we
extract high-level semantics of a cx from Wikipedia, we use
the Wikipedia category link information database2 . We regard category sxi as a high-level semantic of cx . In addition,i is a number of category of cx . We also extract high-level
semantic sxim of sxi from the Wikipedia category link information database. We create n − th high-level semantics tocreate the smallest topic graph. As described in this paper,n is 2.
In this time, the category words which contain “Wiki,”“stub” and “user” are deleted from the category, because
these words are Wikipedia’s system words. These are inappropriate for using high level semantics. We also delete“Living people” and “XX-language surnames,” which haveno important meaning. Then, the leaf node of ST Gj is a
topic of topic cluster. Non-leaf nodes of ST Gj are highlevel semantics of a leaf node (topic). For example, there
are two topics of “Ichiro” and “Masahiro Tanaka.” First,
we create the smallest topic graph about “Ichiro” as c1 . We
search for articles about “Ichiro” from Wikipedia. We extract categories, which are “American League stolen basechampions” as s11 , and “Japanese Major Leaguers” as s12 ,as high-level semantics from the Wikipedia category link
information database. We also extract two-hop high-level
semantics of topic from the Wikipedia category link information database. Then we create the smallest topic graphrelated to “Ichiro” (c1 ). Next, we search each article about“Masahiro Tanaka” (c2 ) from Wikipedia. We also extract
1
2

https://code.google.com/p/bayon
http://dumps.wikimedia.org/jawiki

Figure 4: Smallest topic graphcategories which are “Japanese Major Leaguers” (s21 ) and“Olympic baseball players of Japan” (s22 ) as high-level semantics. Then we extract two-hop high-level semantics of
topics. Finally, we create the smallest topic graph related
to “Masahiro Tanaka.”
The number of a smallest topic graph is a total numberof topics of all topic clusters. After we create all smallest
topic graphs, we create topic graphs based on joining at thesame nodes.
(b) Creating a topic graph
Next, when the same label exists in ST Gj and ST Gk , wejoin ST Gj and ST Gk to create the topic graph. There are
two types of joining: mutual joining of non-leaf-nodes (highlevel semantic) (Fig. 5(a)), and joining between a leaf-node(topics) and a non-leaf-node (Fig. 5(b)). For example, inFig. 5(a), c1 is “Ichiro” and S12 is “Japanese Major Leaguers,” c2 is “Masahiro Tanaka” and S21 is also “Japanese
Major Leaguers.” The labels of S12 and S21 are the same.
Then we join S12 and S21 . In Fig. 5(b), c1 is “Japanese
Major Leaguers,” and c2 is “Major Leaguers.” Also, S12 isa high-level semantic of c1 : “Major Leaguers.” In this case,the labels of c2 and S12 are the same label. Consequently,
we join two nodes.
In this way, we join the nodes of all smallest topic graphs
which have the same label to other smallest topic graphs,
and create a topic graph. In the topic graph, we designate
a node for which the label is a topic as a “topic node” anda node for which the label is only high-level semantic as a“semantic node.” A topic node includes tweets for which
the topic is the label of the topic node. A semantic node
does not include tweets, but only a label of the node (highlevel semantic). When we join a topic node and a semantic
node in the smallest topic graph, the node becomes a topic
node. All leaf nodes of a topic graph are topic nodes. The
topic graph presents the topic granularity: a topic that isa greater distance from root node is a more detailed topic
than another topic which is a shorter distance from the root
node. Consequently, leaf nodes are the most detailed topics
in the topic graph.

4.2

Determining Type of Topic based on timeinterval

We determine topic nodes in a topic graph as known topics
or unknown topics. First, we check the time stamp of tweets
in the topic nodes. If a time stamp of a tweet is the browsingtime of a browsing user, then the tweet becomes a known
tweet. However, if a time stamp of tweet is non-browsingtime of a browsing user, then the tweet becomes an unknown
tweet. After we extract each time stamp of a tweet, wenext determine a type of topic node. When at least one
known tweet includes a topic node, the topic node becomesare semantic nodes. They do not include tweets. Gray circles are browsing areas that include only topic nodes. Gray
rectangle areas are also browsing areas, but they consist of
topic nodes and semantic nodes.
• Un-joined smallest topic graphThis case of a smallest topic graph is not joined withother smallest topic graphs. In this case, topics (nodes)
are mutually isolated from other topics.
(a) Known topic (Fig. 7(a))
The browsing user knows a part of tweets in the
topic node, which means the user knows an overview
of the structure of the smallest topic graph. We
present only topics of the leaf node.

Figure 5: Type of joining in smallest topic graph(b) Unknown topic (Fig. 7(b))
The browsing user does not know all tweets in the
topic node. Therefore, the browsing user does notknow the structure of the smallest topic graph.
We present details of the topics. Then we present
all topic structures of the topic graph and all
tweets in the topic node.
• Joined smallest topic graphIn this case, there are two cases which are joined between semantic nodes and joined between topic nodeand semantic node (see Fig. 5).
– Between semantic nodeIn this case, the topic nodes diﬀer, but they havethe same semantic nodes.

Figure 6: Example of topic types in the topic grapha known topic. When all tweets in a topic node are unknown
tweets, the topic node becomes an unknown topic. Figure
6 presents an example of a type of topic node in the topic
graph. In Fig. 6, the topic cluster “Ichiro” includes bothbrowsed tweets and non-browsed tweets. Therefore “Ichiro”
is a known topic. In contrast, “Masahiro Tanaka” includes
only browsed tweets. Therefore “Masahiro Tanaka” is an
unknown topic.
We determine all topic nodes for topics of two types.

4.3 Presenting missing tweets based on granularity and time interval
After creating the topic graph and determine the typeof topic node, we ascertain what contents are present for abrowsing user based on the type of topic graph.
Our content-presenting semantic is that it is good for thebrowsing user to present an outline of the topics in the caseof a known topic. Then we present a structure of a topic
graph of the missing information to the user. For an unknown topic, it is diﬃcult for a browsing user to understand all contents of a topic clearly because the tweets were
tweeted during a time when the browsing user is unable tobrowse. We present details of a topic graph. The following and Fig. 7 present the type of topic graph. In Fig.
7, black nodes, both circles and triangles, are topic nodes
that include tweets for which the topic is the label of thenode. Nodes of black circles are known topics. Nodes of
black triangles are unknown topics. Nodes of white circles(c) All known topics (Fig. 7(c))
This case is that for which all topic nodes are
known topics. This case is almost identical tothat of type (a), but we regard the common
high-level semantic as important. We present
a label of all topic nodes and the label of common semantic nodes.
(d) All unknown topics (Fig. 7(d))
This case is that for which all topic nodes
are unknown topics. The browsing user does
not know the content of the topic graph atall. This case is the same as type (B). We
present all topic structures of the topic graphand all tweets in all topic nodes.
(e) Known topic and unknown topic (Fig. 7(e))
Basic concepts of this case are mixed types(a) and (b). The browsing user knows part ofthe structure of the topic graph. We present
labels of known topics along with tweets inunknown topics and labels of semantic nodes
which are parent nodes of unknown topics.
– Between topic node and semantic node(f) Known topic & Known topic (Fig. 7(f))
In this case, the parent node of known topic isalso a known topic. The browsing user knows
the topic nodes. The user knows an overview
of the structure of topic graph. We present
labels of topic nodes and parent topic nodes.
(g) Known topic & Unknown topic (Fig. 7(g))
In this case, the parent node of a known topic
is an unknown topic. The browsing user knows
the topic node, but the user does not know

Figure 7: Topic graph types
the parent of the topic node. We consider
that the browsing user does not know the
topic graph structure well. Therefore, we present
a label of the topic node along with labels andtweets of parent topic nodes.
(h) Unknown topic & Known topic (Fig. 7(h))
In this case, the known topic is the parent
node of the unknown topic. We regard thebrowsing user as knowing almost the entire
structure of the topic graph. Then we present
the tweets and label of the topic node, which
is an unknown topic, and the label of the parent topic node, which is a known topic.
(i) Unknown topic & Unknown topic (Fig. 7(i))
In this case, we expect that the browsing user
does not know the overall structure of the
topic graph. We present all tweets of the unknown topics and all labels of all nodes of the
topic graph.

• Data sets
– Number of users: 5– Number of tweets: 1000 tweets/user total is 5000
tweets.
– Feature of users:
User A: interest topics: “anime,” “game”
He tweets many short tweets and nonsensical tweets.
User B: interest topics: “anime,” “idol”
He tweets specific idol names and idol groups.
User C: interest topics: “soccer”
He tweets long tweets related to soccer games andplayers.
User D: interest topics: “soccer,” “baseball,” “game”
He always tweets daily things.
User E: interest topics: “music,” “radio”
Many of his tweets include local names and building names.
– Correct answer:
We extract correct answers manually.

5. EXPERIMENT
We conducted experiments of two types to obtain the benefits of our proposed method.

5.1 Experiment 1: Clustering Method
We conducted an experiment to obtain whether repeatedbisection is suitable for clustering of the tweets of followees.

5.1.1 Condition
Conditions of experiment 1 are the following:• Parameter:
divided point of bayon is 1.0
threshold of cosine similarity is 0.5.

5.1.2

Results and Discussion

Table 1 shows results of clustering. We regard the correct
data is by hands and we calculate precision. The precision
is greater than 0.7 in all users in the Table 1,the results are
good. Especially, results of User B, User D and User E arehigh precision.The reason is that these users tweeted witha lot of proper nouns such as human name, place name,and music title, then there are many feature words which
are used for clustering in their tweet. It is easy to cluster
these tweets by using the feature words. However, resultsof User A and User C are not high precision. The reason isthat their tweets are too short and there are many slang andUser A
User B
User C
User D
User E
Average

Table 1: Results of clustering
Number of tweets
Number of Number oftweetsclusterin the largest cluster
325
97
14
353
47
23
583
171
7
487
124
14
469
121
12
443.4
112
14

Precision
0.708
0.912
0.762
0.891
0.938
0.842

Table 2: Example of Topics in each Cluster
User name

User A

User B

User C

User D

User E

Cluster ID
61
36
65
53
41
51
39
27
13
74
10
14
17
19
21
32
15
93
151
113
148
123
129
93
14

Topic 1
Speedcorefamilymart
comicmarket?? (doubke question marks)
Ramen
ISUCA
Keyhole Turtleneckanisama
Otome
ISUCA
excitement
bundesliga
Osakagekisakaconcern
TigersDale
Koshien
Gomez
Part time job
MINAMI WHEEL
NigataFukuoka
NagoyaTweet

abbreviation words in their tweets, it is diﬃcult to extract
feature words to clustering.
Table 2 presents results of clustering of the top five clusters based on respective users. In this table, topics of user C
are not the same as his interest topics; the topics are almost
entirely general words such as “excitement” and “concern.”
The reason is the problem of dictionary of Japanese language morphological analysis. His tweets are not good forJapanese language morphological analysis. We cannot extract feature words. Then the tweets are clustered by generalwords. From these results, our method of extracting featurewords and clustering methods is good for the clustering thefollowees’ tweets.

5.2 Experiment 2: Creating topic graphs
We conducted an experiment to ascertain whether creating the smallest topic graphs is beneficial or not.

5.2.1 Conditions
The conditions of experiment 2 are the following:• data: 2335 tweets that contain obvious topics clustering by experiment1s method, the number of cluster is
152 topic clusters.
• The number of topic graph : 299.
• Subjects: Nine men and women in their 20s.
The flow of experiment is

Feature value
0.999
0.978
0.963
0.962
0.962
0.986
0.986
0.985
0.969
0.959
0.876
0.856
0.839
0.836
0.831
0.974
0.971
0.971
0.970
0.969
0.980
0.962
0.962
0.959
0.958

Topic 2t+pazolite
chills caught in sleep
Take it easy
Tear
Ramen Jiro
KohimeElithabeth
Audience Camera
ISUCA
ISUCA tremendous
eroticismrole
somebody else problem
Zenit
new world
Central League
TsumTsum
Shimabukuro
RBI
Breaktime
Carbonara
Doshitefmfukuokafm
Beautiful Sky
I Love You

Feature value
0.023
0.211
0.103
0.157
0.160
0.121
0.067
0.087
0.201
0.186
0.340
0.333
0.164
0.176
0.287
0.175
0.239
0.0962
0.136
0.068
0.099
0.084
0.136
0.113
0.285

1. We extract the top two topics from each cluster. Thenwe extract categories of topics from Wikipedia.
2. We create the smallest topic graph of each clusters.
3. We join the same node of smallest topic graphs andcreate topic graphs. In this experiment, we joined only
two clusters in each topic graph.
4. Subjects show tweets in the topic graph, and judge thejoined node is good or not as three choices (0, bad; 1,
middle; 2, good).

5.2.2

Results and Discussion

Table 3 shows the experiment results: we select three(Jleague, Anime, and Macroeconomics) are good results; theother three(Help, Society, and Registration of mark) arebad. Two points of diﬀerence exist between good results
and bad results. First, if the cluster topic is specific, thejoining is successful. For example, each cluster of J-league
club(which is Japanese professional football league name),
Anime, Macroeconomics have specific topics. However, theclusters of help, society, registration of marks have wide topics. For them, clustering was not successful. Second, highlevel semantic in the bad case have a large number of subordinate semantics, which is over 100. The topic graph is not
successful if the high-level semantic has wide semantics. In
the near future, we must consider such cases.

Table 3: Example of results of experiment2
High-level semantic
J league club
Anime
Macroeconomics
Help
Society
Registration of markNumber of subordinate semantic
54
95
79
107
149
414

Topic in cluster A
Bellmare,Shonan BMW stadium
TV anime, NeversayNever(Music title)
primary balance, second reportfootball player, baseball player
tennis, condition
Softbank, rute 1

6. CONCLUSION AND FUTURE WORKAs described in this paper, we propose a method to extract missing information that a user missed: it is importantand unknown information from the user’s timeline on Twitter. The method also browses a summary of these numerous
tweets automatically. First, we cluster the followee’s tweets
to find the user’s missing information. Then we extract their
topics. Next we create a topic graph based on the semantic structure from Wikipedia. We determine the topic typefrom followee’s topic graph based on the browsing time interval. Then we extract missing information and present the
information to the users. We conducted experiments of two
types to measure the feasibility of clustering for followee’s
tweet and to assess the availability of topic graphs. These results demonstrate that our method of extracting topics andclustering methods is good for clustering of the followees’
tweets. Results show that our topic graph is almost feasible.
However, for cases in which the topics are not specific, ourtopic graph is unsuitable. In the near future, we expect toconduct experiments to improve content presentation based
on topic granularity. We must also develop a user interface
for our systems and conduct user experiments.
In future work, first, we expect to improve the method of
extracting future words and high-level semantics based on
results of the experiments described herein. Next, we plan tocreate a user interface and experiment to demonstrate that
our method and user interface are appropriate for browsing
missing information.

7. ACKNOWLEDGMENTS
This work was partially supported by Research Instituteof Konan University, and by JSPS KAKENHI Grant Number 26330347.

8. REFERENCES[1] H. Becker, M. Naaman, and L. Gravano. Beyond
trending topics: Real-world event identification on
twitter. Proceedings of the Fifth International AAAI
Conference on Weblogs and Social Media (ICWSM’11), pages 2695–2697, 2011.
[2] M. S. Bernstein, B. Suh, L. Hong, J. Chen, S. Kairam,and E. H. Chi. Eddi: Interactive topic-based browsingof social status streams. In Proceedings of the 23Nd
Annual ACM Symposium on User Interface Software
and Technology, UIST ’10, pages 303–312, New York,NY, USA, 2010. ACM.
[3] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent
dirichlet allocation. J. Mach. Learn. Res., 3:993–1022,
Mar. 2003.
[4] M. Cataldi, L. Di Caro, and C. Schifanella. Emerging
topic detection on twitter based on temporal andsocial terms evaluation. pages 4:1–4:10, 2010.

Topic of cluster B
Urawa Reds, urawareds
Seiyu-Paradise(Music title), NeversayNever(Music title)
macro economy, report of municipal government
Mamers(product name), scheduleenergy, Yamaga-member(team name)Cancam(Magazine name), energy policy

Average of value
1.89
1.78
1.56
0.00
0.00
0.00

[5] S. Hanai and A. Nadamoto. Clustering for similar
recipes by using cooking ingredient. IEICE technicalreport, 114(204):47–52, 2014.
[6] L. Hong and B. D. Davison. Empirical study of topic
modeling in twitter. Proceedings of the First Workshop
on Social Media Analytics (SOMA)., pages 80–88,
July 2010.
[7] G. Karypis. Cluto - a clustering toolkit. Dept. ofComputer Science, May 2002.
[8] S. P. Kasiviswanathan, P. Melville, A. Banerjee, and
V. Sindhwani. Emerging topic detection using
dictionary learning. pages 745–754, 2011.
[9] K. Lee, D. Palsetia, R. Narayanan, M. M. A. Patwary,
A. Agrawal, and A. Choudhary. Twitter trending topic
classification. Proceedings of the 2011 IEEE 11th
International Conference on Data Mining Workshops,
pages 251–258, December 2011.
[10] J. MacQueen. Some methods for classification andanalysis of multivariate observations. 5th Berkeley
Synap. Math. Statist, 1:281–297, 1967.
[11] W. Magdy, A. Ali, and K. Darwish. A summarization
tool for time-sensitive social media. In Proceedings ofthe 21st ACM International Conference on
Information and Knowledge Management, CIKM ’12,
pages 2695–2697, New York, NY, USA, 2012. ACM.
[12] M. Mathioudakis and N. Koudas. Twittermonitor:
Trend detection over the twitter stream. In
Proceedings of the 2010 ACM SIGMOD International
Conference on Management of Data, SIGMOD ’10,
pages 1155–1158, New York, NY, USA, 2010. ACM.
[13] M. Michelson and S. A. Macskassy. Discovering users’
topics of interest on twitter: A first look. In
Proceedings of the Fourth Workshop on Analytics for
Noisy Unstructured Text Data, AND ’10, pages 73–80,
New York, NY, USA, 2010. ACM.
[14] Z. Ren, S. Liang, E. Meij, and M. de Rijke.
Personalized time-aware tweets summarization.
Proceedings of the 36th international ACM SIGIR
conference on Research and development in
information retrieval., pages 513–522, 2013.
[15] K. D. Rosa, R. Shah, B. Lin, A. Gershman, and
R. Frederking. Topical clustering of tweets. The 3rdWorkshop on Social Web Search and Mining(SWSM)., 2011.
[16] K. Sasaki, T. Yoshikawa, and T. Furuhashi. Online
topic model for twitter considering dynamics of userinterests and topic trends. Proceedings of the 2014
Conference on Empirical Methods in Natural
Language Processing (EMNLP)., pages 1977–1985,
October 2014.
[17] B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu,

and M. Demirbas. Short text classification in twitterto improve information filtering. pages 841–842, 2010.
[18] M. Steinbach, G. Karypis, and V. Kumar. A
comparison of document clustering techniques. In 6th
ACM SIGKDD,World Text Mining Conference, 2000.
[19] T. Xu and D. W. Oard. Wikipedia-based topic
clustering for microblogs. Proceedings of the American
Society for Information Science and Technology, pages
1–10, 2011.
[20] W. X. Zhao, J. Jiang, J. H. J. Weng, E. Lim, H. Yan,and X. Li. Comparing twitter and traditional mediausingtopic models. In Proceedings of the 33rd
European Conference on Information Retrieval., 2011.