Published Papers - KAZAMA Kazuhiro

メトリック空間オブジェクトを対象とした中心性指標の提案

伏見 卓恭, 斉藤 和巳, 風間 一洋

第13回情報科学技術フォーラム ( 電子情報通信学会 )    2014.09  [Refereed]

Proposal of Multi-Agent Information Diffusion Model for Twitter

Keisuke Ikeda, Yoshiyuki Okada, Fujio Toriumi, Takeshi Sakaki, Kazuhiro Kazama, Itsuki Noda, Kosuke Shinoda, Hirohiko Suwa, Satoshi Kurihara

2014 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2014) ( IEEE/WIC/ACM )    2014.08  [Refereed]

SIR-Extended Information Diffusion Model of False Rumor and its Prevention Strategy for Twitter

Satoshi Kurihara, Yoshiyuki Okada, Takeshi Sakaki, Fujio Toriumi, Kosuke Shinoda, Kazuhiro Kazama, Itsuki Noda, Masuyuki Numao

Journal of Advanced Computational Intelligence and Intelligent Informatics ( FUJI TECHNOLOGY PRESS LTD )  18 ( 4` ) 598 - 607   2014.07  [Refereed]

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Nowadays, users of Twitter, one of famous social networking service, have rapidly increased in number, and many people have been exchanging information by Twitter. When the Great East Japan Earthquake struck in 2011, people were able to obtain information from social networking services. Though Twitter played an important role, one problem was especially pointed out: false rumor diffusion. In this study, we propose an information diffusion model based on the SIR model and discuss how to prevent false rumor diffusion.

DOI

異種協調型災害情報支援システム実現に向けた基盤技術の構築

鳥海 不二夫, 篠田 孝祐, 榊 剛史, 栗原 聡, 風間 一洋, 野田 五十樹

人工知能学会論文誌特集「データマイニングとシミュレーション」特集・編集委員   29 ( 1 ) 113 - 119   2014.01  [Refereed]

部分情報からの連結性を保証したネットワーク構造推定

伏見 卓恭, 斉藤 和巳, 風間 一洋

人工知能学会全国大会論文集 ( 一般社団法人 人工知能学会 )  2014 ( 0 ) 3F33 - 3F33   2014

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<p>本研究では，属性情報などのエゴセントリック情報のみを用いて，連結性を保証してネットワーク全体の構造を推定することを試みる．著者らが提案した構造推定法では，推定結果のネットワークが1つの連結成分になることを保証していない．そこで，全体が1つの連結成分になることを保証した推定法へ改良する． 複数のネットワークを用いた評価実験から，改良を加えた提案法が既存法より推定精度が幾分か高いことを確認する．</p>

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Collaborative heterogeneous integration of disaster and rescue information (CHIDRI)

Fujio Toriumi, Kosuke Shinoda, Takeshi Sakaki, Satoshi Kurihara, Kazuhiro Kazama, Itsuki Noda

Transactions of the Japanese Society for Artificial Intelligence   29 ( 1 ) 113 - 119   2014  [Refereed]

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Traditionally, the support in the event of a disaster were based on hardware, the expertise are strongly required to rescuers. For this reason, in the case that the disaster had been occurred, the role of the people expressly divided to the victims, the rescuers, and the onlookers. On the other hand, in the case of the Great East Japan Earthquake, many engineers tried to develop information support systems for the victims by information technologies. This fact shows that some of engineers become not onlookers but information providers. It can be estimated that one of the reasons why these systems were realized is generally utilization of information infrastructures such as web in our daily life. After such situation is considered, we started a project "Collaborative Heterogeneous Integration of Disaster and Rescue Information (CHIDRI)" which adopted to Challenge for Realizing Early Profits (CREP) in Japanese Society for Artificial Intelligence. In this paper, we explain the frame, the purpose, and the activities of the challenge.

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MDSR: An eigenvector approach to core analysis of multiple directed graphs

Shoko Kato, Kazumi Saito, Kazuhiro Kazama, Tetsuji Satoh

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ( Springer Verlag )  8862   447 - 458   2014  [Refereed]

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In this paper, we address a problem of extracting core portions of a network represented as amultiple directed graph. For this purpose, we propose a new core extraction method called MDSR (Multiple-Directed-Spectral-Relaxation) based on an eigenvector approach. The MDSR method extracts a user-defined number of core portions by repeating the following three steps
1) calculating the left-and right-eigenvectors of an adjacency matrix of a network, 2) quantizing elements of these eigenvectors to binary ones as an indicator of extracting core portion, and 3) removing links of the extracted one. The left-and right-eigenvectors at the first iteration respectively correspond to the hub and authority vectors of the HITS algorithm. In our experiments using a reply network on Twitter constructed as a multiple directed graph, we demonstrate that theMDSR method was able to uncover some part of communities, such as groups of similar account names, users who like to sendmessages to ‘null’, and so on. We also show that some communities were overlapped ones. Furthermore, we confirmthat such communities were hard to be automatically found by two methods, which were constructed by straightforwardly extending the conventional k-core method.

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Extracting important sightseeing spots considering actual distances

FUSHIMI Takayasu, SAITO Kazumi, MUTOH Nobuaki, IKEDA Tetsuo, KAZAMA Kazuhiro

JSAI Technical Report, SIG-KBS ( The Japanese Society for Artificial Intelligence )  102   6 - 6   2014

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<p>In this paper, we attempt to extract important sightseeing spots so as to improve the convenience of tourists. To this end, we propose two measures, one is detour centrality which is a easiness measure of brief detour and the other is convenience centrality which is a accessibility measure, based on traditional closeness and betweenness centrality respectively. These traditional centrality measures do not intend for actual distances as weights on links therefore, our proposed measures are designed with distances between spots. Furthermore, by assuming that these natures like easiness of detour and accessibility of spots behave collaboratively, we also propose extended measures for an arbitrary set of spots. In our experiments using two real sightseeing spots datasets, we show that our extended method can extract an appropriate set of spots in terms of easiness of detour and accessibility, and these pivots extracted by two methods are substantially different.</p>

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顔文字に着目したツイートの感情変化の分析

風間 一洋, 榊 剛史, 鳥海 不二夫, 篠田 孝祐, 栗原 聡, 野田 五十樹

WebDB Forum 2013     2013.11  [Refereed]

MDSR法を用いたreplyツイートネットワークの特性分析

加藤 翔子, 斉藤 和巳, 風間 一洋, 佐藤 哲司

WebDB Forum 2013     2013.11  [Refereed]

エゴセントリック情報からのネットワーク構造推定

伏見 卓恭, 斉藤 和巳, 風間 一洋

WebDB Forum 2013     2013.11  [Refereed]  [Invited]

ノードの注目度に基づく機能コミュニティ抽出法

伏見 卓恭, 斉藤 和巳, 池田 哲夫, 風間 一洋

情報処理学会論文誌：データベース   6 ( 4 ) 104 - 112   2013.09  [Refereed]

ネットワーク分析によるTwitterユーザのフォロー形成に関する一考察

小出 明弘, 斉藤 和巳, 風間 一洋, 鳥海 不二夫

情報処理学会論文誌：数理モデル化と応用   6 ( 2 ) 164 - 173   2013.08  [Refereed]

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本稿では，Twitterのフォローネットワークを分析することにより，ユーザのフォローがどのような目的で行われているのか議論する．まず，フォローネットワークの特徴を把握するため，ネットワーク内の高次数ノードに着目し，ブログの読者関係とレビューサイトのお気に入り関係を表したそれぞれのネットワーク構造の特徴と比較する．その結果，ブログやレビューサイトでは，比較的小規模な高コリンクグループが得られたのに対し，フォローネットワークでは，強い双方向関係により構築された大規模な高コリンクグループと，双方向関係がほとんど見られない複数の小規模な低コリンクグループが存在することが分かった．さらに，高次数ノードのツイート集合を分析し，これらのグループは同じようなツイートをしているにもかかわらず，フォロワとの関係に大きな違いが見られることが分かった．In this paper, we explored Twitter's follow mechanism through a network analysis. In order to characterize the salient structure of Twitter's follow network, we first empirically compared it with those of reader and favorite networks form blog and review sites by focusing on their high degree nodes. From this experiment, we observed a relatively large high co-link group whose nodes are mutually connected to each other and some small low co-link groups whose nodes are not mutually connected to each other in Twitter's network. On the other hand, most groups are relatively small high co-link groups such as discussion groups in blog and review sites. Moreover, by analyzing messages tweeted by these group's users, we found that these groups much differ in relation with followers although these groups resemble in content of tweets.

リンクの向きに着目した機能コミュニティとモチーフの関係分析

伏見 卓恭, 斉藤 和巳, 池田 哲夫, 風間 一洋

情報処理学会論文誌：数理モデル化と応用   6 ( 2 ) 137 - 146   2013.08  [Refereed]

Information Sharing on Twitter During the 2011 Catastrophic Earthquake

Fujio Toriumi, Takeshi Sakaki, Kosuke Shinoda, Kazuhiro Kazama, Satoshi Kurihara, Itsuki Noda

SWDM 2013 ( ASSOC COMPUTING MACHINERY )    1025 - 1028   2013.05  [Refereed]

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Such large disasters as earthquakes and hurricanes are very unpredictable. During a disaster, we must collect information to save lives. However, in time disaster, it is difficult to collect information which is useful for ourselves from such traditional mass media as TV and newspapers that contain information for the general public. Social media attract attention for sharing information, especially Twitter, which is a hugely popular social medium that is now being used during disasters. In this paper, we focus on the information sharing behaviors on Twitter during disasters. We collected data before and during the Great East Japan Earthquake and arrived at the following conclusions:- Many users with little experience with such specific functions as reply and retweet did not continuously use them after the disaster.- Retweets were well used to share information on Twitter.- Retweets were used not only for sharing the information provided by general users but used for relaying the information from the mass media.We conclude that social media users changed their behavior to widely diffuse important information and decreased non-emergency tweets to avoid interrupting critical information.

Regional Analysis of User Interactions on Social Media in Times of Disaster

Takeshi Sakaki, Fujio Toriumi, Kosuke Shinoda, Kazuhiro Kazama, Satoshi Kurihara, Itsuki Noda

WWW 2013     2013.05  [Refereed]

SIR-Extended Information Diffusion Model of False Rumor and its Prevention Strategy for Twitter

Satoshi Kurihara, Yoshiyuki Okada, Takeshi Sakaki, Fujio Toriumi, Kosuke Shinoda, Kazuhiro Kazama, Itsuki Noda, Masuyuki Numao

The 5th International Workshop on Emergent Intelligence on Networked Agents (WEIN 2013) ( AAMAS )    2013.05  [Refereed]

東日本大震災時におけるTwitterの活用状況とコミュニケーション構造の分析

SHINODA Kosuke, SAKAKI Takeshi, TORIUMI Fujio, KAZAMA Kazuhiro, KURIHARA Satoshi, NODA Itsuki, MATSUO Yutaka

J. SOFT ( Japan Society for Fuzzy Theory and Intelligent Informatics )  25 ( 1 ) 598 - 608   2013.03  [Refereed]

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The Great East Japan Earthquake caused devastating damage to infrastructure and loss of life. Many people used social media to communicate and share information through the series of earthquake events and aftershocks. We analyzed over 400 million Tweets and the Twitter network in Japan before and after the earthquake, which revealed the earthquake that occurred in the Twitter milieu itself. We performed analyses of two kinds. As a result, we suggest that the role of Twitter as a means to share information changed during the earthquake period.

DOI

異なる視点からのノード機能に基づくコミュニティ抽出法

伏見 卓恭, 斉藤 和巳, 風間 一洋

日本データベース学会論文誌 ( 日本データベース学会 )  11 ( 3 ) 27 - 32   2013.02  [Refereed]

The possibility of social media analysis for disaster management

Takeshi Sakaki, Yutaka Matsuo, Satoshi Kurihara, Fujio Toriumi, Kosuke Shinoda, Itsuki Noda, Koki Uchiyama, Kazuhiro Kazama

2013 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2013     238 - 243   2013  [Refereed]

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Collecting sharing, and delivering information in disaster situations is crucially important. Mass media such as TV, radio, and newspapers have played important roles in information distribution in past disasters and crises. Recently, social media have received much attention for their use as an information sharing tool. Especially, it is said that people used Twitter to collect and share information in the aftermath of the Great East Japan Earthquake. In academic fields, some researchers have started to propose some methods and systems for disaster management by analyzing social media data. Other people doubt whether social media will actually function effectively for disaster management because of uncertainty and inaccuracies related to rumors and misunderstanding. In this paper, we overview current studies of social media analysis for disaster management and explain some studies in detail to show their possibility and availability. We specifically examine situational awareness, user behavior analysis and information propagation analysis, which are three approaches to social media analysis, to clarify what social media analysis can and cannot do. Additionally, we propose some concepts for social media analysis and show how those concepts help to collaborate with us, researchers in social media analysis fields and other research fields. © 2013 IEEE.

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