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1 GDELT Multifacet comparative analysis of newspaper articles from different conutries - Analysis based on Global Database of Events, Language and Tone (GDELT) Masaharu Yoshioka 1 Noriko Kando Graduate School of Information Sciecne and Technology, Hokkaido University 2 2 National Institute of Informatics Abstract: The News Site Contrast (NSContrast) system analyzes multiple news sites based on the concept of contrast set mining and it can extract terms that characterize different topics of interest for specific countries. In this study, we used the NSContrast system to analyze Global Database of Events, Language and Tone (GDELT) data by comparing news articles from different regions (e.g., USA, Asia, and the Middle East). We also present examples of analyses performed using this system. 1 NSContrast [1, 2] Global Database of Events, Language and Tone (GDELT) [3] Google News ( ) [email protected] 1 NSContrast GDELT NSContrast GDELT 2 NSContrast GDELT 2.1 NSContrast NSContrast[1] ( ) ChaSen CaboCha Wikipedia Wikipedia [4] NSContrast
2 1. [5] Wikipedia 4. ( ) Wikipedia 5. 3 ( ) ( ) ( ) Wikipedia ( ) ( ) 2.2 Global Database of Events, Language and Tone (GDELT) Global Database of Events, Language and Tone (GDELT) [3] Google News BBC Newswire 2 GDELT Event Database GDELT ID URL Conflict and Mediation Event Observation (CAMEO)
3 ( ) ( ) ( ) ( ) ( ) GDELT Global Knowledge Graph (GKG) GDELT Event Database ID ( ) ( ) URL GDELT Google BigQuery 2.3 GDELT NSContrast GDELT GDELT Global Knowledge Graph (GKG) GDELT GKG GDELT GKG NSContrast 3 GKG ( ) -100( ) 100( ) URL NSContrast GDELT.com.net 1. world-newspapers.com 3 BBC Newswire 2. (.jp,.uk ) 3. ( ) 8 URL GDELT GKG URL CGI URL URL URL URL
4 NSContrast GDELT GKG ,177,775 38%(4,280,168) 1: 2,933,282 1,295,274 1,258, , , , ,204 35,552 4,280,168 nawaz sharif(mr. Nawaz Sharif: ) 10 nawaz sharif nawaz sharif ( 2) nawaz sharif ( 3) 3.2 NSContrast ( ) ronald bechtold (Mr. Ronald Bechtold: ) 3: Results of the term collocation analysis imran khan(mr. Imran Khan: ) nawaz sharif nawaz sharif 4 nawaz sharif Graph1( ) Graph2( ) Graph3( ) Graph4( ) nawaz sharif
5 1: 2: Nawaz Sharif
6 人工知能学会 インタラクティブ 情報アクセスと可視化マイニング研究会(第8回) $ 図 4: Nawaz Sharif を対象とした多観点分析 #!" $ # 図 5: Nawaz Sharif と Imran Khan を対象とした多観点分析
7 人工知能学会 インタラクティブ 情報アクセスと可視化マイニング研究会(第8回) $ 図 6: Nawaz Sharif の賛否に関する比較 この分析ウィンドーにおいて 検索式を修正すると 条件を満たした対象記事に対して グラフが更新され る 図 5 は 検索式に 先ほどの分析で得た人物 imran khan を追加した場合を示す このグラフから 6 月 の段階では imran khan は アジアとヨーロッパで は それなりに注目されていたが アメリカでは あま り注目されていないことが分かる しかし 11 月にな ると この imran khan は アメリカの無人攻撃機 を利用したタリバンへの攻撃 に反対することで ア メリカでも注目されることになる この様に アメリ カのニュースサイトを見ていただけでは 突然 注目 された人物という事になるが NSContrast のように 世界のニュースサイトを比較分析していくシステムを 用いることにより この様な自体に対しても より多 角的な分析ができるようになると考えている また この多観点分析システムは 様々な観点に関す る情報を並べて提示するだけでなく 異なる条件の検索 結果の比較を行うことも可能である 図 6 は nawaz sharif を含む記事における賛否に対する様々な国の比 較を行った結果である Graph1(左上) が全てのニュー スサイト (追加検索条件なし) Graph2(右上) がインド (サイト国 インド) Graph3(左下) がパキスタン (サ イト国 パキスタン) Graph4(右下) がアメリカ (サイ ト国 アメリカ) の賛否の時間遷移を表示したグラフと なる この比較から アメリカとパキスタンにおける 賛否の違いなどを読み取ることが可能となる 3.3 考察 この分析の結果 NSContrast が提供する比較分析機 能は 世界中では注目されてるとはいえないが 特定 の地域では注目されているような ローカルな情報を 見つけ出すのに有用であると考えている しかし より精緻な分析を行うためには 以下の点に おいて データを充実させる必要があると考えている サイトの所属する国の判定 現時点では 38%の記事について サイトの所属 する国が正しく判定されていない これについて は より適切な情報となるように 更新すること が望ましい ニュース記事のタイトル GDELT の GKG データは 記事を単位として作 られているが 記事から抽出された情報をリスト としてみるだけでは 閲覧性が高くない 少なく とも 記事のタイトルを 別のデータとして作成 した上で 表示させることが出来ると 閲覧性の 向上につながると考えている 4 まとめ 本論文では ニュース記事の多観点分析を支援する NSContrast と ニュース記事を基盤とした大規模なイ
8 GDELT NSContrast (B) [1],,. nscontrast., 2011-IFAT-103, IFAT [2] Masaharu Yoshioka and Noriko Kando. Multifaceted analysis of news articles by using semantic annotated information. In Proceedings of the fifth workshop on Exploiting semantic annotations in information retrieval, ESAIR 12, pp , New York, NY, USA, ACM. [3] Kalev Leetaru and Philip A. Schrodt. Gdelt:global data on events, location, and tone, In ISA Annual Convention 2013, Vol. 2, p. 4, [4] Yohei Seki, Noriko Kando, and Masaki Aono. Multilingual opinion holder identification using author and authority viewpoints. Information Processing & Management, Vol. 45, No. 2, pp , [5] Jon Kleinberg. Bursty and hierarchical structure in streams. In Proceedings of the 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pp , New York, NY, USA, ACM Press
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