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- 1. 2012 年 10 月 28 日 Project 311 最終報告 震災時における デマの拡散と制御に関する研究 高安＠東工大 高安美佐子 A ，高安秀樹 B ，佐野幸恵 C ，山田健太 D ，渡辺隼史 E ，三浦航 A ，佐藤和也 A 東工大 A ，ソニー CSLB ，日大 C ，早大 D ，ホットリンク E
- 2. 目的 提供された貴重なデータを活用し，非常時における人間の集団行動に対する定量分析と 可視化を行う．社会の異常なパニック現象をモデル化することにより， そのパニックを和らげる方法や予防，より安全な社会システムの構築につなげる． 時系列解析 空間構造解析 不安の定量化 マスコミ効果の測定 震災前→震災後 デマの拡散スピード デマの伝播過程 訂正ツイートの効果 訂正の伝播過程 デマ収束までの時間 RT の影響 急上昇をワードの検出 うわさの感染症 モデルによるシミュレーション デマの拡散と収束のシミュレーションを行えるデマ拡散版 SPEEDI デマが広がりやすい状況を警告する，デマ版天気予報
- 3. 本研究の 4 つの発見 時系列解析 １．自発的なデマの訂正はあまり拡散しない ２．善意の心配がデマを拡散させている可能性が高い 空間構造解析 ３．浦安市広報に代表される公式アカウントやマス コミによるデマ訂正の情報は有効に機能した 感染症 のモデル ４．デマの拡散は，感染症モデルで説明可能 → デマ拡散の予測へ応用
- 4. コスモ石油爆発とデマの拡散?収束 3/11 15:47 頃 時系列解析 — コスモ石油 ツイート数／ 3/12 正午頃 — デマ コスモ石油が — デマ訂正 HP で公式否定 3/11 18:40 頃 分 デマが出始める 3/11 20:55 3/12 15:31 浦安市による公式のデマ訂正は拡散？ 最初のデマ訂正ツイート 「デマ流すな！根拠ないだろ」 しかし，善意のデマ訂正は拡散せず
- 5. デマの基準について “ コスモ石油”を含むツイート 基準語句 1 のいずれか 含む 含まない デマに関わっている可能性 デマに関わりがない 基準語句 2 のいずれか 含む 含まない デマ訂正 ツイート デマツイート 根性 マイニングの結果 、幾 つかの語 を追加 ? 傘 ? カッパ ? 有害物質 ? レインコート
- 6. 颜文字や形容词で「雰囲気」の定量化 「歯がゆい」 ツイート割合（ ×10-3 ） デマの割合 r=0.79 デマの割合 ／時 典型的な形容詞の割合 デマの変動 が重 なる デマの拡散は，必ずしも悪意に 基づくものではない 歯がゆい，もどかしい，いたたまれない， こころもとない，恐ろしい，すごい，もの 善意の心配がデマを拡散させて 凄い，すさまじい， 憂い， 脆い いる可能性が高い
- 7. コスモ石油のデマ?訂正 RT ネットワーク A B 空間構造解析 A が B のツイートをリツイート 基本的にはどちらもループの少ない木構造 （フィードバックは少なく、分岐しながら広がっていく）
- 8. うわさの感染症モデルの构筑 - デマの拡散の再現 - 実データ 件 感染症 のモデル 「感染確率」の推定
- 9. うわさの感染症モデルの构筑 - 訂正の拡散の再現 - 実データ 65,714 件 感染症 のモデル
- 10. うわさの感染症モデルによる予測 もし，訂正情報 の発生 が早 かったら？ 実データ 感染症 のモデル 条件 をいろいろと変 えて、 デマの拡散 を抑制 する効果 を調 べられる
- 12. appendix
- 13. 揺れの大きさと书き込み数の変化 １分 データを組み合わせることで現象がよりクリアに見える！
- 14. ユーザーの投稿過程の可視化 1 ユーザー変化をトレーシング
Editor's Notes
- #9: 次にわれわれのチームは，エージェントベースの「噂の感染症モデル」を用いて実測された時系列や，個人のダイナミクスを再現することに成功しています． モデル内には，噂の感染力や，個人がどういった心理状態にあるのかを示すパラメータもあるため，直接観測できないことも見積もることができます． なお，時系列は全数で規格化しているので 1 日目の山と 2 日目の山の高さがほぼ同じです．
- #15: また，今回のデータには，どのユーザーがいつ何を投稿したかが分かるので，このようなグラフを作成することも可能です．このグラフの横１行は１ユーザーがどのようにコスモ石油の书き込みを行ったかを见ることができます，このユーザーは始めコスモ石油のつぶやきを行いその后デマ情报を拡散してしまいその后订正ツイートを行ったことを表しています．