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ソーシャルビッグデータで理解するヒトと社会の性質 Dynamics of Human Behavior and Societies based on Social Big Data 高野雅典 Masanori TAKANO アブストラクトソーシャルビッグデータはヒトや社会について分析し理解するための強力なツールであるソーシャルビッグ

たに発生した問題に関するソーシャルビッグデータ分析研究も紹介するキーワード計算社会科学, ビッグデータ, 協調行動, 社会的グルーミング, 内集団バイアス, 性選択 Abstract Social big data provide a particularly powerful tool for the quantitative study of human behavior and

In this paper, we introduce four topics in social science: cooperative behavior, social grooming and social structures, in-group bias, and sexual selection.

1 ソーシャルビッグデータで理解するヒトと社会の性質 Dynamics of Human Behavior and Societies based on Social Big Data 高野雅典 Masanori TAKANO アブストラクトソーシャルビッグデータはヒトや社会について分析し理解するための強力なツールであるソーシャルビッグデータ登場以前には観測が難しかった大量のヒトの詳細な社会的行動を分析できるからである本稿では社会科学の研究トピックのうち協調行動社会的グルーミングと社会構造内集団バイアス性的選好の四つについて紹介するそれぞれ先行研究をレビューした後関連するソーシャルビッグデータ分析による研究について述べるまた Web の普及に伴って新たに発生した問題に関するソーシャルビッグデータ分析研究も紹介するキーワード計算社会科学, ビッグデータ, 協調行動, 社会的グルーミング, 内集団バイアス, 性選択 Abstract Social big data provide a particularly powerful tool for the quantitative study of human behavior and social phenomena, because it enables us to observe the social behaviors of humans in detail. In this paper, we introduce four topics in social science: cooperative behavior, social grooming and social structures, in-group bias, and sexual selection. We introduce previous works and recent studies on social big data analysis in each topic. Additionally, we mention new problems that are arising with the growth of the Web and studies on these problems. Key words Computational Social Science, Big Data, Cooperation, Social Grooming, In-group Bias, Sexual Selection 知見の取り込み 1. はじめに Web を介したオンラインコミュニケーションは今や我々の日常生活の一部となっておりそこでのヒトの社会的行動のデー抽象的数理モデルシミュレーション研究室実験理解がしやすいタが日々大量に生成蓄積されている例えばマイクロブログソーシャルネットワーキングサービス SNS オンラインゲーム Massively Multiplayer Online Role-Playing Game 図1 ソーシャルビッグデータのデータマイニングフィールドワークアンケート発見した知見をフィードバック現実に近い理解が煩雑ソーシャルビッグデータ分析による社会科学研究の位置付け MMO-RPG やソーシャルゲームなどであるこのようなソーシャルビッグデータの分析によってヒトや社会の性質につ 2 いて知ろうとする領域を計算社会科学 1 と言う注 1 計算る舞うことができるそして比較的小規模なものでも数万人以上のユーザの社会的相互作用であるため非常に大きな集団サ社会科学をテーマとした国際会議 International Conference of イズを仮定することの多い理論研究数理解析シミュレーショ Computational Social Science (IC2S2) 注 2 は 2015 年に第 1 ンとの対比がしやすいまたサービスの仕組みにより調査回が開催され 495 件という非常に多くの研究が投稿され大き研究が対象としている実社会よりはユーザの行動は制限されるな盛り上がりを見せたため現実の社会よりも理解が容易であるそしてユーザの社会科学研究における基本的なアプローチ理論実験調行動はログとして記録されるためより詳細な分析が可能である査観察と比較してソーシャルビッグデータ分析のアプローすなわちソーシャルビッグデータ分析による計算社会科学はチは図 1 のように位置付けることができる Web では実際にヒ理論実験研究と調査観察研究を補間する強力な手段である 3 トが自分の意志でサービスを利用しサービスの仕組みの範囲加えて同図のようにこのアプローチは既存の枠組みの中に位内で任意のタイミングで任意の行動が可能であるため理論的置付けられるため非常に多く存在する先行研究の蓄積は Web 実験的な先行研究のようにモデル化された環境よりも自由に振上でのヒトの行動理解とそれに基づいたサービスの改善にも有高野雅典株サイバーエージェント秋葉原ラボ takano masanori@cyberagent.co.jp Masanori TAKANO, Nonmember (Akihabara Laboratory, CyberAgent, Inc., Tokyo, Japan). 電子情報通信学会基礎境界ソサイエティ Fundamentals Review Vol.10 No.4 pp 年 4 月 c 電子情報通信学会 2017 IEICE Fundamentals Review Vol.10 No.4 注 1 計算社会科学はソーシャルビッグデータ分析に基づくものだけでなくオンライン実験バーチャルラボやコンピュータシミュレーションを用いたアプローチなども含む本稿では主にビッグデータ分析によるアプローチについて述べる注 2 このときの基調講演は YouTube で公開されており誰でも見ることができる様々な分野の第一人者が計算社会科学研究の成果と期待について述べている 275

2 Web Takano SNS Twitter Facebook SNS 2. 2 social grooming IEICE Fundamentals Review Vol.10 No.4

3 Facebook 34 Twitter ; weak tie Yule Simon Yule Simon Vlahovic 3 Skype 59 Burke Facebook Facebook Facebook 60 Takano in-group bias IEICE Fundamentals Review Vol.10 No.4 277

4 66 Web 67 Web Kenrick Web SNS 77 Rudder 77 Ok Ok Araújo Google Bing beautiful woman ugly woman 3. Web Web IEICE Fundamentals Review Vol.10 No.4

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