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 E-mail takano masanori@cyberagent.co.jp Masanori TAKANO, Nonmember (Akihabara Laboratory, CyberAgent, Inc., Tokyo, 101-0021 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.275 281 2017 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 https://www.youtube.com/channel/ucugsblwl4g2cqqfk95ozjvw 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
Web 2. 2. 1 4 5 6 7 8 9 10 11 13 14 18 19 21 Takano 11 13 8 9 15 22 23 24 11 12 13 12 13 11 12 13 SNS Twitter Facebook 25 26 SNS 2. 2 social grooming 27 31 31 32 27 29 150 30 276 IEICE Fundamentals Review Vol.10 No.4
150 33 Facebook 34 Twitter 35 27 28 29 36 37 12 13 29 38 40 ; weak tie 2 41 42 Yule Simon 43 45 46 51 33 49 47 48 50 55 Yule Simon 53 55 3 56 57 30 58 Vlahovic 3 Skype 59 Burke Facebook Facebook Facebook 60 Takano 55 150 40 2. 3 in-group bias 61 62 62 63 64 65 IEICE Fundamentals Review Vol.10 No.4 277
66 Web 67 Web 63 64 24 68 70 71 2. 4 Kenrick 72 73 74 4 75 76 Web SNS 77 Rudder 77 Ok Ok Araújo Google Bing 78 22 beautiful woman ugly woman 3. Web Web 79 4 278 IEICE Fundamentals Review Vol.10 No.4
79 0.5% 80 Twitter Web 81 82 83 84 Toriumi 85 66 86 88 4. 4 2 3 89 89 89 60 90 Burke 60 Scissors 90 Facebook Facebook Facebook Facebook 91 89 92 1 D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, and M. Van Alstyne, Computational social science, Science, vol.323, no.5915, pp.721 3, 2009. 2 (Computational Social Science) vol.30 no.6 pp.856 859 2015 3 D.J. Watts, Everything Is Obvious*: *Once You Know the Answe, Crown Business, 2011. 4 J.M. Smith and E. Szathmáry, The Origins of Life: From the Birth of Life to the Origin of Language, Oxford University Press, 2000. 5 E. Fehr and U. Fischbacher, The nature of human altruism, Nature, vol.425, no.23, pp.785 791, 2003. 6 R. Axelrod, The Evolution of Cooperation: Revised Edition, Basic Books, 2006. 7 J. H, Barkow, L. Cosmides, and J. Tooby, The Adapted Mind: Evolutionary Psychology and the Generation of Culture, Oxford University Press, 1995. 8 M.A. Nowak, Five rules for the evolution of cooperation, Science, vol.314, no.5805, pp.1560 1563, 2006. 9 D.G. Rand and M.A. Nowak, Human cooperation, Trends in Cognitive Sciences, vol.17, no.8, pp.413 425, 2013. 10 J.M. Smith, Evolution and the Theory of Games, Cambridge University Press, 1982. 11 M. Takano, K. Wada, and I. Fukuda, Environmentally driven migration in a social network game, Scientific reports, vol.5, p.12481, 2015. 12 M. Takano, K. Wada, and I. Fukuda, Reciprocal altruism-based cooperation in a social network game, New Gener. Comput., vol.34, no.3, pp.257 271, 2016. 13 M. Takano, K. Wada, and I. Fukuda, Lightweight interactions for reciprocal cooperation in a social network game, Proceedings of The 8th International Conference on Social Informatics (SocInfo), pp.125 137, 2016. 14 F. Santos, J. Pacheco, and T. Lenaerts, Cooperation prevails when individuals adjust their social ties, PLoS computational biology, vol.2, no.10, p.e140, 2006. 15 G. Ichinose and T. Arita, The role of migration and founder effect for the evolution of cooperation in a multilevel selection context, Ecological Modelling, vol.210, IEICE Fundamentals Review Vol.10 No.4 279
no.3, pp.221 230, 2008. 16 X. Chen, F. Fu, and L. Wang, Social tolerance allows cooperation to prevail in an adaptive environment, Phys. Rev. E, vol.80, 051104, 2009. 17 J.A. Damore and J. Gore, A slowly evolving host moves first in symbiotic interactions, Evolution; international journal of organic evolution, vol.65, no.8, pp.2391 2398, 2011. 18 X. Chen, A. Szolnoki, and M. Perc, Risk-driven migration and the collective-risk social dilemma, Phys. Rev. E, vol.86, 036101, 2012. 19 K. Fehl, D.J. van derpost, and D. Semmann, Coevolution of behaviour and social network structure promotes human cooperation, Ecology letters, vol.14, no.6, pp.546 51, 2011. 20 D.G. Rand, S. Arbesman, and N. Christakis, Dynamic social networks promote cooperation in experiments with humans, Proc. Nat. Acad. Sci., vol.108, no.48, pp.19193 19198, 2011. 21 J. Wang, S. Suri, and D.J. Watts, Cooperation and assortativity with dynamic partner updating, Proc. Nat. Acad. Sci. USA, vol.109, no.36, pp.14363 8, 2012. 22 J.W. Pepper and B.B. Smuts, A mechanism for the evolution of altruism among nonkin: positive assortment through environmental feedback, The American naturalist, vol.160, no.2, pp.205 13, 2002. 23 J.W. Pepper, Simple models of assortment through environmental feedback, Artificial life, vol.13, no.1, pp.1 9, 2007. 24 R.L. Trivers, The evolution of reciprocal altruism, Quarterly Review of Biology, vol.46, pp.35 37, 1971. 25 Y. Hirahara, F. Toriumi, and T. Sugawara, Evolution of cooperation in SNS-norms game on complex networks and real social networks, Proceedings of The 6th International Conference on Social Informatics (SocInfo), pp.112 120, 2014. 26 I. Okada, H. Yamamoto, F. Toriumi, and T. Sasaki, The effect of incentives and meta-incentives on the evolution of cooperation, PLoS Computational Biology, vol.11, no.5, p.e1004232, 2015. 27 H. Kobayashi and S. Kohshima, Unique morphology of the human eye, Nature, vol.387, no.6635, pp.767 768, 1997. 28 H. Kobayashi and K. Hashiya, The gaze that grooms: contribution of social factors to the evolution of primate eye morphology, Evolution and Human Behavior, vol.32, no.3, pp.157 165, 2011. 29 R. Dunbar, Gossip in evolutionary perspective, Review of General Psychology, vol.8, no.2, pp.100 110, 2004. 30 R. Dunbar, On the origin of the human mind, Evolution and the Human Mind, P. Carruthers and A. Chamberlain, eds., pp.238 253, Cambridge University Press, 2000. 31 M. Nakamura, Gatherings of social grooming among wild chimpanzees: implications for evolution of sociality, Journal of Human Evolution, vol.44, no.1, pp.59 71, 2003. 32 H. Nelson, Encoding and decoding mutual grooming: Communication with a specialized form of touch, Dissertation abstracts international. B, The sciences and engineering, vol.68, no.4-b, p.2664, 2007. 33 W.-X. Zhou, D. Sornette, R.A. Hill, and R.I.M. Dunbar, Discrete hierarchical organization of social group sizes, Proc. R. Soc. B: Biological Sciences, vol.272, no.1561, pp.439 444, 2005. 34 V. Arnaboldi, A. Guazzini, and A. Passarella, Egocentric online social networks: Analysis of key features and prediction of tie strength in Facebook, Comput. Commun., vol.36, no.10-11, pp.1130 1144, 2013. 35 B. Gonçalves, N. Perra, and A. Vespignani, Modeling users activity on twitter networks: validation of Dunbar s number, PLoS ONE, vol.6, no.8, p.e22656, 2011. 36 F. Harrison, J. Sciberras, and R. James, Strength of social tie predicts cooperative investment in a human social network, PLoS ONE, vol.6, no.3, p.e18338, 2011. 37 M. Haan, P. Kooreman, and T. Riemersma, Friendship in a public good experiment, IZA Discussion Paper, vol.2108, 2006. 38 M. Granovetter, The strength of weak ties, American Journal of Sociology, vol.78, pp.1360 1380, 1973. 39 N. Eagle, M. Macy, and R. Claxton, Network diversity and economic development, Science, vol.328, no.5981, pp.1029 31, 2010. 40 V. Arnaboldi, M. Conti, A. Passarella, and R. Dunbar, Dynamics of personal social relationships in online social networks: a study on Twitter, Proceedings of the first ACM conference on Online social networks - COSN 13, pp.15 26, ACM Press, New York, New York, USA, 2013. 41 A.-L. Barabási and R. Albert, Emergence of scaling in random networks, Science, vol.286, no.5439, pp.509 512, 1999. 42 R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance of complex networks, Nature, vol.406, no.6794, pp.378 382, 2000. 43 G.U. Yule, A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S., Philos. Trans. R. Soc. B: Biological Sciences, vol.213, no.402-410, pp.21 87, 1925. 44 H.A. Simon, On a class of skew distribution functions, Biometrika, vol.42, no.3-4, pp.425 440, 1955. 45 M.E.J. Newman, Power laws, Pareto distributions and Zipf s law, Contemp. Phys., vol.46, pp.323 351, 2005. 46 S.G.B. Roberts and R.I.M. Dunbar, Communication in social networks: Effects of kinship, network size, and emotional closeness, Personal Relationships, vol.18, no.3, pp.439 452, 2011. 47 V. Arnaboldi, M. Conti, A. Passarella, and F. Pezzoni, Analysis of ego network structure in online social networks, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, pp.31 40, IEEE, 2012. 48 C. Song, D. Wang, and A.-L. Barabasi, Connections between human dynamics and network science, 2012 (arxiv:1209.1411). 49 V. Arnaboldi, M. Conti, A. Passarella, and F. Pezzoni, Ego networks in Twitter: An experimental analysis, 2013 Proceedings IEEE INFOCOM, pp.3459 3464, IEEE, 2013. 50 A. Fujihara and H. Miwa, Homesick Lévy walk: A mobility model having Ichi-Go Ichi-e and scale-free properties of human encounters, 2014 IEEE 38th Annual Computer Software and Applications Conference, pp.576 583, IEEE, 2014. 51 R.I.M. Dunbar, Do online social media cut through the constraints that limit the size of offline social networks?, Royal Society Open Science, vol.3, no.1, p.150292, 2016. 52 T. Hossmann, T. Spyropoulos, and F. Legendre, A complex network analysis of human mobility, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp.876 881, IEEE, 2011. 53 T. Pachur, L.J. Schooler, and J.R. Stevens, When will we meet again? Regularities of social connectivity and their reflections in memory and decision making, Simple Heuristics in a Social World, pp.199 224, Oxford University Press, 2012. 54 T. Hu, B.-L. Wenning, C. Görg, U. Toseef, and Z. Guo, Statistical analysis of contact patterns between human-carried mobile devices, 4th International Conference on Mobile Networks and Management MON- AMI 2012, A. Timm-Giel, J. Strassner, R. Agüero, S. Sargento, and K. Pentikousis, eds., vol.58, pp.244 280 IEICE Fundamentals Review Vol.10 No.4
257, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. 55 M. Takano and I. Fukuda, Limitations of Time Resources in Human Relationships Determine Social Structures, Palgrave Communications, 2017 (in printing). 56 R. Byrne and A. Whiten, Machiavellian Intelligence: Social Expertise and the Evolution of Intellect in Monkeys, Apes, and Humans (Oxford Science Publications), Oxford University Press, USA, 1989. 57 R. Dunbar, The social brain hypothesis, Evolutionary Anthropology, vol.6, no.5, pp.178 190, 1998. 58 pp.20 36 2010 59 T.A. Vlahovic, S.G.B. Roberts, and R.I.M. Dunbar, Media Naturalness Theory and human social bonding, Human Behavior and Evolution Society Conference, 2013. 60 M. Burke and R.E. Kraut, Growing closer on facebook: changes in tie strength through social network site use, Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI 14, pp.4187 4196, ACM Press, New York, New York, USA, 2014. 61 vol.37 no.1 pp.97 108 1997 62 vol.54 no.2 pp.101 111 2015 63 vol.81 no.2 pp.114 122 2010 64 : vol.29 no.2 pp.65 74 2013 65 S. Yardi and D. Boyd, Dynamic debates: An analysis of group polarization over time on Twitter, Bulletin of Science, Technology & Society, vol.30, no.5, pp.316 327, 2010. 66 H.H.S. Li, Z. Yang, Q. Lv, R.I.R.R. Han, and S. Mishra, A comparison of common users across instagram and ask.fm to better understand cyberbullying, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp.355 362, IEEE, 2014. 67 vol.27 no.1 pp.51 58 2012 68 D. Nettle and R. Dunbar, Social markers and the evolution of reciprocal exchange, Current Anthropology, vol.38, no.1, pp.93 99, 1997. 69 M.C. Baker, The Atoms of Language: The Mind s Hidden Rules of Gramma, Oxford University Press, 2002. 70 E. Cohen, The evolution of tag-based cooperation in humans, Current Anthropology, vol.53, no.5, pp.588 616, 2012. 71 R.L. Riolo, M.D. Cohen, and R. Axelrod, Evolution of cooperation without reciprocity, Nature, vol.414, no.6862, pp.441 443, 2001. 72 D.T. Kenrick and R.C. Keefe, Age preferences in mates reflect sex differences in human reproductive strategies, Behav. Brain Sci., vol.15, no.01, pp.75 91, 1992. 73? 2005 74 R.A. Fisher, The evolution of sexual preference, The Eugenics review, vol.7, no.3, pp.184 92, 1915. 75 G.F. Miller, A review of sexual selection and human evolution: How mate choice shaped human nature, Handbook of evolutionary psychology: Ideas, issues, and applications, C. Crawford and D. Krebs, eds., pp.87 130, ESRC Centre on Economics Learning and Social Evolution, 1998. 76 :, pp.98 130 2013 77 C. Rudder, Dataclysm : who we are when we think no one s looking, Crown, 2014. 78 C.S. Araújo, W. Meira, and V. Almeida, Identifying stereotypes in the online perception of physical attractiveness, Proceedings of The 8th International Conference on Social Informatics (SocInfo), pp.419 437, 2016. 79 2016 80 Tweet ( ) vol.29 no.2 pp.172 181 2014 81 27 2016 82 R&D vol.30 no.3 pp.318 324 2015 83 A. Gupta, P. Kumaraguru, and A. Sureka, Characterizing pedophile conversations on the Internet using online grooming, Proceedings of CoRR, 2012. 84 C.S. Crandall and M. Schaller, Social psychology of prejudice: Historical and contemporary issues, Proceedings of The 6th International Conference on Social Informatics (SocInfo), 2014. 85 F. Toriumi, T. Nakanishi, M. Tashiro, and K. Eguchi, Encounters between predators and their targets in private chat, Journal of Transformation of Human Behavior under the Influence of Infosocionomics Society, vol.1, pp.22 28, 2016. 86 The New York Times, Facebook considering ways to combat fake news, Mark Zuckerberg says,. 87 NATROM Dr. Koala 2016 88 M. Sharma K. Yadav N. Yadav, and K.C. Ferdinand, Zika virus pandemic analysis of Facebook as a social media health information platform AmericanJournal of Infection Control, 2016 89 D.J. Watts, Computational social science: Exciting progress and future directions, The Bridge, vol.43, no.4, pp.5 10, 2013. 90 L. Scissors, M. Burke, and S. Wengrovitz, What s in alike? Attitudesandbehaviorsaroundreceivinglikes on Facebook, Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW 16, pp.1499 1508, ACM Press, New York, New York, USA, 2016. 91 A. Mann, Core concepts: Computational social science, Proc. natl Acad. Sci. USA, vol.113, no.3, pp.468 70, 2016. 92 IoT vol.34 pp.47 82 2016 SITE 28 12 28 2009 ( ) IEICE Fundamentals Review Vol.10 No.4 281