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0.introduction to social computing
Social computing is an interdisciplinary field that studies how technology can facilitate social interaction and communication. It has three main goals: 1) Develop better social software to facilitate online interaction; 2) Computerize aspects of human society through modeling and simulation; 3) Forecast how changing technologies may impact social behavior. Social computing draws from fields like sociology, computer science, and applied physics. It covers topics such as social networks, recommendation systems, virtual worlds, and using technology to study social dynamics. Methods include social network analysis, simulation, and data mining of social media. Learning social computing would help information scientists develop technologies that consider social factors and behaviors.
0.introduction to social computing
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- 2. [ Q ] Whatis Social computing?
- 4. source Definition Communications ofthe ACM Describing any type of computing application in which software serves as an intermediary or a focus for a social relation Wikipedia (http:// en.wikipedia.org/wiki/ Social_computing) Referring to the use of social software, a growing trend in ICT usage of tools that support social inter- action and communication Forrester Research A social structure in which technology puts power in individuals and communities, not institutions Wang et al. (2007)’s definition Computational facilitation of social studies and human social dynamics as well as the design and use of ICTs that consider social context
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- 6. Goals •Develop better socialsoftwares to facilitate interaction and communication among groups of people •Computerize aspects of human society •Forecast the effects of changing technologies and policies on social and cultural behavior. * Wang, F.Y., Zeng, D., Carley, K.M., & Mao, W. (2007). Social computing: From social informatics to social intelligence. IEEE Intelligent Systems, 22(2), 79-83.
- 7. •Social network sites:Facebook •Recommendation •Social network analysis •Community •Open sensing and privacy •Ecology •Trend analysis •Simulation •Virtual worlds as a social space •Opinion sentimental analysis •Social visualization Scope
- 8. •Social recommendation •ICTs e.g.,e-mail, Skype, handout, Line •Social network sites: Facebook •Lifelogging & privacy •Virtual worlds as a social space Topics
- 9. Methods • Social networkanalysis • Community • Trend analysis • Social Simulation • Ecological Analysis • Opinion sentimental analysis • SocialVisualization
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- 13. Sociology - Social networktheories Applied Physics - Small World Applied Physics - Chaos, complexity Sociology - Simulation
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- 17. Social Network Sites Socialcomputing> Social Network Sites
- 18. •Networked •Acquaintance-based hard to socialnetworking •Clustered •ego-centric •Asynchronous multiplay Social computing> Social Network Sites
- 19. •From surfer tohouseholder (geo-center) •Communities based on social network Ptolemy Model
- 20. Virtual worlds asartificial societies
- 22. 1 - 3030 - 60 60 - 120 120 - 240 240 - 4800 0 1 - 30 30 - 45 45 - 60 60 AverageLevelsofGuildMembers Number of Guild Members 4 20 48 2.5 5 6 11.5 3 0.5 0.010.51 2.5 42 2 8 1 - 30 30 - 60 60 - 120 120 - 240 240 - 4800 0 1 - 30 30 - 45 45 - 60 60 AverageLevelsofGuildMembers Number of Guild Members
- 23. 1 - 3030 - 60 60 - 120 120 - 240 240 - 4800 0 1 - 30 30 - 45 45 - 60 60 AverageLevelsofGuildMembers Number of Guild Members LV6/32 1 - 30 30 - 60 60 - 120 120 - 240 240 - 4800 0 1 - 30 30 - 45 45 - 60 60
- 24. New Guild 1 -30 30 - 60 60 - 120 120 - 240 240 - 480 0 0 1 - 10 10 - 20 40 - 50 New Guild Small Guild Newbie Guild Elite Guild Unstable Guild AverageLevelsofGuildMembers Number of Guild Members Dismissing Advancing 20 - 30 30 - 40 50 - 60 60 - 70 Big Guild 1 - 30 30 - 60 60 - 120 120 - 240 240 - 480 0 0 1 - 10 10 - 20 40 - 50 Newbie Guild AverageLevelsofGuildMembers Number of Guild Members 20 - 30 30 - 40 50 - 60 60 - 70 Following New Guild Small Guild Big Guild Elite Guild Unstable Guild
- 25. 1 - 3030 - 60 60 - 120 120 - 240 240 - 480 0 0 1 - 10 10 - 20 40 - 50 Newbie Guild AverageLevelsofGuildMembers Number of Guild Members 20 - 30 30 - 40 50 - 60 60 - 70 Following Dismissing Advancing New Guild Small Guild Big Guild Elite Guild Unstable Guild 1 - 30 30 - 60 60 - 120 120 - 240 240 - 480 0 0 1 - 10 10 - 20 40 - 50 New Guild Small Guild Big Guild Newbie Guild Elite Guild Unstable Guild AverageLevelsofGuildMembers Number of Guild Members Quit Guild Play but Stay in Game 20 - 30 30 - 40 50 - 60 60 - 70
- 27. Characters Behaviors Characters Behaviors Characters Behaviors Characters Behaviors Characters Behaviors Network self Connected, Intermediated Realself Virtual self Intermediated, represented
- 28. Modeling & Simulation Socialcomputing> Simulation
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- 30. The Gale-Shapley algorithm forstable matching • • • Lloyd Shapley Alvin Roth Social computing> Simulation
- 31. • Roth,A.E. &Shapley, L.S. (2012). Stable allocation and the practice of market design. Nobel Price Scientific Background. http://www.nobelprize.org/nobel_prizes/economics/laureates/2012/advanced- economicsciences2012.pdf • Hills,T. & Todd, P. (2008). Population Heterogeneity and Individual Differences in an Assortative Agent-Based Marriage and Divorce Model (MADAM) Using Search with Relaxing Expectations. Journal of Artificial Societies and Social Simulation 11(4)5 <http://jasss.soc.surrey.ac.uk/11/4/5.html>. • Novikoff,T. P., Kleinberg, J. M., & Strogatz, S. H. (2012). Education of a model student. Proceedings of the National Academy of Sciences, 109(6), 1868-1873. Social computing> Simulation
- 32. 42/50 (1)(2) (3) (4)(5) (1) (2) (3) (4) (5) Social computing> Simulation
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- 34. Stroud, P., DelValle, S., Sydoriak, S., Riese, J., & Mniszewski, S. (2007). Spatial dynamics of pandemic influenza in a massive artificial society. Journal of Artificial Societies and Social Simulation, 10(4), 9. Social computing> Simulation
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- 39. 0 50 100 150 200 2 3 45 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950 0 50 100 150 200 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950 PredictedTraining
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- 41. Christine, R. (2009)Tracking life in graphic detail.The Wall Street Journal, http://online.wsj.com/article/SB124537742433730231.html
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- 46. Works •Leading 1 paperdiscussion •Taking course notes for 10 weeks •2 take-home assignments for social network analysis •Final Projects
- 47. Grading Policy •1 presentation20% •1 final projects 45% •2 social network analysis assignments 20% •3 notes 15%
- 48. No response, Noinsight
- 49. [Q] Why information scientists shouldlearn social computing?