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Course Description:
The course discusses theories, methods and algorithms used to understand and shape online crowd behavior and the production of digital goods and services by crowds. Examples come from collaborative authoring systems, social media, online social networks and health applications. The goal of this course is to enable students to competently analyze and understand crowd behavior and –production in online environments.
Learning Goals:
Students taking this course will be able to:
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Understand online crowd behavior and –production research and the literature it generates
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Analyze online crowd behavior and –production in specific application domains, with a focus on social media and health related applications.
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Discuss selected instruments and methods to shape online crowd behavior and –production
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Know how to evaluate crowd-based production systems
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Understand relationships between online crowd behavior and the artifacts it generates
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Appreciate the social-computational complexity of online crowd behavior and –production
Preliminary Week by Week Outline
Week 1: Motivation and introduction
A general introduction and motivation of the topics of this seminar will take place.
In Week 1, students will pick the topics (from subseqent weeks) that they will present, discuss and research on during the course of the seminar.
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We will discuss Examples: http://en.wikipedia.org/wiki/Medpedia
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The Social Determinants of Health, WHO report
http://www.euro.who.int/__data/assets/pdf_file/0005/98438/e81384.pdf
BREAK: A couple of weeks break for students to work on their topics.
Week 2: Encouraging contributions from online crowds
We will discuss how contributions from online crowds can be encouraged and shaped.
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A. Cabrera, E.F. Cabrera. Knowledge-sharing Dilemmas. Organization Studies 23 (5) (2002) 687–710.
http://e-archivo.uc3m.es:8080/bitstream/10016/7143/2/Knowledge_cabrera_OS_2002_ps.pdf
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Kraut, R. E. & Resnick, P. Chapter 2: Encouraging contributions to online communities, In Kraut, R. E. & Resnick, P. (Under contract). Building Successful Online Communities: Evidence-based social design. Cambridge, MA: MIT Press.
http://www.cs.cmu.edu/~kraut/RKraut.site.files/articles/Kraut10-Contribution-current.pdf
Week 3: Online crowd formation and consensus engineering
In this week, we will discuss the ways in which crowds form and how consensus emerges.
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Q. Lu, G. Korniss, and B.K. Szymanski, The Naming Game on Social Networks: Community Formation and Consensus Engineering, Journal of Economic Interaction and Coordination, vol. 4(2), 2009
http://homepages.rpi.edu/~korniss/Research/JEIC/Lu_NG.pdf
Week 4: Models of influence
In this week, we will discuss different models of influence in online crowds, and the reflection problem.
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David Easley and Jon Kleinberg, Chapter 16. Information Cascades, In Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press (2010)
http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book-ch16.pdf
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David Easley and Jon Kleinberg, Chapter 19. Cascading Behavior in Networks, In Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press (2010)
http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book-ch19.pdf
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Charles F Manski, Identification of Endogenous Social Effects: The Reflection Problem, The Review of Economic Studies, Volume: 60, Issue: 3, (1993) Pages: 531-542
http://fisher.osu.edu/~schroeder_9/AMIS900/Manski1993.pdf
Week 5: Conflict and coordination
In this week, we will discuss mechanisms of conflict and coordination in collaborative authoring systems such as wikis.
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A. Kittur, R.E. Kraut, Beyond Wikipedia: Conflict and coordination in online production groups. CSCW 2010: Proceedings of the ACM Conference on Computer-Supported Cooperative Work. New York: ACM Press (2010)
https://research.microsoft.com/en-us/um/redmond/groups/connect/cscw_10/docs/p215.pdf
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Aniket Kittur, Bongwon Suh, Bryan A Pendleton, Ed H Chi, He Says, She Says: Conflict and Coordination in Wikipedia, Proceedings of the SIGCHI conference on Human factors in computing systems CHI 07 (2007)
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.81.5406&rep=rep1&type=pdf
Week 6: Identifying expertise & experts
The identification of experts and expertise in online crowds will be discussed in this week.
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M. G. Noll, C.-M. Au Yeung, N. Gibbins, C. Meinel, N. Shadbolt, Telling Experts from Spammers: Expertise Ranking in Folksonomies, Proceedings of 32nd ACM SIGIR Conference, Boston, USA, July 2009, pp. 612-619
http://eprints.ecs.soton.ac.uk/17327/1/sigir09-tagexpert.pdf
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E. Smirnova and K. Balog. A User-oriented Model for Expert Finding, In: 33rd European Conference on Information Retrieval (ECIR 2011), LNCS 6611, pages 580-592, April 2011.
http://krisztianbalog.com/files/ecir2011-ues.pdf
Week 7: Voting mechanisms and manipulation
In this week we will discuss mechanisms of manipulation of crowds, and how to tackle them.
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Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, and Qiaozhu Mei, Rumor has it: Identifying Misinformation in Microblogs, Empirical Methods on Natural Language Processing (EMNLP 2011).
http://aclweb.org/anthology-new/D/D11/D11-1147.pdf
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Nguyen Tran, Bonan Min, Jinyang Li, Lakshminarayanan Subramanian, Sybil-Resilient Online Content Voting, In Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation (NSDI), Boston, April 2009.
http://www.news.cs.nyu.edu/~trandinh/publications/SumUp.pdf
Week 8: Crowdsourcing and microwork
In this week, we will discuss microworking platforms such as Mechanical Turk.
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G. Paolacci, G. Chandler and P.G. Ipeirotis. Running Experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5, (2010) 411-419.
http://journal.sjdm.org/10/10630a/jdm10630a.pdf
Week 9:Analyzing epidemics in crowd-generated data
In this week, we will discuss epidemic models and how crowd-generated data can be utilized for identifying epidemics.
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David Easley and Jon Kleinberg, Chapter 21. Epidemics, In Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press (2010)
http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book-ch21.pdf
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Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski & Larry Brilliant, Detecting influenza epidemics using search engine query data, Nature 457, 1012-1014(19 February 2009)
http://research.google.com/pubs/archive/34503.pdf
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Philip Munz, Ioan Hudea, Joe Imad and Robert J, Smith, When Zombies Attack!: Mathematical Modelling of an Outbreak of Zombie Infection, In “Infectious Disease Modelling Research Progress,” eds. J.M. Tchuenche and C. Chiyaka, Nova Science Publishers, Inc. pp. 133-150, 2009.
http://mysite.science.uottawa.ca/rsmith43/Zombies.pdf
Week 10: Crowd Production of Medical Knowledge
In this week, we will discuss an ongoing effort to crowdsource a large medical taxonomy.
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T. Tudorache, S. Falconer, N.F. Noy, C. Nyulas, T.B. Ustun, M.-A. Storey, M.A. Musen, Ontology Development for the Masses: Creating ICD-11 in WebProtege, In EKAW 2010 - Knowledge Engineering and Knowledge Management by the Masses, Lisbon, Portugal, 2010.
http://bmir.stanford.edu/publications/view.php/ontology_development_for_the_masses_creating_icd_11_in_webprotege
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Sean M. Falconer, Tania Tudorache, Natalya Fridman Noy: An analysis of collaborative patterns in large-scale ontology development projects. K-CAP 2011: 25-32
http://bmir.stanford.edu/file_asset/index.php/1693/BMIR-2011-1456.pdf
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