000028987 001__ 28987
000028987 005__ 20130422152610.0
000028987 0247_ $$2urn$$aurn:nbn:ch:rero-006-110752
000028987 0248_ $$aoai:doc.rero.ch:20120419090729-EH$$pthesis$$prero_explore$$punisi$$pthesis_urn$$zcdu34$$zreport$$zcdu004$$zbook$$zjournal$$zcdu16$$zpostprint$$zpreprint$$zcdu1$$zdissertation
000028987 041__ $$aeng
000028987 080__ $$a004
000028987 100__ $$aCiampaglia, Giovanni Luca$$d1980-07-12
000028987 245__ $$9eng$$aUser participation and community formation in peer production systems
000028987 300__ $$a193 p
000028987 502__ $$92011-12-15$$aThèse de doctorat : Università della Svizzera italiana, 2011 ; 2011INFO010
000028987 506__ $$ffree
000028987 520__ $$9eng$$aThis thesis explores the phenomenon of user participation in peer production communities. In large-scale collaboration communities such as wikis, open source  software development teams, and file-sharing groups, members are, in general, not remunerated for contributing to the digital common, so the incentives to participate in these projects are usually construed in terms of extrinsic benefits such as building reputations, reciprocity, gratification, and providing a sense of membership to a socially cohesive group. In fact, like any  social group, a community of commons-based peer production is endowed with its own norms, beliefs, and cultural features. In these systems such features,  however, are themselves the result of a bottom-up process of cultural formation and opinion aggregation, mostly related to various aspects of the social  production of digital content. Unfortunately it is still poorly understood how this process happens -- considering that most interactions between members are  mediated by the digital artifacts encyclopedic articles, source code, etc.) that the project is producing. To this end, we studied the process of community  formation in a large peer production community by means of statistical analysis and agent-based modeling and simulation. In the first part of this thesis we  analyzed the activity of registered editors from the communities of five of the largest versions of Wikipedia, the free online encyclopedia. We found that the  distribution of the user activity lifespan is distributed according to a mixture of log-normal distributions. In order to understand these empirical patterns we  developed, in the second part of the thesis, an agent-based model of a peer production community. Social influence is modeled in terms of dyadic user-page  interactions, under the form of the bounded confidence rule. Thus a measurable aspect of user participation -- activity lifespan -- is linked to microscopic  features of the dynamics of social influence. In order to study the behavior of our model, we perform a factor screening via global sensitivity analysis. We then  calibrate our agent-based model using the empirical data from Wikipedia. To this end, an indirect inference technique is devised and tested. In light of the results  of our model, in the third -- and final -- part of the thesis we analyze a recent dataset from the English Wikipedia and perform a longitudinal study of the life cycle  of user activity. Using a non-parametric approach we study how the daily rate of editing changes during the lifespan of editors, and find a strongly  inhomogeneous temporal life cycle. This approach enables us to look at the temporal evolution of editing activity for the whole community of Wikipedia editors.  These results suggest that user participation in peer production systems can be construed primarily as a process of mediated social influence and that other  factors, most surprisingly intrinsic motivation to contribution, are less important in determining the overall activity lifespan of an individual. In conclusion this thesis  shows how social simulation can be supplemented with large-scale data analysis in order to develop an empirically grounded approach to the computational  study of collective social phenomena and, in particular, social computing platforms.
000028987 695__ $$9eng$$aWikipedia ; Wiki ; User participation ; Commons-based peer production ; Agent-based modeling ; Mixture models ; Indirect inference estimation ; Gaussian processes ; Sensitivity analysis ; Motivation ; User account lifespan ; Peers ; Computational social sciences ; Social simulation
000028987 700__ $$aGambardella, Luca$$eDir.
000028987 700__ $$aGiordano, Paolo$$eCodir.
000028987 700__ $$aVancheri, Alberto$$eCodir.
000028987 8564_ $$f2011INFO010.pdf$$qapplication/pdf$$s5867275$$uhttp://doc.rero.ch/record/28987/files/2011INFO010.pdf$$yorder:1$$zTexte intégral
000028987 918__ $$aFacoltà di scienze informatiche$$bVia Lambertenghi 10A, CH-6904 Lugano
000028987 919__ $$aUniversità della Svizzera italiana$$bLugano$$ddoc.support@rero.ch
000028987 980__ $$aTHESIS$$bUNISI$$fTH_PHD
000028987 990__ $$a20120419090729-EH