Social Informatics: SocInfo 2013 International Workshops, by Akiyo Nadamoto, Adam Jatowt, Adam Wierzbicki, Jochen L.

By Akiyo Nadamoto, Adam Jatowt, Adam Wierzbicki, Jochen L. Leidner

This publication constitutes the refereed post-proceedings of 2 workshops held on the fifth overseas convention on Social Informatics, SocInfo 2013, in Kyoto, Japan, in November 2013: the 1st Workshop on caliber, Motivation and Coordination of Open Collaboration, QMC 2013 and the 1st foreign Workshop on Histoinformatics, HISTOINFORMATICS 2013. The eleven revised papers awarded on the workshops have been conscientiously reviewed and chosen from a number of submissions. They conceal particular components of social informatics. The QMC 2013 workshop attracted papers on new algorithms and strategies to enhance the standard or to extend the incentive of open collaboration, to lessen the price of monetary motivation or to diminish the time had to end collaborative projects. The papers provided at HISTOINFORMATICS 2013 goal at enhancing the interplay among desktop technological know-how and ancient technology in the direction of fostering a brand new learn course of computational history.

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Additional info for Social Informatics: SocInfo 2013 International Workshops, QMC and HISTOINFORMATICS, Kyoto, Japan, November 25, 2013, Revised Selected Papers

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1 Predictive Power of Various Groups of Attributes Next we proceed to assessing predictive power of various types of variables in order to determine which factors are most useful in predicting team qaulity. To this end we have build predictive models on four subsets of variables and compared their performance. We began by using the AdaBoost model here as, in the previous experiment, it offered the best peformance. 3). All models reported in this section included the behavioural attributes being the basic descriptors of features from which other attributes are derived.

Another approach, which we are going to use in this paper, are partial regression methods [7]. The idea is to build regression models which predict all variables in the dataset based on the confounding ones. The confounding variables are then removed and all remaining variables are replaced with residuals from regressions on the confounders. g. [14]) with the predictor variables (confounders in our case). As a result, after the transformation, the resulting variables are uncorrelated with the ones whose influence we are trying to eliminate.

6. Histograms and ROC curves for variables describing the discussion dimension of the Multidimensional Social Network. Note that the x axis of the histograms represents the residuals from projecting the plotted variables onto the removed variables. It can be seen that the discussion dimension is very important. The average (thus corrected for the team size) number of discussions is highly, positively, correlated with article quality. 46) thus it may be difficult to isolate the influence of specific, networkrelated, aspects of the discussion dimension.

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