Now showing 1 - 3 of 3
  • Publication
    Exploring the Relationship between Membership Turnover and Productivity in Online Communities
    (Association for the Advancement of Artificial Intelligence, 2014-06-04) ; ;
    One of the more disruptive reforms associated with the modern Internet is the emergence of online communities working together on knowledge artefacts such as Wikipedia and OpenStreetMap. Recently it has become clear that these initiatives are vulnerable because of problems with membership turnover. This study presents a longitudinal analysis of 891 Wiki Projects where we model the impact of member turnover and social capital losses on project productivity. By examining social capital losses we attempt to provide a more nuanced analysis of member turnover. In this context social capital is modelled from a social network perspective where the loss of more central members has more impact. We find that only a small proportion of Wiki Projects are in a relatively healthy state with low levels of membership turnover and social capital losses.The results show that the relationship between social capital losses and project performance is U-shaped, and that member withdrawal has significant negative effect on project outcomes. The results also support the mediation of turnover rate and network density on the curvilinear relationship.
      161
  • Publication
    The influence of network structures of Wikipedia discussion pages on the efficiency of WikiProjects
    The proliferation of online communities has attracted much attention to modelling user behaviour in terms of social interaction, language adoption and contribution activity. Nevertheless, when applied to large-scale and cross-platform behavioural data, existing approaches generally suffer from expressiveness, scalability and generality issues. This paper proposes trans-dimensional von Mises-Fisher (TvMF) mixture models for L2 normalised behavioural data, which encapsulate: (1) a Bayesian framework for vMF mixtures that enables prior knowledge and information sharing among clusters, (2) an extended version of reversible jump MCMC algorithm that allows adaptive changes in the number of clusters for vMF mixtures when the model parameters are updated, and (3) an online TvMF mixture model that accommodates the dynamics of clusters for time-varying user behavioural data. We develop efficient collapsed Gibbs sampling techniques for posterior inference, which facilitates parallelism for parameter updates. Empirical results on simulated and real-world data show that the proposed TvMF mixture models can discover more interpretable and intuitive clusters than other widely-used models, such as k-means, non-negative matrix factorization (NMF), Dirichlet process Gaussian mixture models (DP-GMM), and dynamic topic models (DTM). We further evaluate the performance of proposed models in real-world applications, such as the churn prediction task, that shows the usefulness of the features generated.
    Scopus© Citations 14  583
  • Publication
    Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles
    (Journal of Machine Learning Research, 2016) ; ;
    The proliferation of online communities has attracted much attention to modelling user behaviour in terms of social interaction, language adoption and contribution activity. Nevertheless, when applied to large-scale and cross-platform behavioural data, existing approaches generally suffer from expressiveness, scalability and generality issues. This paper proposes trans-dimensional von Mises-Fisher (TvMF) mixture models for L2 normalised behavioural data, which encapsulate: (1)a Bayesian framework for vMF mixtures that enables prior knowledge and information sharing among clusters, (2) an extended version of reversible jump MCMC algorithm that allows adaptivechanges in the number of clusters for vMF mixtures when the model parameters are updated, and (3)an online TvMF mixture model that accommodates the dynamics of clusters for time-varying user behavioural data. We develop efficient collapsed Gibbs sampling techniques for posterior inference,which facilitates parallelism for parameter updates. Empirical results on simulated and real-world data show that the proposed TvMF mixture models can discover more interpretable and intuitive clusters than other widely-used models, such as k-means, non-negative matrix factorization (NMF), Dirichlet process Gaussian mixture models (DP-GMM), and dynamic topic models (DTM). Wefurther evaluate the performance of proposed models in real-world applications, such as the churn prediction task, that shows the usefulness of the features generated.
      372