Commons-based peer production does seem to rest upon a paradox. Although users produce all contents, at the same time participation is commonly on a voluntary basis, and largely incentivized by achievement of project's goals. This means that users have to coordinate their actions and goals, in order to keep themselves from leaving. While this situation is easily explainable for small groups of highly committed, like-minded individuals, little is known about large-scale, heterogeneous projects, such as Wikipedia. In this contribution we present a model of peer production in a large online community. The model features a dynamic population of bounded confidence users, and an endogenous process of user departure. Using global sensitivity analysis, we identify the most important parameters affecting the lifespan of user participation. We find that the model presents two distinct regimes, and that the shift between them is governed by the bounded confidence parameter. For low values of this parameter, users depart almost immediately. For high values, however, the model produces a bimodal distribution of user lifespan. These results suggest that user participation to online communities could be explained in terms of group consensus, and provide a novel connection between models of opinion dynamics and commons-based peer production.
The subject of user participation in mass collaboration systems has been already touched by several authors, for example on social networking sites @cite_15 , and knowledge sharing platforms @cite_6 . A momentum'' law has been proposed for the distribution of user life edits of inactive users @cite_1 . The distribution of user account lifespans has been shown to decay with a heavy tail, and a power-law model has been proposed after this observation @cite_18 . Empirical data from Wikipedia, however, seem to support a super-position of different regimes @cite_13 ; a feature of the model we present here is indeed a bimodal distribution of user lifespans. In the context of wikis and other free open source initiatives some authors have used survival analysis to outline the diffences between different communities, @cite_7 but this modeling technique is not suited to understand the connection between social influence, group coordination, and user retention. We advocate the need to explicitly model such processes explicitly.
@cite_18 @cite_7 @cite_1 @cite_6 @cite_15 @cite_13
2116338588 1510898630 2164227199 1786164014
We study the distribution of the activity period of users in five of the largest localized versions of the free, on- line encyclopedia Wikipedia. We find it to be consis- tent with a mixture of two truncated log-normal distri- butions. Using this model, the temporal evolution of these systems can be analyzed, showing that the statis- tical description is consistent over time. contributions: 1. We find all datasets to be consistent with the hypothesis that the lifetime of an user account is described by the su- perposition of two truncated log-normal distributions. An interpretation for this phenomenon is that two different regimes govern the participation of individuals to these versions of the Wikipedia project: the occasional users, who fail to find interest in the project after the first few at- tempts to contribute, and the long-term users, whose with- drawal is probably more related to external factors like the loss of personal incentives in contributing and similar causes. 2. Using our model, we characterize how the participation of users over time evolves, as the system ages. We find that the statistical description of the one-timers is stable over time, while the properties of the group of long-term users change as a consequence of the aging of the system. 3. We find evidence that the inter-edit time distribution de- cays with an heavy tail. In view of this finding, we check that our analysis is not affected by the choice of the pa- rameter used for determining when an user is to be con- sidered "inactive"; we find that for the one-timers it has no quantitative effect. For the statistics of the long-lived users we find instead a very good qualitative agreement. Retaining participation is crucial for information services, online knowledge sharing services among them. We present the first comprehensive analysis of users’ activity lifespan across three predominant online knowledge-sharing communities. Extending previous work focusing on initial interactions of new users, we use survival analysis to quantify participation patterns that can be used to predict individual lifespan over the long term. We discuss how cross-site differences in user participation and the underlying factors can be related to differences in system design and culture. We conduct a longitudinal comparison of the communities’ evolvement between two distinct stages, the initial days just after the site launch and one year later. We also observe that sub-communities corresponding to different topics differ in their ability to sustain users. All results reveal the complexity and diversity in users’ engagement to a site and design implications are discussed. Many people share their activities with others through online communities. These shared activities have an impact on other users' activities. For example, users are likely to become interested in items that are adopted (e.g. liked, bought and shared) by their friends. In this paper, we propose a probabilistic model for discovering latent influence from sequences of item adoption events. An inhomogeneous Poisson process is used for modeling a sequence, in which adoption by a user triggers the subsequent adoption of the same item by other users. For modeling adoption of multiple items, we employ multiple inhomogeneous Poisson processes, which share parameters, such as influence for each user and relations between users. The proposed model can be used for finding influential users, discovering relations between users and predicting item popularity in the future. We present an efficient Bayesian inference procedure of the proposed model based on the stochastic EM algorithm. The effectiveness of the proposed model is demonstrated by using real data sets in a social bookmark sharing service. In crowdsourcing systems, the interests of contributing participants and system stakeholders are often not fully aligned. Participants seek to learn, be entertained, and perform easy tasks, which offer them instant gratification; system stakeholders want users to complete more difficult tasks, which bring higher value to the crowdsourced application. We directly address this problem by presenting techniques that optimize the crowdsourcing process by jointly maximizing the user longevity in the system and the true value that the system derives from user participation. We first present models that predict the "survival probability" of a user at any given moment, that is, the probability that a user will proceed to the next task offered by the system. We then leverage this survival model to dynamically decide what task to assign and what motivating goals to present to the user. This allows us to jointly optimize for the short term (getting difficult tasks done) and for the long term (keeping users engaged for longer periods of time). We show that dynamically assigning tasks significantly increases the value of a crowdsourcing system. In an extensive empirical evaluation, we observed that our task allocation strategy increases the amount of information collected by up to 117.8 . We also explore the utility of motivating users with goals. We demonstrate that setting specific, static goals can be highly detrimental to the long-term user participation, as the completion of a goal (e.g., earning a badge) is also a common drop-off point for many users. We show that setting the goals dynamically, in conjunction with judicious allocation of tasks, increases the amount of information collected by the crowdsourcing system by up to 249 , compared to the existing baselines that use fixed objectives.