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
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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. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . The rapidly increasing popularity of Web 2.0 online communities originates in the ease of collaborative content creation and its sharing. As a result, more community members actively participate in the community and its data growth rates are continuously increasing. This poses the challenge for community platform operators on efficient administration and moderation to ensure the quality of content and to prevent violations of laws (e.g. copyright, privacy, illegal content) and community rules. Involvement of employed administrators who read and approve every piece of user-generated content is clearly the safest way of quality assurance. Since this is a time consuming task it does not scale up with Web 2.0 dimensions. So administrative functions are delegated to members of the community, the moderators. The strategy for choosing trustworthy moderators in big anonymous communities is specified in policies based on user reputation that is measured in bonus points. The proper balancing between community self-management and administration is crucial for the quality, attractiveness, and scalability of the entire community. Therefore understanding the mutual influences between community actors, reputation systems and platform policies that employ user reputation is crucial to ensure the overall quality, user acceptance, and success of the entire online community. Our objective is to predict the behavior in an online community for different policies, which may result in different overall quality of the community content. For this purpose we present our community analysis framework CoSiMo (an acronym for Community Simulation and Modeling), which employs the model-based approach for predicting the impact of policies on community dynamics and health. Through systematic variation of simulated quality assurance mechanisms we show that our model plausibly captures the influence of policies to content quality and can be therefore exploited for optimization of real online communities. Even though people in our contemporary technological society are depending on communication, our understanding of the underlying laws of human communicational behavior continues to be poorly understood. Here we investigate the communication patterns in 2 social Internet communities in search of statistical laws in human interaction activity. This research reveals that human communication networks dynamically follow scaling laws that may also explain the observed trends in economic growth. Specifically, we identify a generalized version of Gibrat's law of social activity expressed as a scaling law between the fluctuations in the number of messages sent by members and their level of activity. Gibrat's law has been essential in understanding economic growth patterns, yet without an underlying general principle for its origin. We attribute this scaling law to long-term correlation patterns in human activity, which surprisingly span from days to the entire period of the available data of more than 1 year. Further, we provide a mathematical framework that relates the generalized version of Gibrat's law to the long-term correlated dynamics, which suggests that the same underlying mechanism could be the source of Gibrat's law in economics, ranging from large firms, research and development expenditures, gross domestic product of countries, to city population growth. These findings are also of importance for designing communication networks and for the understanding of the dynamics of social systems in which communication plays a role, such as economic markets and political systems. To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users. Online communities in the form of message boards, listservs, and newsgroups continue to represent a considerable amount of the social activity on the Internet. Every year thousands of groups ourish while others decline into relative obscurity; likewise, millions of members join a new community every year, some of whom will come to manage or moderate the conversation while others simply sit by the sidelines and observe. These processes of group formation, growth, and dissolution are central in social science, and in an online venue they have ramifications for the design and development of community software In this paper we explore a large corpus of thriving online communities. These groups vary widely in size, moderation and privacy, and cover an equally diverse set of subject matter. We present a broad range of descriptive statistics of these groups. Using metadata from groups, members, and individual messages, we identify users who post and are replied-to frequently by multiple group members; we classify these high-engagement users based on the longevity of their engagements. We show that users who will go on to become long-lived, highly-engaged users experience significantly better treatment than other users from the moment they join the group, well before there is an opportunity for them to develop a long-standing relationship with members of the group We present a simple model explaining long-term heavy engagement as a combination of user-dependent and group-dependent factors. Using this model as an analytical tool, we show that properties of the user alone are sufficient to explain 95 of all memberships, but introducing a small amount of per-group information dramatically improves our ability to model users belonging to multiple groups. In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment. The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size. Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinforcement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose. Peer-to-peer file sharing systems, most notably BitTorrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that long-term relationships among BT peers could be explored for peer cooperation, so as to achieve better sharing efficiency. However, whether such long-term relationships exist remain unknown. From an 80-day trace of 100,000 real world swarms, we find that less than 5 peers can meet each other again throughout the whole period, which largely invalidates the fundamental assumption of these peer cooperation protocols. Yet the recent emergence of online social network applications sheds new light on this problem. In particular, a number of BT swarms are now triggered by Twitter, reflecting a new trend for initializing sharing among communities. In this paper, we for the first time examine the challenges and potentials of accelerating peer-to-peer file sharing with Twitter social networks. We show that the peers in such swarms have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Based on the Hadamard Transform of peers' online behaviors, we develop a social index to quickly locate peers of common patterns. We further demonstrate a practical cooperation protocol that identifies and utilizes the social relations with the index. Our PlanetLab experiments indicate that the incorporation of social relations remarkably accelerates the downloading time. The improvement remains noticeable even in a hybrid system with a small set of socially active peers only. As the popularity of the web increases, particularly the use of social networking sites and style sharing platforms, users are becoming increasingly connected, sharing more and more information, resources, and opinions. This vast array of information presents unique opportunities to harvest knowledge about user activities and interests through the exploitation of large-scale, complex systems. Communal tagging sites, and their respective folksonomies, are one example of such a complex system, providing huge amounts of information about users, spanning multiple domains of interest. However, the current Web infrastructure provides no mechanism for users to consolidate and exploit this information since it is spread over many desperate and unconnected resources. In this paper we compare user tag-clouds from multiple folksonomies to: (a) show how they tend to overlap, regardless of the focus of the folksonomy (b) demonstrate how this comparison helps finding and aligning the user's separate identities, and (c) show that cross-linking distributed user tag-clouds enriches users profiles. During this process, we find that significant user interests are often reflected in multiple Web2.0 profiles, even though they may operate over different domains. However, due to the free-form nature of tagging, some correlations are lost, a problem we address through the implementation and evaluation of a user tag filtering architecture. Although analyzing user behavior within individual communities is an active and rich research domain, people usually interact with multiple communities both on- and off-line. How do users act in such multi-community environments? Although there are a host of intriguing aspects to this question, it has received much less attention in the research community in comparison to the intra-community case. In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments. We also demonstrate the effectiveness of features drawn from these aspects in predicting users' future level of activity. One might expect that a user's trajectory mimics the "settling-down" process in real life: an initial exploration of sub-communities before settling down into a few niches. However, we find that the users in our data continually post in new communities; moreover, as time goes on, they post increasingly evenly among a more diverse set of smaller communities. Interestingly, it seems that users that eventually leave the community are "destined" to do so from the very beginning, in the sense of showing significantly different "wandering" patterns very early on in their trajectories; this finding has potentially important design implications for community maintainers. Our multi-community perspective also allows us to investigate the "situation vs. personality" debate from language usage across different communities. It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23 and the total duration improved by 10 , compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month. It has long been realized that social network of scientific collaborations provides a window on patterns of collaboration within the academic community. Investigations and studies about static and dynamic properties of co-authorship network have also been done in the recent years. However, the accent of most of the research is on the analysis about the macroscopic structure of the whole network or community over time such as distance, diameter shrinking and densication phenomenon and microscopic formation analysis of links,groups or communities over time. But in fact, how an individual or a community grows over time may not only provide a new view point to mine copious and valuable information about scientific networks but also reveal important factors that influence the growth process. In this paper,from a temporal and microscopic analytical perspective, we propose a method to trace scientific individual's and community's growth process based on community's evolution path combination with quantifiable measurements. During the process of tracing, we find out that it is the fact that the lifespan of community is related to the ability of altering its membership, but what's more and complementary, we find out that the lifespan of community is also related to the ability of maintaining its core members meaning that community may last for a longer lifespan if its core members are much more stable. Meanwhile, we also trace the growth process of research individuals based on the evolution of communities. Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts, and pop culture trends. This leads to the fact that a user's historical ratings comprise many aspects of user interests spanning a long time period. However, at a certain time slice, one user's interest may only focus on one or a couple of aspects. Thus, CF techniques based on the entire historical ratings may recommend inappropriate items. In this paper, we consider modeling user-interest drift over time based on the assumption that each user has multiple counterparts over temporal domains and successive counterparts are closely related. We adopt the cross-domain CF framework to share the static group-level rating matrix across temporal domains, and let user-interest distribution over item groups drift slightly between successive temporal domains. The derived method is based on a Bayesian latent factor model which can be inferred using Gibbs sampling. Our experimental results show that our method can achieve state-of-the-art recommendation performance as well as explicitly track and visualize user-interest drift over time. As crowd-sourced curation of news and information become the norm, it is important to understand not only how individuals consume information through social news Web sites, but also how they contribute to their ranking systems. In this paper, we introduce and make available a new data set containing the activity logs that recorded all activity for 309 Reddit users for one year. Using this newly collected data, we present findings that highlight the browsing and voting behavior of the study’s participants. We find that most users do not read the article that they vote on, and that, in total, 73 of posts were rated (i.e., upvoted or downvoted) without first viewing the content. We also show the evidence of cognitive fatigue in the browsing sessions of users that are most likely to vote. Online spaces that enable shared public interpersonal communications are of significant social, organizational, and economic importance. In this paper, a theoretical model and associated unobtrusive method are proposed for researching the relationship between online spaces and the behavior they host. The model focuses on the collective impact that individual information-overload coping strategies have on the dynamics of open, interactive public online group discourse. Empirical research was undertaken to assess the validity of both the method and the model, based on the analysis of over 2.65 million postings to 600 Usenet newsgroups over a 6-month period. Our findings support the assertion that individual strategies for coping with "information overload" have an observable impact on large-scale online group discourse. Evidence was found for the hypotheses that: (1) users are more likely to respond to simpler messages in overloaded mass interaction; (2) users are more likely to end active participation as the overloading of mass interaction increases; and (3) users are more likely to generate simpler responses as the overloading of mass interaction grows.The theoretical model outlined offers insight into aspects of computer-mediated communication tool usability, technology design, and provides a road map for future empirical research.