Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions even when using the most advanced computing technologies available. Human computation for citizen science projects consists in designing systems that allow large crowds of volunteers to contribute to scientific research by executing human computation tasks. Examples of successful projects are Galaxy Zoo and FoldIt. A key feature of this kind of project is its capacity to engage volunteers. An important requirement for the proposal and evaluation of new engagement strategies is having a clear understanding of the typical engagement of the volunteers; however, even though several projects of this kind have already been completed, little is known about this issue. In this paper, we investigate the engagement pattern of the volunteers in their interactions in human computation for citizen science projects, how they differ among themselves in terms of engagement, and how those volunteer engagement features should be taken into account for establishing the engagement encouragement strategies that should be brought into play in a given project. To this end, we define four quantitative engagement metrics to measure different aspects of volunteer engagement, and use data mining algorithms to identify the different volunteer profiles in terms of the engagement metrics. Our study is based on data collected from two projects: Galaxy Zoo and The Milky Way Project. The results show that the volunteers in such projects can be grouped into five distinct engagement profiles that we label as follows: hardworking, spasmodic, persistent, lasting, and moderate. The analysis of these profiles provides a deeper understanding of the nature of volunteers' engagement in human computation for citizen science projects.
In most online citizen science projects, a large proportion of participants contribute in small quantities. To investigate how low contributors differ from committed volunteers, we distributed a survey to members of the Old Weather project, followed by interviews with respondents selected according to a range of contribution levels. The studies reveal a complex relationship between motivations and contribution. Whilst high contributors were deeply engaged by social or competitive features, low contributors described a solitary experience of 'dabbling' in projects for short periods. Since the majority of participants exhibit this small-scale contribution pattern, there is great potential value in designing interfaces to tempt lone workers to complete 'just another page', or to lure early drop-outs back into participation. This includes breaking the work into components which can be tackled without a major commitment of time and effort, and providing feedback on the quality and value of these contributions. We present studies of the attention and time, or engagement, invested by crowd workers on tasks. Consideration of worker engagement is especially important in volunteer settings such as online citizen science. Using data from Galaxy Zoo, a prominent citizen science project, we design and construct statistical models that provide predictions about the forthcoming engagement of volunteers. We characterize the accuracy of predictions with respect to different sets of features that describe user behavior and study the sensitivity of predictions to variations in the amount of data and retraining. We design our model for guiding system actions in real-time settings, and discuss the prospect for harnessing predictive models of engagement to enhance user attention and effort on volunteer tasks.     Wikipedia is often considered as an example of ‘collaborative knowledge’. Researchers have contested the value of Wikipedia content on various accounts. Some have disputed the ability of anonymous amateurs to produce quality information, while others have contested Wikipedia’s claim to accuracy and neutrality. Even if these concerns about Wikipedia as an encyclopaedic genre are relevant, they misguidedly focus on human agents only. Wikipedia’s advance is not only enabled by its human resources, but is equally defined by the technological tools and managerial dynamics that structure and maintain its content. This article analyses the sociotechnical system — the intricate collaboration between human users and automated content agents — that defines Wikipedia as a knowledge instrument. The quality of Wikipedia articles is debatable. On the one hand, existing research indicates that not only are people willing to contribute articles but the quality of these articles is close to that found in conventional encyclopedias. On the other hand, the public has never stopped criticizing the quality of Wikipedia articles, and critics never have trouble finding low-quality Wikipedia articles. Why do Wikipedia articles vary widely in qualityq We investigate the relationship between collaboration and Wikipedia article quality. We show that the quality of Wikipedia articles is not only dependent on the different types of contributors but also on how they collaborate. Based on an empirical study, we classify contributors based on their roles in editing individual Wikipedia articles. We identify various patterns of collaboration based on the provenance or, more specifically, who does what to Wikipedia articles. Our research helps identify collaboration patterns that are preferable or detrimental for article quality, thus providing insights for designing tools and mechanisms to improve the quality of Wikipedia articles. Reliance on volunteer participation for citizen science has become extremely popular. Cutting across disciplines, locations, and participation practices, hundreds of thousands of volunteers throughout the world are helping scientists accomplish tasks they could not otherwise perform. Although existing projects have demonstrated the value of involving volunteers in data collection, relatively few projects have been successful in maintaining volunteers’ continued involvement over long periods of time. Therefore, it is important to understand the temporal nature of volunteers’ motivations and their effect on participation practices, so that effective partnerships between volunteers and scientists can be established. This paper presents case studies of longitudinal participation practices in citizen science in three countries—the United States, India, and Costa Rica. The findings reveal a temporal process of participation, in which initial participation stems in most cases from self-directed motivations, such as personal interest. In contrast, long-term participation is more complex and includes both self-directed motivations and collaborative motivations.  The online encyclopedia Wikipedia is a highly successful “open content” project, written and maintained completely by volunteers. Little is known, however, about the motivation of these volunteers. Results from an online survey among 106 contributors to the German Wikipedia project are presented. Both motives derived from social sciences (perceived benefits, identification with Wikipedia, etc.) as well as perceived task characteristics (autonomy, skill variety, etc.) were assessed as potential predictors of contributors' satisfaction and self-reported engagement. Satisfaction ratings were particularly determined by perceived benefits, identification with the Wikipedia community, and task characteristics. Engagement was particularly determined by high tolerance for opportunity costs and by task characteristics, the latter effect being partially mediated by intrinsic motivation. Relevant task characteristics for contributors' engagement and satisfaction were perceived autonomy, task significance, skill vari...     