The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale

In this paper, we interpret the community question answering websites on the StackExchange platform as knowledge markets, and analyze how and why these markets can fail at scale. A knowledge market framing allows site operators to reason about market failures, and to design policies to prevent them. Our goal is to provide insights on large-scale knowledge market failures through an interpretable model. We explore a set of interpretable economic production models on a large empirical dataset to analyze the dynamics of content generation in knowledge markets. Amongst these, the Cobb-Douglas model best explains empirical data and provides an intuitive explanation for content generation through concepts of elasticity and diminishing returns. Content generation depends on user participation and also on how specific types of content (e.g. answers) depends on other types (e.g. questions). We show that these factors of content generation have constant elasticity---a percentage increase in any of the inputs leads to a constant percentage increase in the output. Furthermore, markets exhibit diminishing returns---the marginal output decreases as the input is incrementally increased. Knowledge markets also vary on their returns to scale---the increase in output resulting from a proportionate increase in all inputs. Importantly, many knowledge markets exhibit diseconomies of scale---measures of market health (e.g., the percentage of questions with an accepted answer) decrease as a function of number of participants. The implications of our work are two-fold: site operators ought to design incentives as a function of system size (number of participants); the market lens should shed insight into complex dependencies amongst different content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201

Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion

Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high migration of users in and out of communities, a key challenge is to design effective strategies for recommending experts for new questions. In this paper, we propose a simple graph-diffusion expert recommendation model for CQA, that can outperform state-of-the art deep learning representatives and collaborative models. Our proposed method learns users' expertise in the context of both semantic and temporal information to capture their changing interest and activity levels with time. Experiments on five real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of ~ 30% performance gain compared to the best baseline method

Social Roles, Interactions and Community Sustainability in Social Q&A Sites: A Resource-based Perspective

Online tech support communities have become valuable channels for users to seek and provide solutions to specific problems. From the resource exchange perspective, the sustainability of a social system is contingent upon the size of its members as well as their communication activities. To further extend the resource-based model, the current research identifies a variety of social roles in a large tech support Q&A forum and examines longitudinal changes in the community’s structure based on the identification. Moreover, this study also investigates the relationship between the community’s functionality and its traffic. Results suggest that the proportion of unsolved questions negatively impacts the number of future incoming questions and the outcome of a given question is not only dependent on users’ interactions within the discussion, but also on the community activities preceding the question. These observations can help community managers to improve system design and task allocation

Beginning Teacher Support in Australia: Towards an Online Community to Augment Current Support

This paper describes opportunities to improve the current support provided to beginning teachers in Australia. It holds that there is a need for approaches that go beyond school-based induction and support. The paper presents data from a survey of beginning teachers in Queensland, with aims to determine current access to support and perceptions about gaps in support. It uses these findings alongside existing evidence to make arguments, that some beginning teachers are effectively unsupported and that universities have the potential to play a greater role in beginning teacher support. Further results are used to suggest guidelines for developing a national online community of pre-service and beginning teachers. A case study of a successful online professional support community is used as a model for how this may be implemented