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

Predicting Answering Behaviour in Online Question Answering Communities

The value of Question Answering (Q&A) communities is de- pendent on members of the community finding the questions they are most willing and able to answer. This can be diffi- cult in communities with a high volume of questions. Much previous has work attempted to address this problem by recommending questions similar to those already answered. However, this approach disregards the question selection behaviour of the answers and how it is affected by factors such as question recency and reputation. In this paper, we identify the parameters that correlate with such a behaviour by analysing the users’ answering patterns in a Q&A com- munity. We then generate a model to predict which question a user is most likely to answer next. We train Learning to Rank (LTR) models to predict question selections using various user, question and thread feature sets. We show that answering behaviour can be predicted with a high level of success, and highlight the particular features that influence users’ question selections

FACTORS INFLUENCING USER’S CONTINUANCE INTENTION ON PAID QUESTION AND ANSWER SERVICE ----A STUDY ON WEIBO IN CHINA

This thesis addresses the research question “Why do users continue to use paid Q&A in China” by means showed below: First, this research introduces research background of paid Q&A in China and raises corresponding research question and highlights the research significance of this thesis topic; Second, the author concludes previous research on paid Q&A in aspects of Q&A system, paid subscription and sharing economy, and finds that most of prior research focuses on exploring the influence of usefulness but not enjoyment on the users’ willingness of continuing using a paid Q&A system; Third, the thesis introduces the VAM theory and build a modified model based on it, this modified model highlights the importance of pleasure on users’ continuance intention in using paid Q&A; Finally, the empirical study combining an Exploratory Factor Analysis and a Confirmatory Factor Analysis proves that, after integrating factors extracted from previous research and the proposed model, the research is tested to be explanatorily capable and hypotheses related to the model are mostly proved to be supported. As a conclusion, this study conducts an investigation on the constructs and related theories that influence users’ continuance intention to use paid Q&A, from a hedonic perspective. In this thesis, VAM theory is selected as the prototype of proposed research model which reveals factors affecting users’ continuance intention to use a Chinese paid Q&A product named Weibo Paid Q&A. In this thesis, the proposed model makes predictions that the constructs perceived fee and community atmosphere along with perceived enjoyment construct have critical effect on users’ continuance willingness in using Weibo Paid Q&A in China. With the assistance of PLS–SEM, this study analyzes data collected from users in WPQA, the empirical study verifies that users' continuance intention is assuredly dependent on perceived fee and community atmosphere along with perceived enjoyment. The study also reveals that quality of answerers and quality of answer positively exert significant influences on perceived enjoyment

Predicting the Way and the Degree of Users’ Content Contribution in the Social Question and Answer Community

Most predictions of user behavior occur after a user has participated in the community for a while, and those who have just registered are easily overlooked because their community characteristics have not yet been revealed. However, users are easy to be lost in the early stage. Based on the theory of social capital, this paper proposes a new approach to predict the willingness, mode, and degree of content contribution of the newly registered user based on users\u27 information disclosure behavior aiming at reducing the churn rate of newly registered users. We crawled the data of 4 million users in the Zhihu community and deeply studied the relationship between the disclosure behavior of different types of information and the content contribution degree of users through statistical analysis methods and machine learning algorithms. The result shows that if a user discloses personal information, the probability of his in-depth response contribution and in-depth questioning contribution will increase correspondingly, and different types of information disclosure will lead to a different probability of an increase. Furthermore, In addition, users\u27 disclosure of different types of information will lead to differences in their preference for the way they contribute content

Predicting best answerers for new questions: An approach leveraging topic modeling and collaborative voting

Workshop of Quality, Motivation and Coordination of Open Collaboration</p