Online Knowledge Sharing Communities Research Articles

Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job Interviews

Job interviews are the most widely accepted method for companies to select suitable candidates, and a critical challenge is finding the right questions to ask job candidates. Moreover, there is a lack of integrated tools for automatically generating interview questions and recommending the right questions to interviewers. To this end, in this paper, we propose an intelligent system for assisting job interviews, namely, DuerQues. To build this system, we first investigate how to automatically generate skill-oriented interview questions in a scalable way by learning external knowledge from online knowledge-sharing communities. Along this line, we develop a novel distantly supervised skill entity recognition method to identify skill entities from large-scale search queries and web page titles with less need for human annotation. Additionally, we propose a neural generative model for generating skill-oriented interview questions. In particular, we introduce a data-driven solution to create high-quality training instances and design a learning algorithm to improve the performance of question generation. Furthermore, we exploit click-through data from query logs and design a recommender system for recommending suitable questions to interviewers. Specifically, we introduce a graph-enhanced algorithm to efficiently recommend suitable questions given a set of queried skills. Finally, extensive experiments on real-world datasets demonstrate the effectiveness of our DuerQues system in terms of the quality of generated skill-oriented questions and the performance of question recommendation.

A Little Help from My Friends: How Receiving Assistance Affects Participation in Online Knowledge-Sharing Communities

Online knowledge-sharing communities are important arenas for the development and exchange of knowledge; both firm participation and sponsorship in communities are increasing, as is scholarly interest in understanding how interactions between members shape subsequent participation. We contribute to this literature by examining the relationship between two increasingly relevant, yet underexplored, sets of factors: we examine the effects of with whom an interaction occurs (a user or an employee) on two types of participatory outcomes (future asking versus future replying). Specifically, we examine whether receiving a reply to a question––and from whom the reply is received––affects a new member’s likelihood of asking another question or replying to a question. We examine fifteen years of data from Statalist, the online community that formed around the Stata statistical software package. Our findings show that receiving a reply from another user is positively associated with future questioning, but negatively associated with future replying. However, receiving a reply from a firm employee is positively associated with future replying. To help address endogeneity concerns, we conducted qualitative interviews with Stata employees, used fixed-effects models, implemented an instrumental variable-based approach, utilized a two-stage Heckman selection model, and applied coarsened exact matching; the findings remain consistent. These findings suggest that social interactions play a key, albeit nuanced, role in shaping subsequent participation and the continued health of a community.

The Effects of Receiving Assistance on Participation in Online Knowledge-Sharing Communities

Online knowledge-sharing communities have become increasingly important arenas for knowledge-sharing that benefit participants and firms alike: participants receive support and assistance, while firms benefit from reduced support costs and innovative insights. Scholars have begun investigating the motives that drive voluntary participation in online knowledge-sharing communities, but whether or not and how interactions between users shape future participation remains underexplored. This study examines whether receiving an answer to a question––and from whom the answer is received––affects a new participant’s likelihood of participating again. We consider this relationship based on fifteen years of data from Statalist, the online community that formed around the Stata statistical software package. The data show that receiving an answer from a “regular” community member is positively associated with asking future questions and negatively associated with answering others’ questions. However, receiving an answer from a Stata employee is positively associated with both future asking and answering. Our findings suggest firms can take simple steps to promote deeper involvement by new community participants.

One size does not fit all: Rethinking recognition system design for behaviorally heterogeneous online communities

Online knowledge-sharing communities typically acknowledge their top contributors by implementing recognition systems. Extant recognition systems suffer from several limitations such as treatment of the entire community as homogeneous and inflexibility of customization. We propose a framework based on socio-technical design principles for designing a multi-criterion- and multi-segment-based recognition system that exploits multiple user characteristics for differential recognition according to community goals. We apply this framework on data gathered from Yelp.com and show how it can be used to recognize top members of different identified behavioral segments (amateurs, adepts, and enthusiasts) based on their performance on various relevant factors.

Understanding the Impact of Opinion Leaders’ Characteristics on Online Group Knowledge-Sharing Engagement from In-Group and Out-Group Perspectives: Evidence from a Chinese Online Knowledge-Sharing Community

Opinion leaders often play key roles in online knowledge-sharing communities, which has intrigued a lot of researchers and practitioners worldwide. However, it is not clear how various characteristics of opinion leaders may affect different online groups’ knowledge-sharing engagement. This paper aims to answer this question by building upon social capital theory to examine the differential influences of opinion leaders’ characteristics (interactivity, authority, and activity) on online groups. In-groups and out-groups were distinguished, and the study used the context of an investment-oriented online knowledge-sharing community. By leveraging a unique aggregated group-level secondhand dataset collected from Snowball.com, we conducted log-linear and Poisson regression models. The results revealed that the intensity of online group knowledge-sharing engagement was heavily contingent upon the types of characteristics of opinion leaders. We found that in-group knowledge-sharing engagement (generating new knowledge) was driven by an opinion leader’s interactivity and authority, whereas out-group knowledge-sharing engagement (developing new members) could not be facilitated by these types of characteristics. Instead, the opinion leader’s activity hindered out-group users from joining in-groups. The study also identified a “mutual promotion” issue, which was generated from the association between in-group and out-group knowledge-sharing engagement.

Why People are Involved in and Committed to Online Knowledge-Sharing Communities

One challenge to the success of online knowledge-sharing communities relates to the participants' longtime participation. Literature has explored the determinants of initial participation rather than longtime participation despite significant differences between them. To fill this research gap, this article conceptualizes involvement and continuous commitment regarding longtime participation and examines their antecedents in the Chinese context. Extending the expectancy-value theory, knowledge-sharing expectancy, knowledge-sharing value, and knowledge-sharing affect are identified as antecedents of involvement and continuous commitment. This article further suggests that interpersonal trust and the norm of reciprocity are important contextual factors in the Chinese context that enhance the positive impacts of these antecedents on involvement and continuous commitment. Empirical results confirm most hypotheses. Interestingly, the impact of knowledge-sharing affect is not influenced by interpersonal trust or the norm of reciprocity. Both theoretical and practical implications are discussed.

Understanding expert finding systems: domains and techniques

Expert finding can be required for a variety of purposes: finding referees for a conference paper, recommending consultants for a software project, and identifying qualified answerers for a question in online knowledge-sharing communities, to name a few. This paper presents taxonomy of the task of expert finding that highlights the differences between finding experts, from the type of expertise indicator’s point of view. The taxonomy supports deep understanding of different sources of expertise information in the enterprise or online communities; for example, authored documents, emails, online posts, and social networks. In addition, different content and non-content features that characterize the evidence of expertise are discussed. The goal is to guide researchers who seek to conduct studies regarding the different types of expertise indicators and state-of-the-art techniques for expert finding in organizations or online communities. The paper concludes that although researchers have utilized a large number of graph and machine-learning techniques for locating expertise, there are still technical issues associated with the implementation of some of these methods. It also corroborates that combining content-based expertise indicators and social relationships has the benefit of alleviating some of the issues related to identifying and ranking answer experts. The above findings give implications for developing new techniques for expert finding that can overcome the technical issues associated with the performance of current methods.

Context-Aware Expert Finding in Tag Based Knowledge Sharing Communities

With the rapid development of online Knowledge Sharing Communities (KSCs), the problem of finding experts becomes increasingly important for knowledge propagation and putting crowd wisdom to work. A recent development trend of KSCs is to allow users to add text tags for annotating their posts, which are more accurate than traditional category information. However, how to leverage these user-generated tags for finding experts is still underdeveloped. To this end, this paper develops a novel approach for finding experts in tag based KSCs by leveraging tag context and the semantic relationship between tags. Specifically, the extracted prior knowledge and user profiles are first used for enriching the query tags to infer tag context, which represents the user’s latent information needs. Specifically, two different approaches for addressing the problem of tag sparseness in authority ranking are proposed. The first is a memory-based collaborative filtering approach, which leverages non-negative matrix factorization (NMF) to find similar users for alleviating tag sparseness. The second approach is based on Latent Dirichlet Allocation (LDA) topic model, which can further capture the latent semantic relationship between tags. A large-scale real-world data set is collected from a tag based Chinese commercial Q&A web site. Experimental results show that the proposed method outperforms several baseline methods with a significant margin.

Activity Lifespan: An Analysis of User Survival Patterns in Online Knowledge Sharing Communities

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.