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.

Sustainability of Stack Exchange Q&A communities: the role of trust

Knowledge-sharing communities are fundamental elements of a knowledge-based society. Understanding how different factors influence their sustainability is of crucial importance. We explore the role of the social network structure and social trust in their sustainability. We analyze the early evolution of social networks in four pairs of active and closed Stack Exchange communities on topics of physics, astronomy, economics, and literature and use a dynamical reputation model to quantify the evolution of social trust in them. In addition, we study the evolution of two active communities on mathematics topics and two closed communities about startups and compare them with our main results. Active communities have higher local cohesiveness and develop stable, better-connected, trustworthy cores. The early emergence of a stable and trustworthy core may be crucial for sustainable knowledge-sharing communities.

How to Achieve Maximum Participation of Users in Technical Versus Nontechnical Online Q&A Communities?

ABSTRACT Low user participation in online questions-and-answers (Q&A) communities to contribute knowledge is a serious threat to the sustainability of online forums. This subject is rarely addressed in the context of content shared at these forums. Furthermore, no study has introduced solutions to reduce low participation exclusively considering participants’ age, gender, and education level. User participation in the online question and answer (Q&A) communities to contribute knowledge was mostly examined by applying variance-based methodologies using primary and secondary datasets. This study has targeted the asymmetrical relationship between variables to achieve users’ participation in technical and nontechnical knowledge-sharing communities. We have collected valid responses, 382 from nontechnical and 395 from technical knowledge-sharing communities, and applied fuzzy-set qualitative comparative analysis (fsQCA) to get various equally effective configurations. These configurations explain maximum users’ participation in sharing knowledge in online Q&A communities. fsQCA results have also revealed that to achieve maximum user participation and knowledge contribution in technical knowledge-sharing communities, a sense of reciprocation and online social interaction are the necessary conditions, whereas for nontechnical knowledge-sharing communities, online social interaction is the necessary condition. Study findings are important for online Q&A community managers to minimize low user participation and achieve maximum knowledge contribution in online communities.

Evolution of the Coordination of Activities Aimed at Building Knowledge in the Wikipedia Community

The first decade of the 21st century spawned the intense development of online communities of practice. The largest knowledge-sharing communities were formed in several dozen language versions of Wikipedia. Defining rules for cooperation was necessary to ensure the desired content quality created by Wikipedians. It was essential to develop the appropriate initiatives, tools, and space for effective activity coordination within the service. Previous research in this area pointed to the role of leadership, group size, and tools facilitating work automation in creating actionable strategies and in the self-organization of work.
 This paper aims to characterize the variability in creating new concepts of cooperation in selected language versions of Wikipedia and identify the factors of participating in various forms of cooperation. The author assumes that the greater number of initiatives a user enters contributes to an increase in their overall activity.
 The research conducted was both qualitative and quantitative. A netnographic approach was used, as well as a statistical analysis of user activity records. Thanks to the netnographic research, the stages of Wikipedia’s evolution were identified. Quantitative research has shown a correlation between the number of activity areas (a user’s affiliation to WikiProjects) and their overall activity (the number of edits made). A change in Wikipedians’ activity style was also observed depending on their seniority on the website. The study’s conclusions may be helpful for organizations using crowdsourcing to achieve their own goals.

A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

PurposeThis paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.Design/methodology/approachFirst, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.FindingsFolksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.Originality/valueThe proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

An empirical study of COVID-19 related posts on Stack Overflow: Topics and technologies

The COVID-19 outbreak, also known as the coronavirus pandemic, has left its mark on every aspect of our lives and at the time of this writing is still an ongoing battle. Beyond the immediate global-wide health response, the pandemic has triggered a significant number of IT initiatives to track, visualize, analyze and potentially mitigate the phenomenon. For individuals or organizations interested in developing COVID-19 related software, knowledge-sharing communities such as Stack Overflow proved to be an effective source of information for tackling commonly encountered problems. As an additional contribution to the investigation of this unprecedented health crisis and to assess how fast and how well the community of developers has responded, we performed a study on COVID-19 related posts in Stack Overflow. In particular, we profiled relevant questions based on key post features and their evolution, identified the most prominent technologies adopted for developing COVID-19 software and their interrelations and focused on the most persevering problems faced by developers. For the analysis of posts we employed descriptive statistics, Association Rule Graphs, Survival Analysis and Latent Dirichlet Allocation. The results reveal that the response of the developers’ community to the pandemic was immediate and that the interest of developers on COVID-19 related challenges was sustained after its initial peak. In terms of the problems addressed, the results show a clear focus on COVID-19 data collection, analysis and visualization from/to the web, in line with the general needs for monitoring the pandemic.

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.

Motivation and barriers to participation in virtual knowledge-sharing communities of practice

Motivation and barriers to participation in virtual knowledge-sharing communities of practice

Liminal roles as a source of creative agency in management: The case of knowledge-sharing communities

Studies suggest that the experience of liminality – of being in an ambiguous, ‘betwixt and between’ position – has creative potential for organizations. We contribute to theory on the link between liminality and creative agency through a study of the coordinators of ‘knowledge-sharing communities’; one of the latest examples of a ‘neo-bureaucratic’ practice that seeks to elicit innovative responses from employees while intensifying control by the organization. Through a role-centred perspective, our study found that both the structural and interpretive aspects of coordinators’ role enactments promoted a degree of creative agency. ‘Front-stage’ and ‘back-stage’ activities were developed to meet the divergent expectations posed by senior management and community members, and the ambiguity of their roles prompted an array of different role interpretations. Our findings contribute to theory by showing how the link between liminality and creative agency is not confined to roles and spaces (consultancy work, professional expertise) that are positioned across organizational boundaries, or free from norms and expectations, but may also apply to roles that are ambiguously situated within organizational contexts and that are subject to divergent expectations. This shows how neo-bureaucratic forms may be both reproduced and renewed through the creative responses of individual managers.

Management of knowledge creation and sharing to create virtual knowledge-sharing communities: a tracking study

Purpose– The main aim of this paper is to address the lack of research on the potential impact of the radical changes in social networking in the so-called network society and indirectly the need to manage and constructively share in the collateral knowledge creation. To do this, a tracking study of the knowledge creation and sharing in a discussion forum has been conducted from a knowledge management perspective.Design/methodology/approach– A quantitative content analysis research design has been adopted in accordance with which content, text and messages on the website were critically examined; categories and themes identified and analysed; content coded and interpreted; and the results reported, relative to the research problem and theory. Both substantive decisions (what and how to code) and technical decisions (how coding will be interpreted) were made during the coding process and the interpretation of the results (Keyton, 2015). Three levels of division were used in the development of the coding system, namely, message construction, a summary of criteria and subcriteria and main links and sublinks (Wagneret al., 2012). The unit of analysis was a website, specifically theSouthAfrica.comonline discussion forum, which presented an observable and measurable unit for the dissection of the text into components, criteria, subcriteria and elements to be analysed.Findings– Two main findings emerged. First, it was empirically found that knowledge intervention by an expert in the organisation is in fact possible (and indeed needed as a proactive means) to ensure new knowledge is created and shared by individuals in the forum on a continuous basis. Second, it was found that a good theoretical foundation or framework can indicate the importance of various aspects which should be considered to obtain useful results from the participants or members of the forum through knowledge management.Research limitations/implications– The study is limited by the fact that it only focussed on the measurement of knowledge management in one online discussion forum,SouthAfrica.com, during two time frames from an organisational perspective in terms of the three Cs. Further research in other settings would enhance the findings of this study, and researchers are encouraged to use the theoretical framework in future studies.Practical implications– The main implication for managers in practice is that the research proved that participants in an online discussion forum quite often regard those members who manage the discussion forum as “experts”, but organisations then run the risk that the knowledge created and shared might not support, and/or might be detrimental to, the overall objectives and brand of the organisation.Originality/value– This paper proposes the use of a theoretical framework to measure knowledge management, as applied to the identified online discussion forum, focussing on the three main components of content, communication and consumer with subcriteria and elements of the knowledge management perspective specifically. The main findings indicated that knowledge creation and sharing in online discussion forums is best supported if these components are proactively managed by an expert in the organisation to sustain and enhance successful communication.

Change Agents and the Enactment Of Liminal Roles: The Case Of Knowledge-sharing Communities

Recent studies have suggested that the experience of liminality – of being in a ‘betwixt and between’ state – has implications for change agents within organizations. In this paper, we explore thes...

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.

Explaining the Sustainability of Digital Ecosystems based on the Wiki Model Through Critical-Mass Theory

This paper investigates the sustainability of a type of digital ecosystem, namely, knowledge-sharing communities built on the wiki model. Sustainability is hypothesized to result from the participation of contributors with varying levels of resources and interests. The differences in resources and interests, according to the critical-mass theory, enable such communities to overcome typical start-up and growth problems. This paper describes a preliminary empirical test of the critical-mass theory in this context, with Wikipedia as a test case that demonstrates sustainability as well as resource and interest heterogeneity, based on a survey of 78 Wikipedians. The characteristic patterns of success exhibited in Wikipedia are expected to inform the management of other wiki-based information assets.

Discovering Knowledge-Sharing Communities in Question-Answering Forums

In this article, we define a knowledge-sharing community in a question-answering forum as a set of askers and authoritative users such that, within each community, askers exhibit more homogeneous behavior in terms of their interactions with authoritative users than elsewhere. A procedure for discovering members of such a community is devised. As a case study, we focus on Yahoo! Answers, a large and diverse online question-answering service. Our contribution is twofold. First, we propose a method for automatic identification of authoritative actors in Yahoo! Answers. To this end, we estimate and then model the authority scores of participants as a mixture of gamma distributions. The number of components in the mixture is determined using the Bayesian Information Criterion (BIC), while the parameters of each component are estimated using the Expectation-Maximization (EM) algorithm. This method allows us to automatically discriminate between authoritative and nonauthoritative users. Second, we represent the forum environment as a type of transactional data such that each transaction summarizes the interaction of an asker with a specific set of authoritative users. Then, to group askers on the basis of their interactions with authoritative users, we propose a parameter-free transaction data clustering algorithm which is based on a novel criterion function. The identified clusters correspond to the communities that we aim to discover. To evaluate the suitability of our clustering algorithm, we conduct a series of experiments on both synthetic data and public real-life data. Finally, we put our approach to work using data from Yahoo! Answers which represent users’ activities over one full year.

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.