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Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

This study aims at exploring users' motives to form attachments within the social Q&A community context and identifying the differences between active users and lurkers when building emotional attachments. By utilizing the media system dependency (MSD) theory, this study investigates into the driving factors of dependency relations (understanding, orientation and play) to user attachments (i.e. attachment to the social Q&A community, attachment to content creators).

The research model is empirically validated by an online questionnaire among users of a social Q&A community. Deriving from the actual behavioral data, the authors divide 262 valid responses into 157 active users and 105 lurkers according to whether they post or not. The partial least squares (PLS) method is exploited to analyze the relationships in the model. In addition, the PLS-based multi-group analysis is conducted for comparing active users and lurkers.

The empirical results confirm that dependency relations (understanding, orientation and play) significantly influence user attachments. Multi-group analysis suggests that the effect of understanding dependency relations on attachment to content creators is stronger for active users than for lurkers. However, the effect of orientation dependency relations on user attachment is significant for lurkers but not significant for active users.

This study enriches the knowledge of the MSD theory by extending it to the social Q&A community setting. Based on the MSD theory, the relationships between three sides of dependency relations and two types of user attachments are hypothesized in the research model. Besides, the impact of user heterogeneity in building user emotional attachment still lacks consideration. This study is one of the first in the field of comparison studies to compare active users and lurkers in such context, providing a novel contribution in understanding the motivations and emotional responses of different users.

Online Q&A communities have been widely highlighted as an important knowledge exchange market. Although motivations for users’ initial knowledge-seeking behavior have been widely investigated, the factors that affect online Q&A users’ continued knowledge-seeking behavior are still vague. This study aims to investigate the factors that affect users continuously seeking knowledge from online social Q&A communities.

Based on social information processing theory, social capital theory, social exchange theory and social cognitive theory, this study used a negative binomial regression model to explore what would affect people’s continued knowledge-seeking behavior. Empirical data was collected from a popular Chinese online social Q&A community.

The results indicate that while previous knowledge sharing behavior, peer responses for previous seeking behavior, identity-based trust have a positive impact on knowledge-seeking behaviors, social exposure has a negative impact. In addition, self-presentation negatively moderates the relationship between social exposure and knowledge-seeking behavior.

This study contributed to the theoretical basis for knowledge-seeking behavior in online Q&A communities. The research findings can be used to derive guidelines for the development and operation of online social Q&A communities.

Although health question-and-answer (Q&A) communities have become popular in recent years, only a few communities have successfully retained and motivated their members to share knowledge. The purpose of this paper is to focus on the ways by which social capital and motivation influence knowledge sharing intention from the perspectives of health professionals and normal users in health Q&A communities.

The developed theoretical model integrates individual motivation and social capital theories. On the basis of a sample comprising 363 members from health Q&A communities in China, the authors tested the hypotheses by using structural equation modeling.

This study empirically finds that social capital positively affects intrinsic and extrinsic motivations, which then positively influence the intention of health professionals and normal users to share knowledge. Motivations of members fully mediate the effects of social capital on knowledge sharing intention. Specifically, intrinsic motivation influences knowledge sharing intention more for health professionals than for normal users, whereas extrinsic motivation influences knowledge sharing intention more for normal users than for health professionals.

This study explores the factors that affect the intentions of sharing knowledge in health Q&A communities by integrating social capital and motivation theories. Individual motivations can then bridge social capital and knowledge sharing intention. The effects of the intrinsic and extrinsic motivations of two user types were further examined and compared. These findings can extend the understanding of the underlying drivers of intention to share knowledge in the context of e-health.

The purpose of this paper is to examine some drivers of users’ participation in online social question-and-answer (Q&A) communities based on social cognitive theory and then identify the underlying mechanism of this process.

This study developed a research model to test the proposed hypotheses, and an online survey was employed to collected data. Totally, 313 valid responses were collected, and partial least squares structural equation modeling was adopted to analyze these data.

This study empirically finds that the outcome expectations (personal outcome expectations and knowledge self-management outcome expectations) are positively related to participation in online social Q&A communities. At the same time, users’ self-efficacy positively influences their participation behaviors. It can not only directly motivate users’ participation, but also indirectly promote participation behaviors through the two dimensions of outcome expectations. Besides, perceived expertise and perceived similarity are two positive and significant environmental elements affecting users’ participation.

This study extends the understanding about how participation behaviors will be motivated in the context of online social Q&A communities. Drawing on the social cognitive theory, constructs were established based on the features of these communities. Meanwhile, some mediating effects in the motivating process were also discussed.

The purpose of this study is to examine the impact of social media affordances, specifically social engagement and social endorsement, on knowledge contribution in online Q&A communities. Building on self-determination theory, this research seeks to tackle the issue of under-provision of knowledge in these communities.

The study employs a sample collected from a popular social Q&A community in China and uses linear panel data models along with multiple robustness checks to test the research model.

The findings reveal that both social engagement and social endorsement have a positive effect on users' knowledge contribution to the online Q&A community. However, the impact of social engagement is mitigated by social endorsement.

This paper makes a valuable contribution to the field by filling the research gap on the role of social engagement behaviors and their interaction with social endorsement in online Q&A communities. The results provide insights into how social media affordances can be leveraged to enhance knowledge contribution in these communities.

With the rapid development of paid online social question and answer (Q&A) communities, monetary social functions have been introduced and have potential benefits for both platforms and users. However, these functions' impact on knowledge contribution remains uncertain. This study proposes a conceptual model based on the stimulus–organism–response framework, according to which monetary and non-monetary social functions can help nurture short-term and long-term relationships among community users, and thereafter improves social identity and knowledge-sharing intentions.

This study selects Zhihu, a famous online social Q&A community in China, and conducts an online survey to collect data from its frequent users. A sample of 286 valid questionnaires was collected to test our research model by using a structural equation modeling method. In addition, a bootstrapping approach is used to test the mediation effect.

Results indicate that monetary social functions help nurture short-term and long-term relationships among community users. However, non-monetary social functions only affect short-term relationships directly. Short-term and long-term relationships both have a positive relationship with social identity and thereafter improve users' knowledge-sharing intentions.

This study focuses on users' knowledge-sharing intentions in Q&A communities from the perspective of social. Specifically, we separated social functions in Q&A platforms into monetary and non-monetary ones and explored their impact on the development of short-term and long-term relationships. Results demonstrate the importance of monetary social functions and explain how monetary and non-monetary social functions affect users' knowledge-sharing intentions in different approaches.

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

The purpose of this paper is to examine the general public’s information needs concerning open source software (OSS) and OSS answerers’ motivations for sharing their knowledge of OSS in social Q&A.

Two studies were carried out. In Study 1, a content analysis classifying OSS-related questions posted during December 2005-December 2012 in Yahoo! Answers was employed to investigate the general public’s information needs regarding OSS. In Study 2, an online survey was conducted with OSS answerers in Yahoo! Answers in order to examine what motivates them to share and continue to share their knowledge of OSS in social Q&A. In total, 1,463 invitations were sent out via Yahoo! Answers’ internal e-mail function to those who provided answers to OSS-related questions during September 2009-September 2012. In total, 150 usable surveys were returned and used for data analysis.

The findings from Study 1 indicate that the general public is most interested in finding out if there is OSS that meets their software need in a certain category (51.4 percent). Other popular question categories include the general description of OSS (15.6 percent), technical issues that they have with OSS (9.8 percent), and the advantages/disadvantages of using OSS (7.0 percent). Results on OSS answerers’ motivations from Study 2 support that all seven motivations identified (i.e. altruism, enjoyment, ideology, learning, reputation, reciprocity, and self-efficacy) are important, with the smallest mean value being 4.42 out of seven (i.e. reciprocity). However, only altruism, ideology, self-efficacy, and enjoyment were found to significantly influence contribution continuance intention.

With social Q&A growing in popularity, OSS communities that look for ways to draw in more users from the general public are recommended to increase their presence in social Q&A. The findings with regard to OSS answerers’ motivations can also help OSS community leaders attract and guide more members who are interested in sharing their OSS knowledge in social Q&A.

By classifying OSS-related questions that are publicly available in Yahoo! Answers, this study offers a breakdown of the general public’s information needs regarding OSS. In addition, results on OSS answerers’ motivations suggest that in order to sustain their member contributions in social Q&A, OSS community leaders should pay more attention to nurturing the motivations that are intrinsic (i.e. altruism, self-efficacy, enjoyment) and integrated (i.e. ideology).

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.