Answering Communities Research Articles

Using social Cognitive theory to reengage dormant users in question and answer Communities: A case study of active StackOverflow participants

Online question-and-answer communities are seriously threatened by low user participation. There is currently a rare comprehensive study on the knowledge contribution pattern of consistently active participants and the moderating role of peer recognition, which can help improve low participation and reengage inactive users, despite researchers having examined the various facets of knowledge contribution and made helpful suggestions. As per the self-determination and social cognitive theory, the communal environment impacts peers and imitates role models or reliable sources in their involvement patterns. We have examined StackOverflow’s most reliable active users from 2010 to 2020 using the social cognition and self-determination theories to use the findings to reactivate dormant users. We have used a two-step dynamic system GMM model to get robust and reliable findings. The research discovered that peer repudiation, reputation, and online social interactions favorably affect the contributed knowledge. However, knowledge-seeking and earning virtual badges such as gold and bronze usually negatively impact it. Furthermore, it was revealed that the effect of virtual badges on contributed knowledge was positively moderated by peer recognition. However, peer recognition reduces the benefits of social interaction and reputation on the contributed knowledge. The study’s findings advance the body of knowledge and provide thorough management implications for raising low participation, reengaging inactive users, and cultivating a culture of innovative sharing of knowledge.

The influence of gamification affordance on knowledge sharing behaviour: an empirical study based on social Q&A community

ABSTRACT The academic community has spent a lot of time and effort in recent years discussing the fact that gamification has a significant positive impact on encouraging user engagement behaviour. As the mainstream of current research, gamification affordance refers to the action possibilities provided by the gamified elements and features of gamified systems. However, there is a lack of research on how to improve users’ knowledge sharing behaviour by introducing gamification affordances into the social question and answer (Q&A) community. This study develops a causal relationship model between gamification affordance and knowledge sharing behaviour by utilising the social Q&A community as the research object. The fundamental characteristics of gamification affordance which users value are defined through a semi-structured interview based on a focus group. The results of the structural equation model analysis on Chinese users (n = 310) demonstrate that gamification affordances enhance the gamified experience of users, thus improving user satisfaction, and ultimately affecting users’ knowledge sharing behaviour. Furthermore, this study explored the important role of gamification affordance in the development of the social Q&A community and verified the significant role of gamified experience in enhancing the psychological outcomes of users. The study concludes with a discussion-worthy theoretical and practical significance.

Knowledge transfer to aid social coding: The case of Stack Overflow

Focused online question and answer (Q&A) communities aid social coding. Despite the growing importance of social coding, knowledge transfer in this context remains under-researched. Our primary objective is to understand the knowledge transfer process in this context. We conceptualize knowledge transfer as a process that is impacted by the prior knowledge transfer interactions (network) among participants and is augmented by gamification. We argue that social capital resulting from prior knowledge transfer network interactions impact answer quality. Moreover, we also argue that the relationship between social capital and answer quality is moderated by the complexity of the knowledge transferred. Hence, our models draw from multiple related research streams: online communities, knowledge transfer, social capital, and gamification. These models are empirically tested using data from Stack Overflow (SO), a popular online Q&A community that aids social coding. Our results help to understand knowledge transfer in Q&A communities that aid social coding. Moreover, our results have implications for research on other types of Q&A communities and can inform development of platforms to support online communities.

To answer or to ignore? The impact of questioners and questions on continuous knowledge contributions in virtual Q&A communities

ABSTRACT Continuous knowledge contributions are a key component of the success of virtual question and answer (Q&A) communities. Continuous knowledge contributions comprise answerers’ responses to questioners and their questions. As antecedent information to continuous knowledge contributions, based on stimulus–organism–response theory and information dual-process theory, a research model that reflects the impact of the characteristics of questions and questioners on answerers’ continuous knowledge contributions is established. Then, the model is empirically examined by using short panel data containing 44,198 question and answer interaction data points collected from 1,830 answerers on the Chinese Software Developer Network. Meanwhile, the mediation effects of outcome expectations and perceived achievement on the characteristics of questions and questioners are investigated. The two-way fixed-effects negative binomial regression results show that (1) the label similarity, question readability, norms of reciprocity and reputation of questioners all have a positive impact on answerers’ continuous knowledge contributions in virtual question and answer communities; (2) outcome expectations fully mediate the effect of label similarity, while they partially mediate the effect of question readability, the norms of reciprocity and the reputation of questioners; and (3) perceived achievement mediates the effects of label similarity, the norms of reciprocity and the reputation of questioners.

Contemporaneous effects of gamified reward specificity on knowledge contribution: Evidence from a Q&A community

This study investigates the contemporaneous effects of virtual badges on knowledge contribution in a Question and Answer (Q&A) community. Drawing on regulatory fit theory, we propose a conceptual framework of gamified reward specificity to explain how winning some types of badges can stimulate users’ contemporaneous knowledge contributions more likely than others. This study empirically assesses such contemporaneous effects by conducting logistic regression analyses on the data collected from Stack Overflow. Our findings suggest that attaining a specific badge can increase users’ contemporaneous knowledge contributions related to that badge while earning a non-specific badge can decrease such contemporaneous contributions. These findings contribute a new perspective to the existing literature and address overlooked aspects of gamification practices, offering innovative insights into designing gamified reward systems more effectively in Q&A communities.

Does gamification affect knowledge-sharing behavior? The mediating role of intrinsic satisfaction needs

PurposeThis study employed self-determination theory (SDT) and the “Motivational affordance–Psychological outcomes–Behavioral outcomes” framework to investigate the relationship between gamification features and knowledge-sharing behavior in online communities.Design/methodology/approachA theoretical model was tested with 281 Chinese users from an online social question and answer (Q&A) community. Partial least square structural equation modeling was applied to analyze the data.FindingsThe empirical results revealed that competence mediated the effects of immersion and achievement-related gamification features on knowledge sharing. Moreover, relatedness mediated the effects of immersion, achievement and social-related gamification features on knowledge sharing.Research limitations/implicationsThis study was conducted on a Chinese Q&A platform, and the results may not be generalizable to other cultures or service providers with different goals.Practical implicationsThe study's findings indicate that gamification could serve as an effective toolkit for incentivizing and promoting knowledge sharing in online communities. The findings thus provide strategic insights for administrators of online communities seeking to leverage gamification designs to encourage user participation in knowledge-sharing activities.Originality/valueResearch on the role of gamification in promoting knowledge sharing has been limited in scope and has focused on tourism comment communities. Little evidence exists on the effect of gamification within social Q&A communities. Further, the finding of gamification's positive role in motivating knowledge sharing indicates the need for the knowledge-sharing field to focus on contextual factors.

Research on influencing factors and governance of disinformation dissemination on science and technology topics: an empirical study on the topic of “metaverse”

PurposeThe online Question and Answer community is full of a large number of science and technology topics, the discussion and dissemination of which play an important role in promoting the popularization of new technologies and cultivating public enthusiasm for science. However, the spread of false information and rumors weakens the community's positive effect, making the community more difficult for people to obtain useful information on such topics. Research on the influencing factors and governance of the spread of false information on science and technology topics has become the key to the spread of popular science.Design/methodology/approachTherefore, this paper uses the Elaboration Likelihood Model as the theoretical framework to examine the role of the factors influencing the spread of false information on science and technology topics in Zhihu community on the information persuasion and the impact on public behavior attitude from the core path and the edge path. This paper compiles a crawler program to capture 12,893 response information under the “Metaverse” topic in Zhihu community as an empirical sample and uses text mining and conducts visual correlation analysis to explore the key factors affecting the persuasive transmission path of information on science and technology topics.FindingsThe research finds that the content specialization, content consistency and content coherence of science and technology topics affect personal judgment from the aspect of information content through the core path and have a positive correlation with information persuasion; the number of comments, the length of the text and the publishing authors' influence from the edge image characteristics through the edge path are positively correlated with the information persuasion. Then, from the perspective of topic platform, government and topic participants, this paper puts forward a general plan to improve the information persuasion of science and technology topics so as to deal with false information.Originality/valueCompared with the small data set of the traditional questionnaire survey, the research based on community empirical big data is more reliable. The model takes into account the attitude and behavior of users and is more suitable for the research on the transmission path of scientific and technological information in the internet era. This research provides a direction for analyzing the text characteristics and development trends of information in the field of science and technology and is conducive to promoting the optimization of the network information environment and building a good ecology, with the spread of rumors about science and technology topics curbed and the governance of false information strengthened.

Analysis of Public Purchase Interest in Yamaha Motorcycles Using the K-Nearest Neighbor Method

This data mining will carry out a classification of people who are interested and not interested in buying Yamaha motorcycles. In the data mining process, a method is needed that can provide goals to the data mining process. That's because there are many data mining methods that can be used. In this study the method that will be used by the author is the K-Nearest Neighbor (kNN) method. This method will be used to classify people's buying interest in Yamaha motorbikes. This research was conducted because there are some people who say that Yamaha motorbikes are not good, use of wasteful fuel. Therefore this research was conducted to prove this statement. So a research was made about people's buying interest in Yamaha motorbikes. Classification results obtained from 100 community data. From the classification process that has been carried out, the results show that 41 community data (41% representation) are interested in buying Yamaha motorcycles and 59 community data (59% representation) are not interested in buying Yamaha motorbikes. The results obtained state that there are still many people who are interested in Yamaha motorbikes. But it can be used as a reference that people are interested in motorbikes that have a good appearance, use economical fuel and are affordable. These results were obtained from the community's answers in the questionnaire, they were interested in motorbikes that use little fuel, have good designs and are affordable.

Neural age screening on question answering communities

For online social networks, demographic analysis is absolutely essential for improving their services in many ways. It is instrumental in understanding their different audiences, members and competitors. As well as that, it is pivotal in designing effective personalization and contextualization strategies, especially for displaying and creating better content. There is, for this reason, a great bulk of research into how demographic variables are characterized and how they impact online platforms such as Facebook and Twitter. But surprisingly, only a handful of works delve into their characterization and effect on community Question-Answering (cQA) websites. In this particular context, the subject of age demographics remains largely unexplored.This paper takes the lead on interpreting automatic age recognition on CQAs (a.k.a. age screening) as a regression task. To this effect, it compares state-of-the-art graph-based neural network regression and embedding models on a massive activity-graph encompassing ca. 16 and 837 million nodes (members) and edges, respectively. For this study, a large-scale subset of ca. 657,000 community fellows was automatically associated with their age via aligning their profile texts with a limited number of linguistic patterns.In short, our results show that Node2vec significantly outperforms other embeddings regardless of the regression model used for casting predictions. When this embedding is combined with Artificial Neural Network Regressions, we obtained our best configuration scoring a Root Mean Square Error (RMSE) of 8.39. An interesting qualitative feature of this embedding space is that age-based centroid vectors tend to form a trail ordered by age. Lastly, our outcomes also signal that activity graph based models can rival its counterparts based on image and textual inputs, paving the way for constructing effective multi-modal approaches.

Text-based neural networks for question intent recognition

An effective organization of their knowledge bases is pivotal in keeping social networks vibrant, namely in designing successful personalization and contextualization strategies. This way, enhancing dedicated displays and encouraging the production of better content. Particularly for question answering communities, splitting archived material according to their intent is essential to reuse their knowledge and social capital.Recently, deep neural networks have shown breakthrough capabilities on multiple tasks related to language understanding. Thus the main contribution of this work is a thorough comparison of assorted architectures applied to the detection of question intents (i.e., informational and conversational). Evaluated on two collections, DEBERTA and RoBERTa demonstrated to be the best options by finishing with an accuracy of 71.19% and 74.10%, respectively. As for conventional neural networks, RCNNs proven to be the most effective technique. Overall, best models signal the usefulness of both question titles and bodies, and that fusing diverse learning strategies hold promise since they focus on learning different discriminative patterns.

Towards More Gender-Inclusive Q&As

Online Question and Answer communities (Q&As) are popular spaces for learning and sharing knowledge. However, prior research suggests that Q&As may not be appealing to and inclusive of men and women, with absent social considerations listed as a potential contributing factor. We investigate how additional community presence information can affect users' perceptions of and engagement with a Q&A for graphic design software. Through a 10-day task-based field study with 30 participants (14 women, 14 men, 2 non-binary), we uncover how community presence information can humanize the Q&A and play a role in promoting an inclusive environment. On the other hand, some participants question if community presence information belongs in a Q&A and describe some privacy implications. The women in our sample also talked about the importance of diverse community demographics, while we did not observe this sentiment expressed by the men. Our findings contribute an understanding of how users perceive the role of community presence information within a Q&A. We also discuss how this information might impact women's future participation and engagement.

Predicting the quality of answers with less bias in online health question answering communities

Existing approaches in online health question answering (HQA) communities to identify the quality of answers either address it subjectively by human assessment or mainly using textual features. This process may be time-consuming and lose the semantic information of answers. We present an automatic approach for predicting answer quality that combines sentence-level semantics with textual and non-textual features in the context of online healthcare. First, we extend the knowledge adoption model (KAM) theory to obtain the six dimensions of quality measures for textual and non-textual features. Then we apply the Bidirectional Encoder Representations from Transformers (BERT) model for extracting semantic features. Next, the multi-dimensional features are processed for dimensionality reduction using linear discriminant analysis (LDA). Finally, we incorporate the preprocessed features into the proposed BK-XGBoost method to automatically predict the answer quality. The proposed method is validated on a real-world dataset with 48121 question-answer pairs crawled from the most popular online HQA communities in China. The experimental results indicate that our method competes against the baseline models on various evaluation metrics. We found up to 2.9% and 5.7% improvement in AUC value in comparison with BERT and XGBoost models respectively.

Predicting users knowledge contribution behaviour in technical vs non-technical online Q&A communities: SEM-Neural Network approach

ABSTRACT Online question and answer (Q&A) community users’ knowledge contribution behaviour was studied using primary and secondary data and different research approaches. However, this topic is never explored in the context of content (knowledge) shared in these communities. Furthermore, online social interaction's role as a mediator is also ignored in online Q&A communities. This study model explored community recognition, online social interaction, devotion to community, self-satisfaction, and a sense of reciprocation's role in the knowledge contribution behaviour of Q&A community users. We collected 709 online Q&A community users’ responses and used SEM-ANN two-stage hybrid approach to capture linear and nonlinear relationships between variables. Results revealed that all explanatory variables are positively significant, while the sense of reciprocation is negatively significant to knowledge contribution. It strengthens the earlier researcher's claim that the term ‘tragedy of common’ implies online Q&A communities. Normalised importance results in the second stage figuring out that community recognition, online social interaction, and community devotion are the most influential factors behind knowledge contribution in online Q&A communities. Findings amplify our apprehension about the knowledge contribution behaviour of Q&A community users. It also provides evidence that dual-stage deep learning modelling can better capture variables’ linear and nonlinear relationships.

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.

Impact of monetary and non-monetary social functions on users' knowledge-sharing intentions in online social Q&A communities

PurposeWith 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.Design/methodology/approachThis 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.FindingsResults 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.Originality/valueThis 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.

Patterns of interest change in stack overflow

Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user’s interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user’s interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists’ interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users’ reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.

What motivates online community contributors to contribute consistently? A case study on Stackoverflow netizens.

Online Question and answer (Q&A) communities are the common and famous platforms to learn and share knowledge and are very useful for every knowledge seeker. Less knowledge contribution is a critical issue for the sustainability and future of these platforms. The motivation of inactive users to participate in Q&A communities is a real challenge. Based on the social cognitive and social exchange theory, we have studied the knowledge contribution patterns of active and consistent StackOverflow users over the last eleven years. We have used a difference generalized method of moments estimator to estimate the proposed model. Results revealed that reciprocation of knowledge and social interaction positively, whereas knowledge seeking of active and consistent users negatively influences knowledge contribution. Peer recognition and repudiation have partially positive and negative effects on users’ knowledge contribution. This research offers theoretical and practical suggestions to encourage people to contribute their knowledge to online Q&A communities.

An Exploration Study on the Consequence of the COVID-19 Pandemic on Online Q&A Communities

This work aims to document a two-sided impact of the COVID-19 pandemic on online question and answer communities. It implements empirical analyses on subsidiary communities affiliating to the Stack Exchange network. Using a difference-in-difference approach to identify the impact of the pandemic on community volume (the counts of questions and answers) and responsiveness (the likelihood of questions to be answered), this work has the following discoveries. First, the community volume grows, both in questions and answers, driven by a prolonged time of staying at home during the pandemic. Second, the community responsiveness declines, driven by an influx of new community members during the pandemic, which is also a result of the prolonged time of staying at home. Theoretical and practical implications are discussed.

Sports Information Needs in Chinese Online Q&A Community: Topic Mining Based on BERT

The online Question and Answering (Q&A) community has grown globally, allowing users to ask, discuss, and answer questions based on shared interests. As a gathering place for people’s knowledge production, collaboration, and dissemination in the current Internet scene, the online Q&A community can intuitively reflect the public’s information needs and behavior. It also collects many sports-related data and becomes an effective vehicle for comprehending mass sports information needs and disseminating sports knowledge. However, sports-related studies on the online Q&A community have rarely been reported. This study took the sports information in Zhihu, the largest Q&A community in China, as the research object to explore the public needs for sports information in China. We introduced the BERT model through a self-compiled python program and collected 391,092 sports-topic answers in the online Q&A community of Zhihu. Then, we explored the topic content, evolution trend, and user attributes of these answers. We found that the overall trend of sports information needs in Zhihu can be divided into three cycles: the London 2012 Olympic period, the Rio 2016 Olympic period, and the Tokyo 2020 Olympic period in general. The diversified content of information needs included 40 second-level themes and eight first-level themes. Male and female users had similarities and differences in sports information needs. The male and female users had the same information needs for fitness-related information. However, men were more concerned with confrontational solid sports such as basketball and football; women were more likely to care about weight loss, shape effect, and self-protection while doing sports activities. In addition, compared with men, women preferred to emphasize their gender attributes when expressing their needs for sports information to obtain more practical knowledge. In conclusion, our finding reveals that the sports community formed by the current online Q&A community in China is still a male-dominated information field.

The influence of users’ dark triad on knowledge contribution behaviour on social question and answer sites

The users' knowledge contribution behaviour is the driving force for the sustainable development of the social question and answer sites. This kind of user behaviour is affected by various factors, among which users' personality traits are the prominent ones. The dark triad is a theory on the dark side of personality. This article explores the influence and mechanism of users' dark triad on their knowledge contribution in social question and answer sites. A questionnaire survey and statistical methods such as hierarchical regression and Bootstrap analysis were adopted in this study. Information about 301 users with experience in Chinese social question and answer sites were collected through the questionnaire survey. The quantitative analysis of survey data employed the statistical package SPSS 26.0. The dark triad significantly affects knowledge contribution on social question and answer sites. Online self-disclosure plays a completely mediating role in the relationship between the dark triad and knowledge contribution. The relational psychological contract has a moderating role between online self-disclosure and knowledge contribution. This study argues that the dark triad has a positive effect on knowledge contribution behaviour in socialized question and answer communities by constructing a model of mediated effects that are moderated. The dark triad shows its altruistic side in the context of social question and answer sites. The role of the dark triad in different knowledge-intensive contexts should be viewed dialectically in future research. Based on these findings, we put forward some suggestions for encouraging users' knowledge contribution behaviour in the social media context.