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Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.

The authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.

The authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.

This research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

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.

As academic social Q&A networking websites become more popular, scholars are increasingly using them to meet their information needs by asking academic questions. However, compared with other types of social media, scholars are less active on these sites, resulting in a lower response quantity for some questions. This paper explores the factors that help explain how to ask questions that generate more responses and examines the impact of different disciplines on response quantity.

The study examines 1,968 questions in five disciplines on the academic social Q&A platform ResearchGate Q&A and explores how the linguistic characteristics of these questions affect the number of responses. It uses a range of methods to statistically analyze the relationship between these linguistic characteristics and the number of responses, and conducts comparisons between disciplines.

The findings indicate that some linguistic characteristics, such as sadness, positive emotion and second-person pronouns, have a positive effect on response quantity; conversely, a high level of function words and first-person pronouns has a negative effect. However, the impacts of these linguistic characteristics vary across disciplines.

This study provides support for academic social Q&A platforms to assist scholars in asking richer questions that are likely to generate more answers across disciplines, thereby promoting improved academic communication among scholars.

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.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.