The Impact Of Thread-level Conversation Structure On Problem Solving Degree In Q&A Communities

With the development of the Internet,people’s demand for knowledge is increasing,and user-generated content is becoming more and more popular.In this context,Q&A communities have gradually become an important way for people to find answers to their problems.In social Q&A communities,there are different knowledge sharing activities and user interactions in different categories of threads.Most previous literature has focused on users’ perceptions and experiences during online interactions,or on the factors influencing the quality of answers and questions,or on the characteristics of social networks among members to discover user interactions,without exploring the actual dialogue process in each discussion in more depth.In Q&A communities,the vast majority of social interactions between members are thread-based textual interactions,but most current research focuses on posts or the entire social network,lacking structural analysis of thread-level verbal interactions;in addition,the transmission of speech and information among members also relies heavily on textual content,which can intuitively reflect users’ intentions and purposes,while current research on the content aspects of Q&A communities focuses on simple topic analysis or semantic association and sentiment analysis and few studies have explored how users communicate with each other from a speech act perspective.Understanding verbal interactions at the thread level helps facilitate user conversations,further aiding problem solving and sustaining a healthy community.Based on speech act theory,this study manually assigns dialogue act labels to each post to investigate the conversation structure differences of different types of threads;and constructs conversation networks to explore the effects of different conversation network variables on problem solving degree.Specifically,based on the CSDN community crawl dataset with 252 threads,this thesis uses process mining and frequent subgraph mining techniques to reveal the structural differences between satisfied-closed and unsatisfied-closed threads.In addition,the network indicators of conversation structure were used as independent variables to construct an econometric model affecting the degree of problem solving,and the hypothesis testing was performed using ordered logistic regression method.The results of this study found that satisfied closed threads tend to have more answer-related posts and fewer question-related posts than unsatisfied closed threads,and contain more civil engagement and interaction in satisfied closed threads.The section on empirical studies shows that graph density,average path length,and Q/A ratio have a significant negative effect on problem solving degree,while the average clustering coefficient has a significant positive effect on problem solving degree.This study is theoretically useful to explore the knowledge sharing process of social Q&A communities from both content and network perspectives,and practically can provide suggestions for community users and managers.The innovations of this study include two main aspects.Firstly,this thesis takes threads as the basic unit of study,most of the previous literature has focused on the analysis of posts or from the community level as a whole,and few studies have examined the effect of thread-level conversation structure.Second,based on speech act theory,we further explore the impact on problem solving degree from the perspective of conversation networks by comparing whether there is a difference in conversation structure between satisfied and unsatisfied closed threads by building an econometric model.Combining content and network analysis enriches existing interaction research and provides new research perspectives for subsequent studies.