EFFECTS OF LABEL USAGE ON QUESTION LIFECYCLE IN Q&A COMMUNITY

Community question answering (CQA) sites have developed into vast collections of valuable knowledge. Questions, as CQA’s central component, go through several phases after they are posted, which are often referred to as the questions’ lifecycle or questions’ lifespan. Different questions have different lifecycles, which are closely linked to the topics of the questions that can be determined by their attached labels. We conduct an empirical analysis based on the dynamic panel data of a Q&A website and propose a framework for explaining the time sensitivity of topic labels. By applying a Discrete Fourier Transform and a Knee point detection method, we demonstrate the existence of three broad label clusters based on their recurring features and four common question lifecycle patterns. We further prove that the lifecycles of questions in disparate clusters vary significantly. The findings support our hypothesis that questions with more time-sensitive labels are more likely to hit their saturation point sooner than questions with less time-sensitive labels. The research results could be applied for better CQA interface design and more efficient digital resources management

How Question Features Influence Page Traffic? A Comparative Study on General and Domain-specific Q&As

Online Question and Answering services (Q&As) are becoming increasingly popular among information seekers. Users on these platforms identify their information needs by asking questions and interacting with others. Frequent user activities have led to a significant increase in traffic on Q&As, which motivates researchers to study the driving factors behind page traffic. The differences in the impacts of question quality features on the page traffic of domain-general and domain-specific Q&As remain unclear. To address this research gap, this study compares the traffic-driven effects of question features on general and domain-specific Q&A communities based on a database with more than 160,000 questions and their related 20 textual and non-textual features. Grey Relational Analysis is used to generate ranking lists for the two communities. The results indicate that review features drive the traffic of general Q&As the most, while user features are more significant in driving traffic for domain-specific Q&As