Influence of Question Quality Features on Web Performance in Community Question Answering
Creators: name: Sha, Alyssa
Publisher: name: The Australian National University
Year: 2023

description: Community Question-Answering (CQA) platforms are among a slew of active and common Web 2.0 apps utilized daily by millions of users to find answers to complicated, subjective, or context-dependent questions. They also developed into useful platforms for creating, sharing, and acquiring a massive amount of human knowledge. Unlike the collaborative or expert-based Q&amp;A models, soliciting others' opinions, recommendations, or personal thoughts becomes a more appropriate option for CQA because opinion-seeking questions may elicit multiple user responses. In contrast, information-seeking questions may only require interaction with a few answerers to resolve information needs. Thus, CQA encourages users to participate in various activities, including not only asking and answering questions but also commenting on them, rating the quality of the answers, and voting for the best ones. The opportunity to communicate with and get social support from others and exposure to a vast amount of content are advantages of using CQA. Questions play a critical role in CQA systems. The quality of questions in CQA significantly affects the satisfaction levels of the users, therefore, the Web performance. A high-quality question can attract users' initial attention and motivate knowledge-sharing intentions, which in turn can lead to an increase in answering attempts, a quicker response rate and higher answer quality. However, it has been shown that the degree of question quality varies greatly which leads to the information asymmetry problems in CQA (Yao et al., 2013). The premise of looking for a good answer is asking a good question first, which is the significance of the question quality assessment. However, it has been shown that the quality of questions differs significantly and rarely raises attention. The majority of previous CQA studies focused on answer quality which have overlooked the fundamental importance of question quality. How might signal precedence affect receiver interpretation is an important research gap in signaling studies, which urgently needs to be resolved in CQAs as it affects the interpretation of signals by users. Thus, the exploration of different signal strengths, in other words, the ranking of the influences of question-quality features, is a gap existing in signaling process research field and vital to the decision-making of users or platform managers. This thesis aims to fill the research gap by quantifying question quality comprehensively in CQA and building the connection of the question's quality signals and its Web performance based on large real-world dataset. This research aims to contribute in the following ways. Firstly, this study fills the current CQA and signaling theory research gaps by comparing the impact of question quality features on the page traffic of general and domain-specific CQAs, which provides theoretical contribution by testing signaling theory on CQA context. Furthermore, this study generates a comprehensive set of quality characteristics, which can be useful for the automation of question or answer quality evaluation on Q&amp;A sites. Practically, this study provides recommendations on how to create more effective knowledge sharing communities. This research opens up a promising path for modeling the growth phase of Web traffic through a GRA ranking framework in CQA, which can potentially result in realistic enhancements to the Web performance of a CQA platform. The findings of this study may assist platform policy makers and tool designers on how to optimize community support systems (such as credit scoring system, voting system and reward system) and maintain content quality control.