What measures of quality do they use for a Q&A platform?
Rating scores given by users
In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.
Knowledge sharing platforms such as Quora and Zhihu emerge as very convenient tools for acquiring knowledge. These question and answer (Q&A) platforms are newly emerged communities about knowledge acquisition, experience sharing and social networks services (SNS). Unlike many other Q&A platforms, Zhihu platform resembles a social network community. Users can follow other people, post ideas, up-vote or down-vote answers, and write their own answers. Zhihu allows users to keep track of specific fields by following related topics, such as “Education”, “Movie”, “Technology” and “Music”. Once a Zhihu user starts to follow a specific topic or a person, the related updates are automatically pushed to the user's feed timeline. Although these platforms have exploded in popularity, they face some potential problems. The key problem is that as the number of users grows, a large volume of low-quality questions and answers emerge and overwhelm users, which make users hard to find relevant and helpful information. Zhihu Live is a real-time voice-answering product on the Zhihu platform, which enables the speakers to share knowledge, experience, and opinions on a subject. The audience can ask questions and get answers from the speakers as well. It allows communication with the speakers easily and efficiently through the Internet. Zhihu Live provides an extremely useful reward mechanism (like up-votes, following growth and economic returns), to encourage high-quality content providers to generate high-level information on Zhihu platform. However, due to the lack of efficient filter mechanism and evaluation schemes, many users suffer from lots of low-quality contents, which affects the service negatively. Recently, studies on social Q&A platforms and knowledge sharing are rising and have achieved many promising results. Shah et al. BIBREF0 propose a data-driven approach with logistic regression and carefully designed hand-crafted features to predict the answer quality on Yahoo! Answers. Wang et al. BIBREF1 illustrate that heterogeneity in the user and question graphs are important contributors to the quality of Quora's knowledge base. Paul et al. BIBREF2 explore reputation mechanism in quora through detailed data analysis, their experiments indicate that social voting helps users identify and promote good content but is prone to preferential attachment. Patil et al. BIBREF3 propose a method to detect experts on Quora by their activity, quality of answers, linguistic characteristics and temporal behaviors, and achieves 97% accuracy and 0.987 AUC. Rughinis et al. BIBREF4 indicate that there are different regimes of engagement at the intersection of the technological infrastructure and users' participation in Quora. All of these works are mainly focused on answer ranking and answer quality evaluation. But there is little research achievement about quality evaluation in voice-answering areas. In this work, we present a data-driven approach for quality evaluation about Zhihu Live, by consuming the dataset we collected to gather knowledge and insightful conclusion. The proposed data-driven approach includes data collection, storage, preprocessing, data analysis, and predictive analysis via machine learning. The architecture of our data-driven method is shown in Fig. FIGREF3 . The records are crawled from Zhihu Live official website and stored in MongoDB. Data preprocessing methods include cleaning and data normalization to make the dataset satisfy our target problem. Descriptive data analysis and predictive analysis are also conducted for deeper analysis about this dataset. The main contributions of this paper are as follows: (1) We release a public benchmark dataset which contains 7242 records and 286,938 text comments about Zhihu Live. Detailed analysis about the dataset is also discussed in this paper. This dataset could help researchers verify their ideas in related fields. (2) By analyzing this dataset, we gain several insightful conclusion about Zhihu Live. (3) We also propose a multi-branched neural network (MTNet) to evaluate Zhihu Lives' scores. The superiority of our proposed model is demonstrated by comparing performance with other mainstream regressors. The rest of this paper is organized as follows: Section 2 describes detailed procedures of ZhihuLive-DB collection, and descriptive analysis. Section 3 illustrates our proposed MTNet. In section 4, we give a detailed description of experiments, and the last section discusses the conclusion of this paper and future work.