Quality Evaluation for Answers in Chinese QA Community Based on Random Forest -- Examples from Zhihu

As user-generated content develops continuously, online QA communities have become a major way to access high quality knowledge and information. Common concern of these communities is therefore to determine high quality answers accurately. This paper focuses on the largest Chinese QA community website Zhihu, and studies part of answers of the twenty popular topics we choose on it. We implement feature extraction to these answers in basic information, diversity and correlation with questions, and then apply random forest classifier to model and analyze the data. The classification model can predict the quality of answers with reasonable accuracy of more than 86%. Robustness test shows that the model has high stability. Introduction With the rapid development of Web 2.0, the dissemination mode of network information shifts rapidly from one-way mode to user-centered divergent mode. On this background, UGC (User-Generated Content) develops super-fast and online QA communities springs up with it. In QA communities, users can both raise and answer questions and give feedback by voting, commenting, selecting the best answer etc. [1]. Famous online QA communities so far include Yahoo! Answers, Uclue, Answers.com, Quora. Baidu Zhidao, Zhihu are more successful ones in China then. By September 2015, Zhihu has gained tens of thousands of topics that include 6.1 million questions and 19 million answers have been created in total. Despite article [2] indicates that user generated contents have high reliability and inevitably, and the quality of data varies greatly because of the huge number. The criterion for users to identify whether an answer is of high quality or not is based on seven kind of values which are content value, cognitive value, social sentiment value, information source value, external value, practicability and language popularity according to Kim’s research on Yahoo! Answers [3]. Harper’s research shows that the answer quality in communities that need to pay is higher than free ones [4]. Researches from Shah claims that the information contained in answers, the completeness and the novelty of answers are all have influences on answer quality [5]. To predict answer qualities, paper [6] constructs a model with non-textual features based on maximum entropy method and kernel density estimation, and get a better predicting performance than traditional models. To accurately evaluate answer quality, not only the answers themselves should be considered, but questions and answer writers may also have critical effects, as per paper [7]. The studies of Chinese QA communities are still having many deficiencies. Considering the huge differences between English and Chinese, the model fits in English communities may not suitable with Chinese communities. We use data from Zhihu, the largest Chinese QA community, propose a model to predict answer quality. Dataset Description Every question in Zhihu has its own topic, and consists of question title and description, each question has several answers. This structure of the website gives us a hint to crawling some valuable information. All topics in Zhihu construct a rooted directed graph trough parent-child International Conference on Humanities Science, Management and Education Technology (HSMET 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Social Science, Education and Humanities Research, volume 96