Hua Zheng

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Hua Zheng is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Wisdom in the social crowd: An analysis of Quora Graph
Online social networks
Q& A system
WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web English 2013 Efforts such as Wikipedia have shown the ability of user communities to collect, organize and curate information on the Internet. Recently, a number of question and answer (Q&A) sites have successfully built large growing knowledge repositories, each driven by a wide range of questions and answers from its users community. While sites like Yahoo Answers have stalled and begun to shrink, one site still going strong is Quora, a rapidly growing service that augments a regular Q&A system with social links between users. Despite its success, however, little is known about what drives Quora's growth, and how it continues to connect visitors and experts to the right questions as it grows. In this paper, we present results of a detailed analysis of Quora using measurements. We shed light on the impact of three different connection networks (or graphs) inside Quora, a graph connecting topics to users, a social graph connecting users, and a graph connecting related questions. Our results show that heterogeneity in the user and question graphs are significant contributors to the quality of Quora's knowledge base. One drives the attention and activity of users, and the other directs them to a small set of popular and interesting questions. Copyright is held by the International World Wide Web Conference Committee (IW3C2). 0 0
Mining the Factors Affecting the Quality of Wikipedia Articles Web2.0
Information quality
Neural Network
Quality Assessment
ISME English 2010 0 0
Mining the factors affecting the quality of Wikipedia articles Information quality
Neural network
Quality assessment
Proceedings - 2010 International Conference of Information Science and Management Engineering, ISME 2010 English 2010 In order to observe the variation of factors affecting the quality of Wikipedia articles during the information quality improvement process, we proposed 28 metrics from four aspects, including lingual, structural, historical and reputational features, and then weighted each metrics in different stages by using neural network. We found lingual features weighted more in the lower quality stages, and structural features, along with historical features, became more important while article quality improved. However, reputational features did not act as important as expected. The findings indicate that the information quality is mainly affected by completeness, and well-written is a basic requirement in the initial stage. Reputation of authors or editors is not so important in Wikipedia because of its horizontal structure. 0 0