Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (6): 12-20    DOI: 10.11925/infotech.2096-3467.2018.0696
Current Issue | Archive | Adv Search |
Ranking Answer Quality of Popular Q&A Community
Ming Yi(),Tingting Zhang
School of Information Management, Central China Normal University, Wuhan 430079, China
Download: PDF(553 KB)   HTML ( 4
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper proposes a new method to rank the quality of answers from a popular Q&A community in China. [Methods] First, based on the information acceptance model, we established initial quality indicators for the answer’s perceived values. Then, we discretized these indicators with the K-Medoids clustering algorithm. Third, we reduced and weighted the indictors with the help of rough set theory. Finally, we generated the formal rankings with the weighted grey correlation analysis. [Results] We evaluated the proposed method with 2 297 answers for six different types of questions from the Q&A website of “Zhihu”. We found that the answers ranked higher generally included textual message with images. These answers were also more informative than others and involved active members of the Q&A community. [Limitations] The size of our dataset needs to be expanded, and the evaluation method of the proposed model could be optimized. [Conclusions] The proposed method is an effective way to rank the quality of answers from the Q&A community.

Key wordsCommon Q&A Community      Answer Quality Ranking      Perceived Value      Rough Set Theory      Weighted Grey Correlation Analysis     
Received: 02 July 2018      Published: 15 August 2019

Cite this article:

Ming Yi,Tingting Zhang. Ranking Answer Quality of Popular Q&A Community. Data Analysis and Knowledge Discovery, 2019, 3(6): 12-20.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0696     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I6/12

[1] Hosseini M, Moore J, Almaliki M, et al.Wisdom of the Crowd Within Enterprises: Practices and Challenges[J]. Computer Networks, 2015, 90: 121-132.
[2] Fichman P.A Comparative Assessment of Answer Quality on Four Question Answering Sites[J]. Journal of Information Science, 2011, 37(5): 476-486.
[3] Zhu Z, Bernhard D, Gurevych I.A Multi-Dimensional Model for Assessing the Quality of Answers in Social Q&A Sites[C]// Proceedings of the 2009 International Conference on Information Quality.2009: 264-265.
[4] Shah C, Pomerantz J.Evaluating and Predicting Answer Quality in Community QA[C]// Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval.2010: 411-418.
[5] Yan Z, Zhou J.Optimal Answerer Ranking for New Questions in Community Question Answering[J].Information Processing and Management, 2015, 51(1): 163-178.
[6] Yang L, Qiu M, Gottipati S, et al.CQArank: Jointly Model Topics and Expertise in Community Question Answering[C]// Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 2013: 99-108.
[7] 刘瑜, 袁健. 基于RTEM模型的问答社区候选答案排序方法[J]. 电子科技, 2016, 29(5): 130-134.
[7] (Liu Yu, Yuan Jian.Candidate Answer Sorting Method of Q&A Community Questions Based on RTEM Model[J]. Electronic Science and Technology, 2016, 29(5): 130-134.)
[8] 张成, 曲明成, 倪宁, 等. 基于概率潜在语义分析模型的自动答案选择[J]. 计算机工程, 2011, 37(14): 70-72.
[8] (Zhang Cheng, Qu Mingcheng, Ni Ning, et al.Automatic Answer Selection Based on Probabilistic Latent Semantic Analysis Model[J]. Computer Engineering, 2011, 37(14): 70-72.)
[9] Guo L, Hu X.Identifying Authoritative and Reliable Contents in Community Question Answering with Domain Knowledge[C]//Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2013: 133-142.
[10] 来社安, 蔡中民. 基于相似度的问答社区问答质量评价方法[J]. 计算机应用与软件, 2013, 30(2): 266-269.
[10] (Lai Shean, Cai Zhongmin.Question Answering Quality Evaluation for Community Question Answering Based on Similarity[J]. Computer Applications and Software, 2013, 30(2): 266-269.)
[11] 王伟, 冀宇强, 王洪伟, 等. 中文问答社区答案质量的评价研究: 以知乎为例[J]. 图书情报工作, 2017, 61(22): 36-44.
[11] (Wang Wei, Ji Yuqiang, Wang Hongwei, et al.Evaluating Chinese Answers’ Quality in the Community QA System: A Case Study of Zhihu[J].Library and Information Service, 2017, 61(22): 36-44.)
[12] Ginsca A L, Popescu A.User Profiling for Answer Quality Assessment in Q&A Communities[C]//Proceedings of the 2013 Workshop on Data-Driven User Behavioral Modelling and Mining from Social Media.2013: 25-28.
[13] 孔维泽, 刘奕群, 张敏, 等. 问答社区中回答质量的评价方法研究[J].中文信息学报, 2011, 25(1): 3-8.
[13] (Kong Weize, Liu Yiqun, Zhang Min, et al.Answer Quality Analysis on Community Question Answering[J]. Journal of Chinese Information Processing, 2011, 25(1): 3-8.)
[14] 姜雯, 许鑫, 武高峰. 附加情感特征的在线问答社区信息质量自动化评价[J]. 图书情报工作, 2015, 59(4): 100-105.
[14] (Jiang Wen, Xu Xin, Wu Gaofeng.Online Q&A Community Automatically Information Quality Evaluation with Sentiment Feature[J]. Library and Information Service, 2015, 59(4): 100-105.)
[15] John B M, Chua A Y K, Goh D H L. What Makes a High-Quality User-Generated Answer?[J]. IEEE Internet Computing, 2011, 15(1): 66-71.
[16] 李晨, 巢文涵, 陈小明, 等.中文社区问答中问题答案质量评价和预测[J]. 计算机科学, 2011, 38(6): 230-236.
[16] (Li Chen, Chao Wenhan, Chen Xiaoming, et al.Quality Evaluation and Prediction for Question and Answer in Chinese Community Question Answering[J]. Computer Science, 2011, 38(6): 230-236.)
[17] Sussman S W, Siegal W S.Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption[J]. Information Systems Research, 2003, 14(1): 47-65.
[18] 王洪伟, 孟园. 在线评论质量有用特征识别: 基于GBDT特征贡献度方法[J]. 中文信息学报, 2017, 31(3): 109-117.
[18] (Wang Hongwei, Meng Yuan.Helpful Features Identification of Online Reviews Quality Based on GBDT Feature Contribution[J].Journal of Chinese Information Processing, 2017, 31(3): 109-117.)
[19] Radev D R, Jing H, Styś M, et al.Centroid-Based Summarization of Multiple Documents[J]. Information Processing & Management, 2004, 40(6): 919-938.
[20] Joyce E, Kraut R.Predicting Continued Participation in Newsgroups[J]. Journal of Computer-Mediated Communication, 2006, 11(3): 723-747.
[21] 周志远, 沈固朝. 粗糙集理论在情报分析指标权重确定中的应用[J]. 情报理论与实践, 2012, 35(9): 61-65.
[21] (Zhou Zhiyuan, Shen Guchao.Application of Rough Set Theory in Determining the Weight of Intelligence Analysis Index[J]. Information Studies: Theory&Application, 2012, 35(9): 61-65.)
[22] 张政超, 关欣, 何友, 等. 粗糙集理论数据处理方法及其研究[J]. 计算机技术与发展, 2010, 20(4): 12-16, 20.
[22] (Zhang Zhengchao, Guan Xin, He You, et al.Rough Sets Data Processing Method and Its Research[J]. Computer Technology and Development, 2010, 20(4): 12-16, 20.)
[23] 张雪萍, 龚康莉, 赵广才. 基于MapReduce的K-Medoids并行算法[J]. 计算机应用, 2013, 33(4): 1023-1025, 1035.
[23] (Zhang Xueping, Gong Kangli, Zhao Guangcai.Parallel K-Medoids Algorithm Based on MapReduce[J]. Journal of Computer Applications, 2013, 33(4): 1023-1025, 1035.)
[24] Fahad A, Alshatri N, Tari Z, et al.A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(3): 267-279.
[25] PawlakZ. Rough Set[J]. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[26] 孙晶晶. 基于粗糙集理论的决策表属性约简与规则约简算法研究及相关应用[D]. 郑州: 中国人民解放军信息工程大学, 2005.
[26] (Sun Jingjing.Research on Attribute Reduction and Rule Reduction Algorithm of Decision Table Based on Rough Set Theory[D]. Zhengzhou: Information Engineering University, 2005.)
[27] 邓聚龙. 灰色系统基本方法[M]. 武汉: 华中理工大学出版社, 1987.
[27] (Deng Julong.Basic Methods of Grey System[M]. Wuhan: Huazhong University of Science & Technology Press, 1987.)
[28] 于亮, 方志耕, 吴利丰, 等. 基于灰色类别差异特性的评价指标客观权重极大熵配置模型[J]. 系统工程理论与实践, 2014, 3(8): 2065-2070.
[28] (Yu Liang, Fang Zhigeng, Wu Lifeng, et al.Maximum Entropy Configuration Model of Objective Index Weight Based on Grey Category Characteristics Difference[J]. Systems Engineering- Theory&Practice, 2014, 3(8): 2065-2070.)
[29] 黄涛. 基于灰色关联度分析的模糊群决策方法研究[D].广州: 华南理工大学, 2016.
[29] (Huang Tao.Research of Fuzzy Multi-Attribute Decision Making Method Based on Grey Correlation Analysis[D]. Guangzhou: South China University of Technology, 2016.)
[1] Jian Li,Mingyue Wang,Luming Xu,Yingchun Tian. The Construction of Digital Medical Information Service Evaluation System Based on User Perceived Value[J]. 数据分析与知识发现, 2019, 3(2): 118-126.
[2] Chunmei Gan,Kai Huang,Jiayi Xu,Tiantian Lin. The Influences of Technological Factors and Perceived Values on Continuance Intention of Social Commerce: An Empirical Study[J]. 数据分析与知识发现, 2018, 2(4): 29-37.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn