Feature Extraction to Filter Out Low-Quality Answers from Social Question Answering Sites

References

• G. Blanco, R. Prez-Lpez, F. Fdez-Riverola, and A. M. G. Loureno, “Understanding the social evolution of the java community in stack overflow: A 10-year study of developer interactions,” Future Gener. Comput. Syst., Vol. 105, pp. 446–454, 2020. doi:10.1016/j.future.2019.12.021
   Web of Science ®Google Scholar
• J. Herrera, D. Parra, and B. Poblete, “Social QA in non-CQA platforms,” Future Gener. Comput. Syst., Vol. 105, pp. 631–649, 2020. doi:10.1016/j.future.2019.12.023
   Web of Science ®Google Scholar
• J. Yin, W. X. Zhao, and X. M. Li, “Type-aware question answering over knowledge base with attention-based tree-structured neural networks,” J. Comput. Sci. Technol., Vol. 32, pp. 805–813, 2017. doi:10.1007/s11390-017-1761-8
   Web of Science ®Google Scholar
• J. Surowiecki, M. P. Silverman, “The wisdom of crowds,” Am. J. Phys., Vol. 75, no. 2, pp. 190–192, Feb. 2007. doi:10.1119/1.2423042
   Google Scholar
• A. Y. Chua, and S. Banerjee, “So fast so good: An analysis of answer quality and answer speed in community question-answering sites,” J. Am. Soc. Inf. Sci. Technol., Vol. 64, pp. 2058–2068, 2013. doi:10.1002/asi.22902
   Web of Science ®Google Scholar
• A. Y. Chua, and S. Banerjee, “Measuring the effectiveness of answers in yahoo! answers,” Online Inf. Rev., Vol. 39, pp. 104–118, 2015. doi:10.1108/OIR-10-2014-0232
   Web of Science ®Google Scholar
• V. Kitzie, and C. Shah, “Faster, better, or both? looking at both sides of online question-answering coin,” Proceedings of the American Society for Information Science and Technology, Vol. 48, pp. 1–4, 2011. doi:10.1002/meet.2011.14504801180
   Google Scholar
• L. Mamykina, B. Manoim, M. Mittal, G. Hripcsak, and B. Hartmann, “Design lessons from the fastest Q&A site in the west,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2011, pp. 2857–2866. ACM
   Google Scholar
• T.-L. Wong, “Answering reachability queries on incrementally updated graphs by hierarchical labeling schema,” J. Comput. Sci. Technol., Vol. 31, pp. 381–399, 2016. doi:10.1007/s11390-016-1633-7
   Web of Science ®Google Scholar
• I. Srba, and M. Bielikova, “Why is stack overflow failing? preserving sustainability in community question answering,” IEEE Softw., Vol. 33, pp. 80–89, 2016b. doi:10.1109/MS.2016.34
   Web of Science ®Google Scholar
• Y. Zhang, D. Lo, X. Xia, and J.-L. Sun, “Multi-factor duplicate question detection in stack overflow,” J. Comput. Sci. Technol., Vol. 30, pp. 981–997, 2015. doi:10.1007/s11390-015-1576-4
   Web of Science ®Google Scholar
• B. Yang, and S. Manandhar, “Tag-based expert recommendation in community question answering,” in Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, 2014, pp. 960–963. IEEE.
   Google Scholar
• P. K. Roy, J. P. Singh, A. M. Baabdullah, H. Kizgin, and N. P. Rana, “Identifying reputation collectors in community question answering (CQA) sites: exploring the dark side of social media,” Int. J. Inf. Manage., Vol. 42, pp. 25–35, 2018b. doi:10.1016/j.ijinfomgt.2018.05.003
   Web of Science ®Google Scholar
• X. Wang, C. Huang, L. Yao, B. Benatallah, and M. Dong, “A survey on expert recommendation in community question answering,” J. Comput. Sci. Technol., Vol. 33, pp. 625–653, 2018. doi:10.1007/s11390-018-1845-0
   Web of Science ®Google Scholar
• R. Ren, H. Duan, W. Liu, and J. Liu, “Aunet: An unsupervised method for answer reliability evaluation in community QA systems,” in Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, 2018, pp. 281–292. Springer.
   Google Scholar
• M. A. Suryanto, E. P. Lim, A. Sun, and R. H. Chiang, “Quality-aware collaborative question answering: methods and evaluation,” in Proceedings of the Second ACM International Conference on web Search and Data Mining, 2009, pp. 142–151. ACM.
   Google Scholar
• X. He, L. Wang, W. Zhang, and P. Zhang, “Research on the quality prediction of online Chinese question answering community answers based on comments,” in Proceedings of the 2nd International Conference on Big Data Technologies, 2019, pp. 114–120.
   Google Scholar
• B. Qu, G. Cong, C. Li, A. Sun, and H. Chen, “An evaluation of classification models for question topic categorization,” J. Am. Soc. Inf. Sci. Technol., Vol. 63, pp. 889–903, 2012. doi:10.1002/asi.22611
   Web of Science ®Google Scholar
• M. J. Blooma, A. Y. Chua, and D. H.-L. Goh, “A predictive framework for retrieving the best answer,” in Proceedings of the 2008 ACM Symposium on Applied Computing, 2008, pp. 1107–1111. ACM
   Google Scholar
• C. Chen, K. Wu, V. Srinivasan, and R. K. Bharadwaj, “The best answers? think twice: identifying commercial campaigns in the CQA forums,” J. Comput. Sci. Technol., Vol. 30, pp. 810–828, 2015. doi:10.1007/s11390-015-1562-x
   Web of Science ®Google Scholar
• H. Toba, Z.-Y. Ming, M. Adriani, and T.-S. Chua, “Discovering high quality answers in community question answering archives using a hierarchy of classifiers,” Inf. Sci. (Ny), Vol. 261, pp. 101–115, 2014. doi:10.1016/j.ins.2013.10.030
   Web of Science ®Google Scholar
• Y. Yao, H. Tong, T. Xie, L. Akoglu, F. Xu, and J. Lu, “Detecting high quality posts in community question answering sites,” Inf. Sci. (Ny), Vol. 302, pp. 70–82, 2015. doi:10.1016/j.ins.2014.12.038
   Web of Science ®Google Scholar
• Xin-Qi Bao, and Yun-Fang Wu, “A Tensor Neural Network with Layerwise Pretraining: Towards Effective Answer Retrieval,” Journal of Computer Science and Technology, Vol. 31, no. 6, pp. 1151–1160, 2016. http://doi.org/10.1007/s11390-016-1689-4.
   Web of Science ®Google Scholar
• E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, “Finding high quality content in social media,” in Proceedings of the 2008 International Conference on web Search and Data Mining, 2008, pp. 183–194. ACM
   Google Scholar
• M. J. Blooma, A. Y.-K. Chua, and D. H.-L. Goh, “Selection of the best answer in CQA services,” in Information Technology: New Generations (ITNG), 2010 Seventh International Conference, 2010, pp. 534–539. IEEE
   Google Scholar
• L. Xu, J. Xiang, Y. Wang, and F. Ni, “Data-driven approach for quality evaluation on knowledge sharing platform,” in 2019 International Conference on Machine Learning and Cybernetics (ICMLC); 2019, pp. 1–6. IEEE.
   Google Scholar
• B. M. John, A. Y.-K. Chua, and D. H.-L. Goh, “What makes a high-quality user-generated answer?,” IEEE Internet Comput., Vol. 15, pp. 66–71, 2011. doi:10.1109/MIC.2011.23
   Web of Science ®Google Scholar
• C. T. Lee, E. M. Rodrigues, G. Kazai, N. Milic-Frayling, and A. Ignjatovic, “Model for voter scoring and best answer selection in community Q&A services. In Web Intelligence and intelligent agent technologies, 2009. WI-IAT’09,” in IEEE/WIC/ACM International Joint Conferences on, 2009, pp. 116–123. IEEE volume 1.
   Google Scholar
• C. Shah, and J. Pomerantz, “Evaluating and predicting answer quality in community QA,” in Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010, pp. 411–418. ACM.
   Google Scholar
• T. P. Sahu, N. K. Nagwani, and S. Verma, “Selecting best answer: An empirical analysis on community question answering sites,” IEEE. Access., Vol. 4, pp. 4797–4808, 2016. doi:10.1109/ACCESS.2016.2600622
   Web of Science ®Google Scholar
• M. J. Blooma, D. Hoe-LianGoh, and A. Yeow-Kuan Chua, “Predictors of high-quality answers,” Online Inf. Rev., Vol. 36, pp. 383–400, 2012. doi:10.1108/14684521211241413
   Web of Science ®Google Scholar
• J. Lou, Y. Fang, K. H. Lim, and J. Z. Peng, “Contributing high quantity and quality knowledge to online CQA communities,” J. Am. Soc. Inf. Sci. Technol., Vol. 64, pp. 356–371, 2013. doi:10.1002/asi.22750
   Web of Science ®Google Scholar
• P. K. Roy, Z. Ahmad, J. P. Singh, M. A. A. Alryalat, N. P. Rana, and Y. K. Dwivedi, “Finding and ranking high quality answers in community question answering sites,” Global Journal of Flexible Systems Management, Vol. 19, pp. 53–68, 2018a. doi:10.1007/s40171-017-0172-6
   Google Scholar
• D. Hoogeveen, A. Bennett, Y. Li, K. M. Verspoor, and T. Baldwin, “Detecting mis-flagged duplicate questions in community question answering archives,” in Tvwelfth International AAAI Conference on Web and Social Media, 2018, pp. 112–120.
   Google Scholar
• O. Kucuktunc, B. B. Cambazoglu, I. Weber, and H. Ferhatosmanoglu, “A large-scale sentiment analysis for yahoo! answers,” in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, 2012, pp. 633–642.
   Google Scholar
• M. Li, Y. Li, Q. Peng, and J. Wang, “A hybrid MCDM model combining DANP with TODIM to evaluate the information quality of community question answering in a two-dimensional linguistic environment,” Expert Syst., Vol. 38, no. 2, pp. e12619, 2021.
   Web of Science ®Google Scholar
• D. Elalfy, W. Gad, and R. Ismail, “A hybrid model to predict best answers in question answering communities,” Egyptian Informatics Journal, Vol. 19, no. 1, pp. 21–31, 2018. doi:10.1016/j.eij.2017.06.002
   Web of Science ®Google Scholar
• Y. Zhang, M. Zhang, N. Luo, Y. Wang, and T. Niu, “Understanding the formation mechanism of high-quality knowledge in social question and answer communities: A knowledge co-creation perspective,” Int. J. Inf. Manage., Vol. 48, pp. 72–84, 2019. doi:10.1016/j.ijinfomgt.2019.01.022
   Web of Science ®Google Scholar
• D. Palomera, and A. Figueroa, “Leveraging linguistic traits and semi-supervised learning to single out informational content across how-to community question-answering archives,” Inf. Sci. (Ny), Vol. 381, pp. 20–32, 2017. doi:10.1016/j.ins.2016.11.006
   Web of Science ®Google Scholar
• J. E. Van Engelen, and H. H. Hoos, “A survey on semi-supervised learning,” Mach. Learn., Vol. 109, no. 2, pp. 373–440, 2019.
   Web of Science ®Google Scholar
• H. Fu, and S. Oh, “Quality assessment of answers with user-identified criteria and data-driven features in social Q&A,” Inf. Process. Manag., Vol. 56, no. 1, pp. 14–28, 2019. doi:10.1016/j.ipm.2018.08.007
   Web of Science ®Google Scholar
• A. Tang, P. Ren, and Z. Sun, “Multi-feature based question–answerer model matching for predicting response time in CQA,” Knowl. Based. Syst., Vol. 182, pp. 104794, 2019. doi:10.1016/j.knosys.2019.06.002
   Web of Science ®Google Scholar
• O. Chergui, A. Begdouri, and D. Groux-Leclet, “Integrating a Bayesian semantic similarity approach into CBR for knowledge reuse in community question answering,” Knowl. Based. Syst., Vol. 185, pp. 104919, 2019. doi:10.1016/j.knosys.2019.104919
   Web of Science ®Google Scholar
• Q. Tian, P. Zhang, and B. Li, “Towards predicting the best answers in community-based question-answering services,” in ICWSM, 2013, pp. 725–728.
   Google Scholar
• I. Rish, “An empirical study of the naive Bayes classifier,” in IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001, pp. 41–46. IBM volume 3.
   Google Scholar
• J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Stat., Vol. 29, no. 5, pp. 1189–1232, 2001.
   Web of Science ®Google Scholar
• L. Breiman, “Random forests,” Mach. Learn., Vol. 45, pp. 5–32, 2001. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” J. Artif. Intell. Res., Vol. 16, pp. 321–357, 2002. doi:10.1613/jair.953
   Web of Science ®Google Scholar
• H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” in 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, pp. 1322–1328. IEEE.
   Google Scholar
• J. Davis, and M. Goadrich, “The relationship between precision-recall and roc curves,” in Proceedings of the 23rd International Conference on Machine Learning ICML ‘06, 2006, pp. 233–240. ACM
   Google Scholar
• L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza, and D. Fullerton, “Improving low quality stack overflow post detection,” in Software Maintenance and Evolution (ICSME), 2014 IEEE International Conference on, 2014, pp. 541–544. IEEE.
   Google Scholar