建模专家的行为在问答社区预测有价值的讨论

T. B. Procaci, S. Siqueira, B. Nunes, Terhi Nurmikko-Fuller
{"title":"建模专家的行为在问答社区预测有价值的讨论","authors":"T. B. Procaci, S. Siqueira, B. Nunes, Terhi Nurmikko-Fuller","doi":"10.1109/ICALT.2017.56","DOIUrl":null,"url":null,"abstract":"This paper investigates expert behaviour in Q&A communities in order to understand their influence in online discussions. Our evaluation shows that experts are more likely to provide help than non-experts, and when they participate in a discussion, the quality and length of the discussions tend to increase. In addition, we propose the usage of two models (Artificial Neural Network and Stochastic Gradient Boosting) to predict worthy discussions in the community. The results show that some adjustments in the models' parameters and in the input data can significantly improve the quality of the predictions.","PeriodicalId":134966,"journal":{"name":"2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Modelling Experts Behaviour in Q&A Communities to Predict Worthy Discussions\",\"authors\":\"T. B. Procaci, S. Siqueira, B. Nunes, Terhi Nurmikko-Fuller\",\"doi\":\"10.1109/ICALT.2017.56\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates expert behaviour in Q&A communities in order to understand their influence in online discussions. Our evaluation shows that experts are more likely to provide help than non-experts, and when they participate in a discussion, the quality and length of the discussions tend to increase. In addition, we propose the usage of two models (Artificial Neural Network and Stochastic Gradient Boosting) to predict worthy discussions in the community. The results show that some adjustments in the models' parameters and in the input data can significantly improve the quality of the predictions.\",\"PeriodicalId\":134966,\"journal\":{\"name\":\"2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICALT.2017.56\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2017.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

本文调查了问答社区中的专家行为,以了解他们在在线讨论中的影响。我们的评估表明,专家比非专家更有可能提供帮助,当他们参与讨论时,讨论的质量和长度往往会增加。此外,我们建议使用两种模型(人工神经网络和随机梯度增强)来预测社区中有价值的讨论。结果表明,对模型参数和输入数据进行调整可以显著提高预测质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling Experts Behaviour in Q&A Communities to Predict Worthy Discussions
This paper investigates expert behaviour in Q&A communities in order to understand their influence in online discussions. Our evaluation shows that experts are more likely to provide help than non-experts, and when they participate in a discussion, the quality and length of the discussions tend to increase. In addition, we propose the usage of two models (Artificial Neural Network and Stochastic Gradient Boosting) to predict worthy discussions in the community. The results show that some adjustments in the models' parameters and in the input data can significantly improve the quality of the predictions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信