Estimating sharer reputation via social data calibration

Jaewon Yang, Bee-Chung Chen, D. Agarwal
{"title":"Estimating sharer reputation via social data calibration","authors":"Jaewon Yang, Bee-Chung Chen, D. Agarwal","doi":"10.1145/2487575.2487685","DOIUrl":null,"url":null,"abstract":"Online social networks have become important channels for users to share content with their connections and diffuse information. Although much work has been done to identify socially influential users, the problem of finding \"reputable\" sharers, who share good content, has received relatively little attention. Availability of such reputation scores can be useful or various applications like recommending people to follow, procuring high quality content in a scalable way, creating a content reputation economy to incentivize high quality sharing, and many more. To estimate sharer reputation, it is intuitive to leverage data that records how recipients respond (through clicking, liking, etc.) to content items shared by a sharer. However, such data is usually biased --- it has a selection bias since the shared items can only be seen and responded to by users connected to the sharer in most social networks, and it has a response bias since the response is usually influenced by the relationship between the sharer and the recipient (which may not indicate whether the shared content is good). To correct for such biases, we propose to utilize an additional data source that provides unbiased goodness estimates for a small set of shared items, and calibrate biased social data through a novel multi-level hierarchical model that describes how the unbiased data and biased data are jointly generated according to sharer reputation scores. The unbiased data also provides the ground truth for quantitative evaluation of different methods. Experiments based on such ground-truth data show that our proposed model significantly outperforms existing methods that estimate social influence using biased social data.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2487575.2487685","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

Online social networks have become important channels for users to share content with their connections and diffuse information. Although much work has been done to identify socially influential users, the problem of finding "reputable" sharers, who share good content, has received relatively little attention. Availability of such reputation scores can be useful or various applications like recommending people to follow, procuring high quality content in a scalable way, creating a content reputation economy to incentivize high quality sharing, and many more. To estimate sharer reputation, it is intuitive to leverage data that records how recipients respond (through clicking, liking, etc.) to content items shared by a sharer. However, such data is usually biased --- it has a selection bias since the shared items can only be seen and responded to by users connected to the sharer in most social networks, and it has a response bias since the response is usually influenced by the relationship between the sharer and the recipient (which may not indicate whether the shared content is good). To correct for such biases, we propose to utilize an additional data source that provides unbiased goodness estimates for a small set of shared items, and calibrate biased social data through a novel multi-level hierarchical model that describes how the unbiased data and biased data are jointly generated according to sharer reputation scores. The unbiased data also provides the ground truth for quantitative evaluation of different methods. Experiments based on such ground-truth data show that our proposed model significantly outperforms existing methods that estimate social influence using biased social data.
通过社会数据校准估计共享者声誉
在线社交网络已经成为用户与好友分享内容、传播信息的重要渠道。尽管在识别有社会影响力的用户方面已经做了很多工作,但寻找分享优质内容的“有信誉的”分享者的问题却相对较少受到关注。这种声誉评分的可用性对各种应用程序都很有用,比如推荐值得关注的人、以可扩展的方式获取高质量的内容、创建内容声誉经济以激励高质量的分享等等。为了估计分享者的声誉,利用记录接收者如何回应(通过点击、点赞等)的数据是很直观的。然而,这样的数据通常是有偏差的——它有选择偏差,因为在大多数社交网络中,共享的项目只能被连接到分享者的用户看到和回应,它有响应偏差,因为响应通常受到分享者和接受者之间关系的影响(这可能不能表明共享的内容是否好)。为了纠正这种偏差,我们建议利用一个额外的数据源,为一小部分共享项目提供无偏优度估计,并通过一个新的多层次层次模型来校准有偏的社会数据,该模型描述了如何根据共享者声誉分数共同生成无偏数据和有偏数据。无偏数据也为不同方法的定量评价提供了基础真理。基于这些基本事实数据的实验表明,我们提出的模型明显优于使用有偏见的社会数据估计社会影响的现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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