理解社交网络中的转发行为

Zi Yang, Jingyi Guo, Keke Cai, Jie Tang, Juan-Zi Li, Li Zhang, Zhong Su
{"title":"理解社交网络中的转发行为","authors":"Zi Yang, Jingyi Guo, Keke Cai, Jie Tang, Juan-Zi Li, Li Zhang, Zhong Su","doi":"10.1145/1871437.1871691","DOIUrl":null,"url":null,"abstract":"Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"265","resultStr":"{\"title\":\"Understanding retweeting behaviors in social networks\",\"authors\":\"Zi Yang, Jingyi Guo, Keke Cai, Jie Tang, Juan-Zi Li, Li Zhang, Zhong Su\",\"doi\":\"10.1145/1871437.1871691\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.\",\"PeriodicalId\":310611,\"journal\":{\"name\":\"Proceedings of the 19th ACM international conference on Information and knowledge management\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"265\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 19th ACM international conference on Information and knowledge management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1871437.1871691\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th ACM international conference on Information and knowledge management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1871437.1871691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 265

摘要

转发是Twitter上的一种重要动作(行为),指用户转发好友的微博的行为。在挖掘用户生成的文本内容或分析社交网络结构方面已经做了很多工作,但很少有出版物系统地研究转发行为的潜在机制。在本文中,我们对Twitter上的问题进行了有趣的分析。我们发现,几乎25.5%的用户发布的推文实际上是从朋友的博客空间转发的。我们的调查发现,对于转发行为,一些统计数据仍然遵循幂律分布,而另一些统计数据则违反了Web的最新状态分布。基于这些重要的观察结果,我们提出了一个因子图模型来预测用户的转发行为。在Twitter数据集上的实验结果表明,该方法预测转发行为的准确率为28.81%,召回率为37.33%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding retweeting behaviors in social networks
Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.
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:604180095
Book学术官方微信