论提及在推特病毒式传播中的作用

Soumajit Pramanik, Qinna Wang, Maximilien Danisch, Sumanth Bandi, Anand Kumar, Jean-Loup Guillaume, Bivas Mitra
{"title":"论提及在推特病毒式传播中的作用","authors":"Soumajit Pramanik, Qinna Wang, Maximilien Danisch, Sumanth Bandi, Anand Kumar, Jean-Loup Guillaume, Bivas Mitra","doi":"10.1109/DSAA.2016.28","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate the role of mentions on tweet propagation. We propose a novel tweet propagation model SIR_MF based on a multiplex network framework, that allows to analyze the effects of mentioning on final retweet count. The basic bricks of this model are supported by a comprehensive study of multiple real datasets and simulations of the model show a nice agreement with the empirically observed tweet popularity. Studies and experiments also reveal that follower count, retweet rate & profile similarity are important factors in gaining tweet popularity and allow to better understand the impact of the mention strategies on the retweet count. Interestingly, we analytically identify a critical retweet rate regulating the role of mention on the tweet popularity. Finally, our data driven simulation demonstrates that the proposed mention recommendation heuristic \"Easy-Mention\" outperforms the benchmark \"Whom-To-Mention\" algorithm.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"On the Role of Mentions on Tweet Virality\",\"authors\":\"Soumajit Pramanik, Qinna Wang, Maximilien Danisch, Sumanth Bandi, Anand Kumar, Jean-Loup Guillaume, Bivas Mitra\",\"doi\":\"10.1109/DSAA.2016.28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we investigate the role of mentions on tweet propagation. We propose a novel tweet propagation model SIR_MF based on a multiplex network framework, that allows to analyze the effects of mentioning on final retweet count. The basic bricks of this model are supported by a comprehensive study of multiple real datasets and simulations of the model show a nice agreement with the empirically observed tweet popularity. Studies and experiments also reveal that follower count, retweet rate & profile similarity are important factors in gaining tweet popularity and allow to better understand the impact of the mention strategies on the retweet count. Interestingly, we analytically identify a critical retweet rate regulating the role of mention on the tweet popularity. Finally, our data driven simulation demonstrates that the proposed mention recommendation heuristic \\\"Easy-Mention\\\" outperforms the benchmark \\\"Whom-To-Mention\\\" algorithm.\",\"PeriodicalId\":193885,\"journal\":{\"name\":\"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSAA.2016.28\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSAA.2016.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

摘要

在本文中,我们研究了提及在tweet传播中的作用。我们提出了一种新的基于多路网络框架的推文传播模型SIR_MF,该模型允许分析提及对最终转发数的影响。该模型的基本组成部分得到了对多个真实数据集的全面研究的支持,模型的模拟与经验观察到的tweet流行度非常吻合。研究和实验还表明,关注者数量、转发率和个人资料相似度是获得推文受欢迎程度的重要因素,可以更好地理解提及策略对转发数的影响。有趣的是,我们通过分析确定了一个关键的转发率,它调节了提及对推文受欢迎程度的作用。最后,我们的数据驱动仿真表明,提出的启发式推荐“Easy-Mention”优于基准的“who - to - mention”算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
On the Role of Mentions on Tweet Virality
In this paper, we investigate the role of mentions on tweet propagation. We propose a novel tweet propagation model SIR_MF based on a multiplex network framework, that allows to analyze the effects of mentioning on final retweet count. The basic bricks of this model are supported by a comprehensive study of multiple real datasets and simulations of the model show a nice agreement with the empirically observed tweet popularity. Studies and experiments also reveal that follower count, retweet rate & profile similarity are important factors in gaining tweet popularity and allow to better understand the impact of the mention strategies on the retweet count. Interestingly, we analytically identify a critical retweet rate regulating the role of mention on the tweet popularity. Finally, our data driven simulation demonstrates that the proposed mention recommendation heuristic "Easy-Mention" outperforms the benchmark "Whom-To-Mention" algorithm.
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
期刊最新文献
A Multi-Granularity Pattern-Based Sequence Classification Framework for Educational Data Task Composition in Crowdsourcing Maritime Pattern Extraction from AIS Data Using a Genetic Algorithm What Did I Do Wrong in My MOBA Game? Mining Patterns Discriminating Deviant Behaviours Nonparametric Adjoint-Based Inference for Stochastic Differential Equations
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1