融合热点话题的微博转发预测研究

陈 江,刘 玮,巢文涵,王丽宏

PDF(4066 KB)
PDF(4066 KB)
中文信息学报 ›› 2015, Vol. 29 ›› Issue (6) : 150-158.
综述

融合热点话题的微博转发预测研究

  • 陈 江1,刘 玮2,3,4,巢文涵1,王丽宏2
作者信息 +

Microblog Forwarding Prediction Based on Hot Topics

  • CHEN Jiang1, LIU Wei2,3,4, CHAO Wenhan1, WANG Lihong2
Author information +
History +

摘要

微博转发行为是实现信息传播的重要方式,微博转发预测对微博影响力分析、微博话题分析具有重要价值。现有微博转发预测研究大多围绕消息属性、用户属性等微博自身特征,该文提出融合热点话题的微博转发预测方法,对背景热点话题内容和传播趋势对用户转发行为的影响进行量化分析,提出融合背景热点信息的转发兴趣、转发活跃度、行为模式等特征,并基于分类算法建立了面向热点话题相关微博的转发预测模型,在真实数据上的实验结果表明,该方法的预测准确性达到96.6%,提升幅度最高达到12.14%。

Abstract

Microblog forwarding is an important way to the information dissemination, and microblog forwarding prediction is of great value in the analysis of microblog influence and microblog topic analysis. Existing methods of microblog forwarding prediction mostly focus on microblog and user attributes. In this paper, a microblog forwarding prediction method based on hot topics is proposed. We quantitatively analyze the impact of hot content and transmission tendency on users’ forwarding behavior, and then introduc features concerned with hot topics such as forwarding interest, forwarding activity and behavior pattern. Finally, we establish the hot topic oriented microblog forwarding prediction model based on the classification algorithm. Our experimental results on real data show that the accuracy of this method is 96.6%, and the max improvement of is 12.14%.
Key words microblog forward; forwarding prediction; hot topic
   
   
   

关键词

转发行为 / 转发预测 / 热点话题

Key words

microblog forward / forwarding prediction / hot topic

引用本文

导出引用
陈 江,刘 玮,巢文涵,王丽宏. 融合热点话题的微博转发预测研究. 中文信息学报. 2015, 29(6): 150-158
CHEN Jiang,LIU Wei,CHAO Wenhan,WANG Lihong. Microblog Forwarding Prediction Based on Hot Topics. Journal of Chinese Information Processing. 2015, 29(6): 150-158

参考文献

[1] KortLou. 微博(微型博客).百度百科. http://baike.baidu.com/link?url=Qsdt8nZWb5Q_iTpNaS41Wl-K2ZxMJeaUC8g9cuHWpK2V01Grlj6wiUx7C4170CT-m2988GAfKuQoMHuWdmq1V65C0zVgKyuU1qMYl-Z44yMBe_ , 2015-11-29
[2] Petrovic S, Osborne M, Lavrenko V. RT to Win! Predicting Message Propagation in Twitter[C]//Proceedings of the ICWSM. 2011.
[3] Galuba W, Aberer K, Chakraborty D, et al. Outtweeting the twitterers-predicting information cascades in microblogs[C]//Proceedings of the 3rd conference on Online social networks. 2010, 39(12): 3aAS3.
[4] 李英乐, 于洪涛, 刘力雄. 基于SVM的微博转发规模预测方法[J]. 计算机应用研究, 2013, 30(9): 2594-2597.
[5] 曹玖新, 吴江林, 石伟, 等. 新浪微博网信息传播分析与预测[J]. 计算机学报, 2014, 37(4): 779-790.
[6] Kanavos A, Perikos I, Vikatos P, et al. Modeling ReTweet Diffusion Using Emotional Content[M]. Artificial Intelligence Applications and Innovations. Springer Berlin Heidelberg, 2014: 101-110.
[7] Ma H, Qian W, Xia F, et al. Towards modeling popularity of microblogs[J]. Frontiers of Computer Science Selected Publications from Chinese Universities, 2013, 7(2):171-184.
[8] Ying-Le L I, Hong-Tao Y U, Liu L X. Predict algorithm of micro-blog retweet scale based on SVM[J]. Application Research of Computers, 2013, 30(9):2594-2597.
[9] Zhang Y, Rong L U, Yang Q. Predicting Retweeting in Microblogs[J]. Journal of Chinese Information Processing, 2012, 26(4):109-108.
[10] Pastor-Satorras R, Vespignani A. Epidemic dynamics and endemic states in complex networks[J]. Phys.rev.e, 2001, 63(6):138-158.
[11] Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks.[J]. Physical Review Letters, 2001, 86(14):3200-3203.
[12] Boyd D, Golder S, Lotan G. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter[C]//Proceedings of the Hawaii International Conference on. IEEE, 2010:1 - 10.
[13] Suh B, Hong L, Pirolli P, et al. Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network[C]//Proceedings of the 2010 IEEE Second International Conference on. IEEE, 2010:177-184.
[14] Yang Z, Guo J, Cai K, et al. Understanding retweeting behaviors in social networks[C]//Proceedings of the 19th ACM International fConference on Information and Knowledge Management. ACM, 2010:1633-1636.
[15] Jiang Y, Counts S. Predicting the Speed, Scale, and Range of Information Diffusion in Twitter[J]. ICWSM,2010,10:355-358.
[16] Hong L, Dan O, Davison B D. Predicting popular messages in twitter[C]//Proceedings of the 20th international conference companion on World wide web. ACM, 2011: 57-58.
[17] Bandari R, Asur S, Huberman B A. The Pulse of News in Social Media: Forecasting Popularity[J]. Sixth International Aaai Conference on Weblogs & Social Media, 2012.
[18] Ma Z, Sun A, Cong G. On predicting the popularity of newly emerging hashtags in twitter[J]. Journal of the American Society for Information Science and Technology, 2013, 64(7): 1399-1410.
[19] Szabo G, Huberman B A. Predicting the popularity of online content[J]. Communications of the ACM, 2010, 53(8): 80-88.
[20] Yang J, Leskovec J. Modeling information diffusion in implicit networks[C]//Proceedings of the 2010 IEEE 10th International Conference on. IEEE, 2010: 599-608.
[21] Music0007. 兴趣. 百度百科. http://baike.baidu.com/subview/45281/8045345.htm#viewPageContent , 2015-11-30.
[22] 宗成庆.统计自然语言处理[M].北京: 清华大学出版社,2008.

基金

国家自然科学基金(61170230);国家科技支撑计划(2012BAH46B01);国家高技术研究发展计划(SS2014AA012303);国家高技术研究发展计划(863计划)(2014AA015105)
PDF(4066 KB)

Accesses

Citation

Detail

段落导航
相关文章

/