一种在线社交网络的自适应观点引导模型及实现

doi:10.3969/j.issn.0372-2112.2016.07.028

电子学报 ›› 2016, Vol. 44 ›› Issue (7): 1714-1720.DOI: 10.3969/j.issn.0372-2112.2016.07.028

一种在线社交网络的自适应观点引导模型及实现

许文文¹, 时鹏², 于留宝³, 胡长军¹

1. 北京科技大学计算机与通信工程学院, 北京 100083;
2. 北京科技大学国家材料服役安全科学中心, 北京 100083;
3. 微梦创科网络科技(中国)有限公司, 北京 100080

收稿日期:2015-02-04 修回日期:2015-05-07 出版日期:2016-07-25
- 通讯作者:
- 时鹏
- 作者简介:
- 许文文 女,1989年5月出生于山西临汾.北京科技大学博士研究生,研究方向为社交网络中信息传播分析.E-mail:xuwenwenustb@163.com
- 基金资助:
- 国家973重点基础研究发展计划 (No.2013CB329605)

An Adaptive Opinion Guiding Model for Online Social Networks

XU Wen-wen¹, SHI Peng², YU Liu-bao³, HU Chang-jun¹

1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China;
3. The Micro Dreaming of Beijing Network Technology Company Limited, Beijing 100080, China

Received:2015-02-04 Revised:2015-05-07 Online:2016-07-25 Published:2016-07-25
- Supported by:
- National Program on Key Basic Research Project of China (973 Program) (No.2013CB329605)

摘要/Abstract

摘要：

在线社交网络已成为信息传播的重要途径,给人们获取信息带来便利的同时,也为不良信息的扩散提供了温床.目前主流的在线社交网络都采用关键字匹配的方式屏蔽不良信息的发布,在阻止信息本身的同时,也屏蔽了与其相关的积极观点的传播.本文提出一种自适应的观点引导模型,实现对在线社交网络用户的观点引导.该模型首先分析网络用户对事件/事物的观点和情感倾向,确定其中观点消极的用户作为引导对象,然后向其推送与之关注点相近且情感相对积极的信息或用户,进行观点引导,同时引入反馈机制,根据引导对象的观点变化动态调整推送内容,以实现长期精确引导.基于该模型设计并实现了观点引导系统,包括引导信息模块、观点标注模块、推荐模块和反馈模块,实现了自动选择群体、自动识别群体情感倾向、自动选择和调整推送内容等功能.实验结果表明,该模型能够实现对在线社交网络用户的观点引导.

关键词: 在线社交网络, 观点引导, 情感分析, 内容推荐

Abstract:

Online social networks are now recognized as an important platform for the spread of information. While providing convenient exchange for users,it also makes OSNs fertile grounds for the wide spread of misinformation which can lead to undesirable consequences.Most mainstream media outlets use keyword matching as a search method to find misinformation and forbid the presentation in context.However,this method also blocks positive messages related to misinformation.In this paper,we propose an adaptive opinion guiding model to limit the spread of misinformation.Based on user's opinion and sentiment,the model recommends messages or other users that have relative positive feeling to current user.It also introduces the feedback mechanism to achieve a long-term and accurate guiding by adjusting the pushing content dynamically.We also design and finish the guiding system.Experiments show that the model can guide the opinion of the network group effectively.

Key words: online social networks, opinion guiding, sentiment analysis, content-based recommendation

中图分类号:

TP393.4

许文文, 时鹏, 于留宝, 等. 一种在线社交网络的自适应观点引导模型及实现[J]. 电子学报, 2016, 44(7): 1714-1720.

XU Wen-wen, SHI Peng, YU Liu-bao, et al. An Adaptive Opinion Guiding Model for Online Social Networks[J]. Acta Electronica Sinica, 2016, 44(7): 1714-1720.

参考文献

[1] Lorenz J.Continuous opinion dynamics under bounded confidence:A survey[J].Inte rnational Journal of Modern Physics C,2007,18(12):1819-1838.
[2] Splichal S.Public Opinion:Developments and Controversies in the Twentieth Century[M].USA:Rowman & Littlefield,1999.
[3] Foyle D C.Leading the public to war? The influence of American public opinion on the Bush administration's decision to go to war in Iraq[J].International Journ al of Public Opinion Research,2004,16(3):269-294.
[4] 陈力丹.舆论学:舆论导向研究[M].中国广播电视出版社,1999.
[5] 刘毅.网络舆情研究概论[M].天津:天津人民出版社,2007.
[6] 赵文晶,刘军宏.微博舆论引导策略研究[J].中国出版,2011,(12):12-15.
[7] 崔鑫,胡勇,丁雪峰,等.基于SNA点度的网络舆论导向机理研究[J].四川大学学报:工程科学版,2011,43(1):104-108.
[8] Su X,Khoshgoftaar T M.A survey of collaborative filtering techniques[J].Advanc es in Artificial Intelligence,2009,2009(1):421425_-1-421425_-19.
[9] Agarwal D,Chen B C.Regression-based latent factor models[A].Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Minin g[C].USA:ACM,2009.19-28.
[10] 谢海涛,孟祥武.适应用户需求进化的个性化信息服务模型[J].电子学报,2011,39(3):643-648. XIE Hai-tao,MENG Xiang-wu.A personalized information service model adapting to user requirement evolution[J].Acta Electronica Sinica,2011,39(3):643-648.(in Chinese)
[11] Pang B,Lee L.Opinion mining and sentiment analysis[J].Foundations and Trends in Information Retrieval,2008,2(1-2):1-135.
[12] Liu B.Sentiment analysis and subjectivity[A].Handbook of Natural Language Proc essing[C].USA:Taylor and Francis Group,2010.627-666.
[13] 赵妍妍,秦兵,刘挺.文本情感分析[J].软件学报,2010,21(8):1834-1848. ZHAO Yan-Yan,QIN Bing,LIU Ting.Sentiment analysis[J].Journal of Software,2010,21(8):1834-1848.(in Chinese)
[14] Pang B,Lee L,Vaithyanathan S.Thumbs up?:Sentiment classification using machine learning techniques[A].Proceedings of the ACL-02 Conference on Empirical Metho ds in Natural Language Processing(Volume 10)[C].USA:Association for Computation al Linguistics,2002.79-86.
[15] Turney P D.Thumbs up or thumbs down?:semantic orientation applied to unsupervise dclassification of reviews[A].Proceedings of the 40th Annual Meeting on Assoc iation for Computational Linguistics[C].USA:Association for Computational Ling uistics,2002.417-424.
[16] Thelwall M,Buckley K,Paltoglou G,et al.Sentiment strength detection in short inf ormal text[J].Journal of the American Society for Information Science and Tech nology,2010,61(12):2544-2558.
[17] Armentano M G,Godoy D,Amandi A A.Followee recommendation based on text analysis of micro-blogging activity[J].Information Systems,2013,38(8):1116-1127.
[18] Hannon J,Bennett M,Smyth B.Recommending twitter users tofollow using content an dcollaborative filtering approaches[A].Proceedings of the Fourth ACM Conferen ce on Recommender Systems[C].USA:ACM,2010.199-206.
[19] Chin A.Finding cohesive subgroups and relevant members in the nokia friend view mobile social network[A].Proceedings of Computational Science and Engineering[C].USA:IEEE,2009.278-283.
[20] Shen D,Sun J T,Yang Q,et al.Latent friend mining from blog data[A].Proceedings of the Sixth International Conference on Data Mining[C].USA:IEEE,2006.552-561.
[21] Gurini D F,Gasparetti F,Micarelli A,et al.A sentiment-based approach to twitter user recommendation[A].Proceedings of RSWeb@RecSys[C].Hong Kong:Technology, Health & Medicine,2013.8401-8404.
[22] Oard D W,Kim J.Implicit feedback for recommender systems[A].Proceedings of the AAAI Workshop on Recommender Systems[C].USA:Wollongong,1998.81-83.
[23] Hu Y,Koren Y,Volinsky C.Collaborative filtering for implicit feedback datasets[A].Proceedings of the Eighth IEEE International Conference on Data Mining[C]. USA:IEEE,2008.263-272.
[24] Ostuni V C,Di Noia T,Di Sciascio E,et al.Top-N recommendations from implicit fe edback leveraging linked open data[A].Proceedings of the 7th ACM Conference on Recommender Systems[C].USA:ACM,2013.85-92.
[25] 刘胜宗,廖志芳,胡佳,等.基于隐反馈的类时齐Markov推荐模型[J].电子学报,2014,42(4):703-710. LIU Sheng-zong,LIAO Zhi-fang,HU Jia,et al.Classified time homogeneous Markov model for recommendation based on implicit feedback[J].Acta Electronic a Sinica,2014,42(4):703-710.(in Chinese)
[26] Yang S H,Long B,Smola A,et al.Like like alike:joint friendship and interest prop agation in social networks[A].Proceedings of the 20th International Conference on World Wide Web[C].USA:ACM,2011.537-546.

一种在线社交网络的自适应观点引导模型及实现

An Adaptive Opinion Guiding Model for Online Social Networks

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