A Rumor Detection Model Based on Neural Network

doi:10.3969/j.issn.1671-1122.2017.09.031

Abstract

Abstract:

This paper proposes a blogger and micro-blog fusion attribute message text feature neural network recognition model. First, according to characteristics of blogger information: blogger category, blogger activity, blogger friends, fans, bloggers original news.Then micro-blog text representation vectors based on word2vec, and extracted the text feature by parallel convolutional neural network, according to the semantic content of text feature representation.Finally building BP neural network rumor detection model, the integration of micro-blog text semantic and micro-blog blogger as the model’s multi-feature to identify micro-blog news.

Key words: rumor detection, neural network, Weibo, deep learning

CLC Number:

TP399

Dagao DUAN, Yongheng XIE, Xinxin GAI, Zhanbin LIU. A Rumor Detection Model Based on Neural Network[J]. Netinfo Security, 2017, 17(9): 134-137.

Figures/Tables 2

References 14

[1]	FLANAGIN,ANDREW J, MIRIAM J M.Perceptions of Internet Information Credibility[J]. Journalism and Mass Communication Quarterly, 2010,77(3):515-540.
[2]	CARLOS C, MARCELO M,BARBARA P.Information Credibility on Twitter[C]// ACM . 20th International Conference on World Wide Web,March 28 - April 1, 2011,Hyderabad, India. New York:ACM, 2012:675-684.
[3]	MANISH G, ZHAO P X,HAN J W. Evaluating Event Credibility on Twitter[EB/Ol]. ,2017-4-15.
[4]	MENDOZA M,POBLETE B,CASTILLO C.Twitter Under Crisis: Can We Trust What We RT?[C]// ACM.1st Workshop on Social Media Analytics (SOMA'10), July 25 - 28, 2010,Washington D.C., District of Columbia .New York:ACM, 2010: 71-79.
[5]	CANINI K R, SUH B,PIROLLI P L.Finding Credible Information Sources in Social Networks Based on Content and Social Structure[C]//ACM. 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on SocialComputing, October 9-11,2011, Boston, MA, USA. New York:ACM, 2011:1-8.
[6]	MORRIS M R,COUNTS S,HOFF A,et al.Tweeting is Believing? Understanding Microblog Credibility Perceptions[C]//ACM.ACM 2012 conference on Computer Supported Cooperative Work, February 11 - 15, 2012,Seattle, Washington, USA. New York:ACM, 2013:441-450.
[7]	RAVIKUMAR S, BALAKRISHNAN R, KAMBHAMPATI S.Ranking Tweets Considering Trust and Relevance[C]//ACM.Proceedings of the Ninth International Workshop on Information Integration on the Web, May 20 , 2012 ,Scottsdale, Arizona, USA. New York:ACM, 2012: 4-8.
[8]	GUPTA A,KUMARAGURU P.Credibility Ranking of Tweets During High Impact Events[C]//ACM.Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, April 17 , 2012,Lyon, France.New York:ACM, 2012: 2-9.
[9]	张天宇, 林鸿飞. 多元表征军事信息可信度研究[J]. 计算机工程与科学, 2011.33(9):109-116.
[10]	汤志伟, 彭志华, 张会平. 公共危机中网络新闻可信度影响因素的结构方程模型研究[J]. 情报杂志, 2010(4):36-40.
[11]	钟诚, 赵志峰, 姚高峰. 语义环境中信息可信度计算方法研究[J]. 情报理论与实践, 2012(1):103-105.
[12]	贺刚,吕学强,李卓,等.微博谣言识别研究[J].图书情报工作,2013(23):114-120.
[13]	蒋盛益,陈东沂,庞观松,等. 微博信息可信度分析研究综述[J].图书情报工作, 2013(12):136-142.
[14]	罗梁,王文贤,钟杰,等. 跨社交网络的实体用户关联技术研究[J]. 信息网络安全,2017(2):51-58.

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