微博演化网络的负信息分类方法

TP393.092; 针对Sina微博博文的转发关系,建立起用户转发博文之间的演化网络,从而利用SMO SVM(sequential minimal optimization support vector machine)分类算法对博文进行分类,筛选出恶意博文、垃圾广告、垃圾营销信息,使用户能够精确地屏蔽不想要的博文和博主.第一步基于微博转发关系的演化网络和SVM分类算法对整个Sina微博进行分类;第二步利用复杂网络等技术对经常发送恶意广告的博主进行标注,从而在网络中对他们进行屏蔽;最后找出垃圾信息的来源以及分辨出博主是不是恶意转发者,在宏观上能更好地遏制垃圾信息的传播.与用户从UCI数据集中...

Full description

Saved in:
Bibliographic Details
Published in计算机科学与探索 Vol. 11; no. 1; pp. 91 - 98
Main Authors 赵一, 何克清, 李昭, 黄贻望
Format Journal Article
LanguageChinese
Published 武汉大学计算机学院软件工程国家重点实验室,武汉,430072%三峡大学计算机与信息技术学院,湖北宜昌,443002 2017
Subjects
evolutionary network
UCI data set
negative information
sequential minimal optimization (SMO)
序列最小优化(SMO)
负信息
support vector machine (SVM)
演化网络
UCI数据集
支持向量机(SVM)
Online AccessGet full text
ISSN1673-9418
DOI10.3778/j.issn.1673-9418.1509090

Cover

Abstract TP393.092; 针对Sina微博博文的转发关系,建立起用户转发博文之间的演化网络,从而利用SMO SVM(sequential minimal optimization support vector machine)分类算法对博文进行分类,筛选出恶意博文、垃圾广告、垃圾营销信息,使用户能够精确地屏蔽不想要的博文和博主.第一步基于微博转发关系的演化网络和SVM分类算法对整个Sina微博进行分类;第二步利用复杂网络等技术对经常发送恶意广告的博主进行标注,从而在网络中对他们进行屏蔽;最后找出垃圾信息的来源以及分辨出博主是不是恶意转发者,在宏观上能更好地遏制垃圾信息的传播.与用户从UCI数据集中实际反馈情况进行比较,实验结果表明,机器学习分类的实验结果吻合度达到89％.
AbstractList TP393.092; 针对Sina微博博文的转发关系,建立起用户转发博文之间的演化网络,从而利用SMO SVM(sequential minimal optimization support vector machine)分类算法对博文进行分类,筛选出恶意博文、垃圾广告、垃圾营销信息,使用户能够精确地屏蔽不想要的博文和博主.第一步基于微博转发关系的演化网络和SVM分类算法对整个Sina微博进行分类;第二步利用复杂网络等技术对经常发送恶意广告的博主进行标注,从而在网络中对他们进行屏蔽;最后找出垃圾信息的来源以及分辨出博主是不是恶意转发者,在宏观上能更好地遏制垃圾信息的传播.与用户从UCI数据集中实际反馈情况进行比较,实验结果表明,机器学习分类的实验结果吻合度达到89％.
Abstract_FL Aiming at the relationship of the Sina micro blogging,this paper establishes the evolving network by user's transmit blog,which classifies blog by SMO SVM (sequential minimal optimization support vector machine) algorithm,and implements the classification of malicious posts,spam,trash marketing information.The method enables users to accurately block the unwanted posts and blogger.The first step,classifying the entire Sina micro blogs based on the evolving network of transmit relationship and SVM classification algorithm;The second step,annotating the bloggers of often sending malicious advertisements by using the complex network technology;When the malicious bloggers sending message,blocking them in the network;Finally,finding out the source of spam,and discerning the blogger malicious or not,on the macro to better curb the spread of spam.The results of this paper are compared with user feedback actual situation from the UCI data set,the experimental results of machine learning classification reaches 89％.
Author 黄贻望
赵一
李昭
何克清
AuthorAffiliation 武汉大学计算机学院软件工程国家重点实验室,武汉,430072%三峡大学计算机与信息技术学院,湖北宜昌,443002
AuthorAffiliation_xml – name: 武汉大学计算机学院软件工程国家重点实验室,武汉,430072%三峡大学计算机与信息技术学院,湖北宜昌,443002
Author_FL HUANG Yiwang
HE Keqing
ZHAO Yi
LI Zhao
Author_FL_xml – sequence: 1
  fullname: ZHAO Yi
– sequence: 2
  fullname: HE Keqing
– sequence: 3
  fullname: LI Zhao
– sequence: 4
  fullname: HUANG Yiwang
Author_xml – sequence: 1
  fullname: 赵一
– sequence: 2
  fullname: 何克清
– sequence: 3
  fullname: 李昭
– sequence: 4
  fullname: 黄贻望
BookMark eNo9jbtKA0EYRqeIYIx5B1uFXeef2bmVErxBwCZ9mJ3ZkawyAUdRS0HEQgtBUwRB8gIR0Ubi5WXcXR_DBUVO8cEpvrOAGn7oM4SWAMdUCLmax4MQfAxc0EglIGNgWNU0UPPfzaN2CIMUsyQhILhsopXiY1pcj8u32-JqVL3fVLP7anz-_fLw9Tkpzx6Ly4vqaVaOXsvnu0U05_R-yNp_20K9jfVeZyvq7mxud9a6keFERsYqzhXG0hJhGNMCOBFaGUUIZi6R2BoNqdNYZJCB49xSRrjVzAEHQlPaQsu_t8faO-13-_nw6MDXwX4e8r2T08NAMAgMdYL-AJFgVo8
ClassificationCodes TP393.092
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.3778/j.issn.1673-9418.1509090
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
DocumentTitle_FL Micro Blog Evolutionary Network to Classification Method of Negative Information
EndPage 98
ExternalDocumentID jsjkxyts201701008
GrantInformation_xml – fundername: The National Basic Research Program of China under Grant No.2014CB340401(国家重点基础研究发展计划
  funderid: (973计划))
GroupedDBID 2B.
4A8
92I
93N
ALMA_UNASSIGNED_HOLDINGS
M~E
PSX
TCJ
ID FETCH-LOGICAL-c628-cd9669008d27c55a71627a9c92205f480dca1bfa07e1e1f66d3526da5f16123b3
ISSN 1673-9418
IngestDate Thu May 29 04:00:17 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords evolutionary network
UCI data set
negative information
sequential minimal optimization (SMO)
序列最小优化(SMO)
负信息
support vector machine (SVM)
演化网络
UCI数据集
支持向量机(SVM)
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c628-cd9669008d27c55a71627a9c92205f480dca1bfa07e1e1f66d3526da5f16123b3
PageCount 8
ParticipantIDs wanfang_journals_jsjkxyts201701008
PublicationCentury 2000
PublicationDate 2017
PublicationDateYYYYMMDD 2017-01-01
PublicationDate_xml – year: 2017
  text: 2017
PublicationDecade 2010
PublicationTitle 计算机科学与探索
PublicationTitle_FL Journal of Frontiers of Computer Science & Technology
PublicationYear 2017
Publisher 武汉大学计算机学院软件工程国家重点实验室,武汉,430072%三峡大学计算机与信息技术学院,湖北宜昌,443002
Publisher_xml – name: 武汉大学计算机学院软件工程国家重点实验室,武汉,430072%三峡大学计算机与信息技术学院,湖北宜昌,443002
SSID ssib054421768
ssib002040941
ssib002423894
ssib051375751
ssib023646573
ssib036438069
ssib002040926
Score 2.085908
Snippet TP393.092; 针对Sina微博博文的转发关系,建立起用户转发博文之间的演化网络,从而利用SMO SVM(sequential minimal optimization support vector machine)分类算法对博文进...
SourceID wanfang
SourceType Aggregation Database
StartPage 91
Title 微博演化网络的负信息分类方法
URI https://d.wanfangdata.com.cn/periodical/jsjkxyts201701008
Volume 11
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  issn: 1673-9418
  databaseCode: M~E
  dateStart: 20070101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://road.issn.org
  omitProxy: true
  ssIdentifier: ssib054421768
  providerName: ISSN International Centre
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JaxRBFG5CvHgRRcWdINZF6NjVXeuxu6eH4MGLEXILPb0oEUYwE9AcBEHEgx4EzSEI4h9QRC8Slz_jzPgzfO9VT8-YREmEoXldb6n31Stq6anF866YXNZKldyvS1X5gofKz6Oq9K0MCql5T5QFrba4oZZuiesrcmVu_ubMqqWNQW-x2Nx3X8n_RBXSIK64S_YQkW2NQgLQEF94QoTheaAYs0yyJGNxhoTpMBuzTLEkZVZQChCKZZolwOJEJMymSICkARnDEsFsl2WCJV0Wc1Q3nMVdUjfMOHWOisACa4mlLCLmLq2cjGvRFLgRUy5AWE3yKUtiStHkgGRxh8WKsgPjGWUHWiHlIoCYxJ9ck_hrZIMpRxAeSS7CjzxDETkVgaw7jX0LjnWmHItQWugNLCinZPbzh9vnSVUV-Y3TCgvCWIIhENIUzz_AtzKWWUtQDfofu0JPWKIoiqAoSd0QIgkeoRiqZyRjmdEYY5AxIYWBWDZDVhxjsF0K-Bam-7gdpiLCE9xZ2BYqGYFYxvyQoNr47a03QMS4fMYJu5q0qwScewDBqkk11RM4aRMzSASH0eOZ79Jc6ci3oulKJx0r39OAuF7S3c_WjLfcJeS7e_JIa0M9OZpfbM0vwvzFBu6C2V3npK-tr9198HCwjpUk4HQAwJEQP9Lhet5H2XRMCt2WnZ1T47v4Y3M3DOLbTgovWFByOsaH18gEqp0DSB5p_O-yfRcCZvluC-7Ea7eCECFd-xsg2jPYr_P-7Znh7fJx71gzL12IXSNzwpvbvHPSuzr8_n74Ynv09dXw-db428vxzpvx9pNfn9_-_PFu9PjD8NnT8ced0daX0afXp7zlbracLvnN5Sp-oULjF6VVykIplaEupMzxIDmd28LixvtamKAsct6r80BXvOLYnONFGiW07BwPbOpFp735_r1-dcZbqIK8qqtaA9cIkOiVkldV1AthdqHz0Jz1LjfQVpu2c311T7DOHUTovHcUafcF9II3P7i_UV2EOcGgd4li_BvBY7j7
linkProvider ISSN International Centre
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%BE%AE%E5%8D%9A%E6%BC%94%E5%8C%96%E7%BD%91%E7%BB%9C%E7%9A%84%E8%B4%9F%E4%BF%A1%E6%81%AF%E5%88%86%E7%B1%BB%E6%96%B9%E6%B3%95&rft.jtitle=%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8E%A2%E7%B4%A2&rft.au=%E8%B5%B5%E4%B8%80&rft.au=%E4%BD%95%E5%85%8B%E6%B8%85&rft.au=%E6%9D%8E%E6%98%AD&rft.au=%E9%BB%84%E8%B4%BB%E6%9C%9B&rft.date=2017&rft.pub=%E6%AD%A6%E6%B1%89%E5%A4%A7%E5%AD%A6%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%AD%A6%E9%99%A2%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B%E5%9B%BD%E5%AE%B6%E9%87%8D%E7%82%B9%E5%AE%9E%E9%AA%8C%E5%AE%A4%2C%E6%AD%A6%E6%B1%89%2C430072%25%E4%B8%89%E5%B3%A1%E5%A4%A7%E5%AD%A6%E8%AE%A1%E7%AE%97%E6%9C%BA%E4%B8%8E%E4%BF%A1%E6%81%AF%E6%8A%80%E6%9C%AF%E5%AD%A6%E9%99%A2%2C%E6%B9%96%E5%8C%97%E5%AE%9C%E6%98%8C%2C443002&rft.issn=1673-9418&rft.volume=11&rft.issue=1&rft.spage=91&rft.epage=98&rft_id=info:doi/10.3778%2Fj.issn.1673-9418.1509090&rft.externalDocID=jsjkxyts201701008
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjsjkxyts%2Fjsjkxyts.jpg