新浪微博反垃圾中特征选择的重要性分析

张宇翔; 孙菀; 杨家海; 周达磊; 孟祥飞; 肖春景

doi:10.11959/j.issn.1000-436x.2016152

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新浪微博反垃圾中特征选择的重要性分析

学术论文 | 更新时间：2024-10-11

- 新浪微博反垃圾中特征选择的重要性分析
- Feature importance analysis for spammer detection in Sina Weibo
- 通信学报 2016年37卷第8期页码：24-33
- 作者机构：
  
  1. 中国民航大学计算机科学与技术学院，天津 300300
  2. 清华大学网络科学与网络空间研究院，北京 100084
  3. 清华信息科学与技术国家实验室，北京 100084
  4. 北京邮电大学网络技术研究院，北京 100876
  5. 北京航空航天大学虚拟现实技术与系统国家重点实验室，北京 100876
- 作者简介：
  
  [ "张宇翔（1975-），男，山西五寨人，博士，中国民航大学副教授，主要研究方向为社会网络分析、推荐技术。" ]
  [ "孙菀（1991-），女，山东烟台人，中国民航大学硕士生，主要研究方向为社会网络分析与推荐技术。" ]
  [ "杨家海（1966-），男，浙江云和人，清华大学教授、博士生导师，主要研究方向为计算机网络管理与测量、云计算与大数据等。" ]
  [ "周达磊（1992-），男，江苏连云港人，北京邮电大学硕士生，主要研究方向为网络分析。" ]
  [ "孟祥飞（1993-），男，山西太原人，北京航空航天大学硕士生，主要研究方向为数据分析技术。" ]
  [ "肖春景（1978-），女，河北唐山人，中国民航大学讲师，主要研究方向为数据挖掘与推荐系统。" ]
- 基金信息：
  
  国家重点基础研究发展计划（“973”计划）基金资助项目;The National Basic Research Program of China (973 Program)(2009CB320505);国家科技支撑计划基金资助项目;The National Science and Technology Supporting Plan of China(2008BAH37B05);国家自然科学基金资助项目;The National Natural Science Foundation of China(61170211);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1533104);国家自然科学基金资助项目;The National Natural Science Foundation of China(61301245);教育部博士学科专项基金资助项目;The Specialized Research Fund for the Doctoral Program of Higher Education(20110002110056)
- DOI：10.11959/j.issn.1000-436x.2016152
  中图分类号： TP391
- 网络首发：2016-08，
  
  纸质出版：2016-08-25
- 稿件说明：
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张宇翔, 孙菀, 杨家海, 等. 新浪微博反垃圾中特征选择的重要性分析[J]. 通信学报, 2016,37(8):24-33.

Yu-xiang ZHANG, Yu SUN, Jia-hai YANG, et al. Feature importance analysis for spammer detection in Sina Weibo[J]. Journal on Communications, 2016, 37(8): 24-33.
张宇翔, 孙菀, 杨家海, 等. 新浪微博反垃圾中特征选择的重要性分析[J]. 通信学报, 2016,37(8):24-33. DOI： 10.11959/j.issn.1000-436x.2016152.

Yu-xiang ZHANG, Yu SUN, Jia-hai YANG, et al. Feature importance analysis for spammer detection in Sina Weibo[J]. Journal on Communications, 2016, 37(8): 24-33. DOI： 10.11959/j.issn.1000-436x.2016152.

摘要

微博中的垃圾用户非常普遍，其异常行为及生产的垃圾信息显著降低了用户体验。为了提高识别准确率，已有研究或是尽可能多地定义特征，或是不断尝试提出新的分类检测方法；那么，微博反垃圾问题的突破点优先置于寻找分类特征还是改进分类检测方法，是否特征越多检测效果越好，新的方法是否可以显著提高检测效果。以新浪微博为例，试图通过不同的特征选择方法与不同的分类器组合实验回答以上问题，实验结果表明特征组的选择较分类器的改进更为重要，需从内容信息、用户行为和社会关系多侧面生成特征，且特征并非越多检测效果越好，这些结论将有助于未来微博反垃圾工作的突破。

Abstract

Microblog has drawn attention of not only legitimate users but also spammers.The garbage information pro-vided by spammers handicaps users' experience significantly.In order to improve the detection accuracy of spammers

most existing studies on spam focus on generating more classification features or putting forward new classifiers.Which kind of issues would be put the high priority of an enormous amount of research effort into? Are extensive features or novel classifiers better for the detection accuracy of spammers? It is tried to address these questions through combining different feature selection methods with different classifiers on a real Sina Weibo dataset.Experimental results show that selected features are more important than novel classifiers for spammer detection.In addition

features should be derived from a wide range

such as text contents

user behaviors

and social relationship

and the dimension of features should not be too high.These results will be useful in finding the breakpoint of Microblog anti-spam works in the future.

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Keywords

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