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基于时空特征的社交网络情绪传播分析与预测模型

熊熙 乔少杰 吴涛 吴越 韩楠 张海清

熊熙, 乔少杰, 吴涛, 吴越, 韩楠, 张海清. 基于时空特征的社交网络情绪传播分析与预测模型. 自动化学报, 2018, 44(12): 2290-2299. doi: 10.16383/j.aas.2018.c170480
引用本文: 熊熙, 乔少杰, 吴涛, 吴越, 韩楠, 张海清. 基于时空特征的社交网络情绪传播分析与预测模型. 自动化学报, 2018, 44(12): 2290-2299. doi: 10.16383/j.aas.2018.c170480
XIONG Xi, QIAO Shao-Jie, WU Tao, WU Yue, HAN Nan, ZHANG Hai-Qing. Spatio-temporal Feature Based Emotional Contagion Analysis and Prediction Model for Online Social Networks. ACTA AUTOMATICA SINICA, 2018, 44(12): 2290-2299. doi: 10.16383/j.aas.2018.c170480
Citation: XIONG Xi, QIAO Shao-Jie, WU Tao, WU Yue, HAN Nan, ZHANG Hai-Qing. Spatio-temporal Feature Based Emotional Contagion Analysis and Prediction Model for Online Social Networks. ACTA AUTOMATICA SINICA, 2018, 44(12): 2290-2299. doi: 10.16383/j.aas.2018.c170480

基于时空特征的社交网络情绪传播分析与预测模型

doi: 10.16383/j.aas.2018.c170480
基金项目: 

成都信息工程大学中青年学术带头人科研基金 J201701

成都市软科学研究项目 2017-RK00-00053-ZF

广西自然科学基金项目 2018GXNSFDA138005

国家自然科学基金 61802035

教育部人文社会科学研究青年基金 17YJCZH202

国家自然科学基金 61772091

成都市软科学研究项目 2017-RK00- 00125-ZF

成都信息工程大学科研基金 KYTZ201715

成都信息工程大学科研基金 KYTZ201750

四川高校科研创新团队建设计划 18TD0027

成都信息工程大学科研基金 KYTZ201637

四川省科技计划项目 2018GZ0253

广东省重点实验室项目 2017B030314073

四川省科技计划项目 2018JY0448

详细信息
    作者简介:

    熊熙   成都信息工程大学网络空间安全学院讲师.2013年获得四川大学信息安全专业博士学位.主要研究方向为web挖掘, 社会计算, 机器学习.E-mail:xiongxi@cuit.edu.cn

    吴涛   重庆邮电大学网络空间安全与信息法学院讲师.2017年获得电子科技大学计算机科学与工程学院博士学位.主要研究方向为数据挖掘.E-mail:wutaoadeny@gmail.com

    吴越   西华大学计算机与软件工程学院副教授.2014年获得四川大学信息安全专业博士学位.主要研究方向为数据挖掘, 复杂网络.E-mail:wuyue_xh@sina.com

    韩楠   成都信息工程大学管理学院讲师.2012年获得成都中医药大学博士学位.主要研究方向为数据挖掘.E-mail:hannan@cuit.edu.cn

    张海清  成都信息工程大学软件工程学院副研究员.2015年获得法国里昂第二大学博士学位.主要研究方向为智能信息处理与知识工程.E-mail:zhanghq@cuit.edu.cn

    通讯作者:

    乔少杰   成都信息工程大学网络空间安全学院教授.2009年获得四川大学计算机学院工学博士学位.主要研究方向为轨迹预测, 移动对象数据库, 大数据.本文通信作者.E-mail:sjqiao@cuit.edu.cn

Spatio-temporal Feature Based Emotional Contagion Analysis and Prediction Model for Online Social Networks

Funds: 

Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology J201701

Soft Science Foundation of Chengdu 2017-RK00-00053-ZF

Natural Science Foundation of Guangxi 2018GXNSFDA138005

National Natural Science Foundation of China 61802035

Youth Foundation for Humanities and Social Sciences of Ministry of Education of China 17YJCZH202

National Natural Science Foundation of China 61772091

Soft Science Foundation of Chengdu 2017-RK00- 00125-ZF

Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology KYTZ201715

Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology KYTZ201750

Innovative Research Team Construction Plan in Universities of Sichuan Province 18TD0027

Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology KYTZ201637

Sichuan Science and Technology Program 2018GZ0253

Guangdong Key Laboratory Project 2017B030314073

Sichuan Science and Technology Program 2018JY0448

More Information
    Author Bio:

       Lecturer at the School of Cybersecurity, Chengdu University of Information Technology. He received his Ph. D. degree in information security from Sichuan University in 2013. His research interest covers web mining, social computing, and machine learning

      Lecturer at the School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications. He received his Ph. D. degree from the School of Computer Science and Technology, University of Electronic Science and Technology of China in 2017. His main research interest is data mining

       Associate professor at the School of Computer and Software Engineering, Xihua University. She received her Ph. D. degree in information security from Sichuan University in 2014. Her research interest covers data mining and complex networks

       Lecturer at the School of Management, Chengdu University of Information Technology. She received her Ph. D. degree from Chengdu University of Traditional Chinese Medicine in 2012. Her main research interest is data mining

      Associate researcher at the School of Software Engineering, Chengdu University of Information Technology. She received his Ph. D. degree from Lumière University Lyon 2 in 2015. Her research interest covers intelligent information processing and knowledge engineering

    Corresponding author: QIAO Shao-Jie   Professor at the School of Cybersecurity, Chengdu University of Information Technology. He received his Ph. D. degree from the College of Computer Science, Sichuan University in 2009. His research interest covers trajectory prediction, moving objects databases, and big data. Corresponding author of this paper
  • 摘要: 社交网络用户情绪传播与用户的空间距离和时间跨度有关,并且受到多种交互机制的影响.从大规模社交网络数据中提取情绪传播的时空特征,研究用户行为对情绪传播的影响,对预测情绪传播趋势具有实际意义.利用线性回归获取的各行为子层的情绪传输率之间存在差异.提出一种基于多层社交网络的情绪传播模型,被称为ECM模型(Emotional contagion model).该模型包括三个行为子层,每层的拓扑结构各不相同,由该行为的交互历史决定.在真实数据上对ECM模型进行仿真分析,可以获得社交网络中情绪传播的过程与规律:1)中性情绪用户所占比例随时间逐渐增大,接近57.1%,而正向情绪与负向情绪比例始终接近.2)情绪传输率越大,用户情绪更容易受到其他用户的影响而发生变化;初始情绪越中立的用户,在演化过程中情绪波动越小,而初始情绪极性越大的用户情绪波动越大.此外,通过实验对比该模型与其他情绪传播模型,表明ECM模型更加接近真实数据,对社交网络中情绪传播具有较好的预测效果,预测准确率相比其他模型可以提高1.8%~7.8%.
    1)  本文责任编委 赵铁军
  • 图  1  社交网络中情绪传播分析及模型构建示意图

    Fig.  1  Analysis and modeling of emotion contagion in social networks

    图  2  用户间情绪关联度与距离之间的关系图

    Fig.  2  Relation between emotional correlation and distances

    图  3  ECM模型的演化规律

    Fig.  3  Evolutionary process of ECM model

    图  4  情绪转换数随用户初始情绪与节点度乘积的变化

    Fig.  4  The relation between the number of individual emotional tendency changes, the degree and the initial emotion

    图  5  三种模型与真实数据的对比(Twitter数据集)

    Fig.  5  The comparison of the three models and the real data (Twitter dataset)

    图  6  三种模型分类度量值的对比

    Fig.  6  The comparison of classification measurements of the three models

    图  7  三种模型中的F-1值随$\theta_1$的变化规律(Twitter数据集)

    Fig.  7  F-1 changes with $\theta_1$ for the three models (Twitter dataset)

    图  8  三种模型中的F-1值随用户数的变化规律(Twitter数据集)

    Fig.  8  F-1 changes with the number of users for the three models (Twitter dataset)

    表  1  数据集统计信息

    Table  1  The statistical information of the datasets

    Higgs数据集数据堂数据集新采集Twitter新采集新浪微博
    数据来源Twitter新浪微博Twitter新浪微博
    用户(节点)数456 62663 64133 0706 344
    好友关系数14 855 8421 391 718185 39354 093
    转发次数328 13227 75988 67724 027
    提及次数150 818未采集41 24510 428
    回复次数32 523未采集12 1744 207
    是否包含文本
    下载: 导出CSV

    表  2  两个数据集不同子层的情绪传输率

    Table  2  The transimisibilities on different layers in the two datasets

    Twitter数据集新浪微博数据集
    转发0.270.31
    提及0.951.07
    回复0.440.45
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-08-31
  • 录用日期:  2018-01-01
  • 刊出日期:  2018-12-20

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