自动化学报2018，Vol.44Issue(12)：2290-2299,10.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
Spatio-temporal Feature Based Emotional Contagion Analysis and Prediction Model for Online Social Networks
摘要
Abstract
Users emotion in social networks is related to spatial distance and time span, and affected by multiple interaction mechanisms. It has practical significance to extract the spatiotemporal features from large-scale social networks and study the influence of users behaviors on emotional contagion in order to predict the trend of emotional contagion. The transmisibility values on different behavioral layers are calculated by linear regression and the results show the differences between these values. An emotional contagion model called ECM on multilayer social networks is proposed. It consists of three behavioral layers with different topologies depending on users interaction history.By simulation on real dataset, it is discovered that, 1) the proportion of users with neutral emotion is gradually increased with time and reaches 57.1 ％ while the proportion of positive emotion is comparable to that of negative emotion from beginning to end;2) users' emotion is more likely to be influenced by other users when transmissibility becomes larger and users with initial polar emotion fluctuate more drastically than users with initial neutral emotion. In order to show the advantages of the proposed model, it is compared with other emotional contagion models.The results demonstrate that the proposed model approximates to the real data of emotional contagion on social networks, and also shows better predictive performance of emotional contagion.The prediction accuracy is increased by 1.8 ％7.8 ％.关键词
情绪传播/多层网络/行为分析/社交网络Key words
Emotion contagion/multilayer networks/behavior analysis/social networks引用本文复制引用
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[J].自动化学报,2018,44(12):2290-2299,10.基金项目
国家自然科学基金(61772091,61802035)；教育部人文社会科学研究青年基金(17YJCZH202)；四川省科技计划项目(2018GZ0253,2018JY0448)；成都信息工程大学科研基金(KYTZ201637,KYTZ201715,KYTZ201750) （61772091,61802035）
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-RK00-00125-ZF,2017-RK00-00053-ZF)；四川高校科研创新团队建设计划(18TD0027)；广西自然科学基金项目(2018GXNSFDA138005)；广东省重点实验室项目(2017B030314073)资助 （J201701）
