基于时空特征的社交网络情绪传播分析与预测模型
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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%.
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 %.
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 %.
引文
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25 Zhang Yun.The practice on the development of software on the Chinese academic bibliometrics based on the open source software.New Technology of Library and Information Service,2010,26(4):87-91(张云.基于开源软件的中文学术文献计量软件的开发实践.现代图书情报技术,2010,26(4):87-91)
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27 Powers D M W.Evaluation:from precision,recall and F-measure to ROC,informedness,markedness&correlation.Journal of Machine Learning Technologies,2011,2(1):37-63
2 Xiong X B,Zhou G,Huang Y Z,Chen H Y,Xu K.Dynamic evolution of collective emotions in social networks:a case study of Sina weibo.Science China Information Sciences,2013,56(7):1-18
3 Lo S C,Huang K P.The smiling mask in service encounters:the impact of surface and deep acting.International Journal of Management,Economics and Social Sciences,2017,6(1):40-55
4 Wang Q Y,Lin Z,Jin Y H,Cheng S D,Yang T.ESIS:emotion-based spreader-ignorant-stifler model for information diffusion.Knowledge-Based Systems,2015,81:46-55
5 Wang Q Y,Jin Y H,Yang T,Cheng S D.An emotionbased independent cascade model for sentiment spreading.Knowledge-Based Systems,2017,116:86-93
6 Dignath D,Janczyk M,Eder A B.Phasic valence and arousal do not influence post-conflict adjustments in the Simon task.Acta Psychologica,2017,174:31-39
7 Lloyd-Jones D M,Larson M G,Leip E P,Beiser A,D’Agostino R B,Kannel W B,et al.Lifetime risk for developing congestive heart failure:the Framingham Heart Study.Circulation,2002,106(24):3068-3072
8 Coviello L,Sohn Y,Kramer A D I,Marlow C,Franceschetti M,Christakis N A,et al.Detecting emotional contagion in massive social networks.PLoS One,2014,9(3):Article No.e90315
9 Del Vicario M,Vivaldo G,Bessi A,Zollo F,Scala A,Caldarelli G,et al.Echo chambers:emotional contagion and group polarization on Facebook.Scientific Reports,2016,6:Article No.37825
10 Guille A,Hacid H,Favre C,Zighed D A.Information diffusion in online social networks:a survey.ACM SIGMODRecord,2013,42(2):17-28
11 Bozorgi A,Samet S,Kwisthout J,Wareham T.Communitybased influence maximization in social networks under a competitive linear threshold model.Knowledge-Based Systems,2017,134:149-158
12 Liu L J.A delayed SIR model with general nonlinear incidence rate.Advances in Difference Equations,2015,2015:329
13 Zhang X H,Jiang D Q,Alsaedi A,Hayat T.Stationary distribution of stochastic SIS epidemic model with vaccination under regime switching.Applied Mathematics Letters,2016,59:87-93
14 Xiong F,Liu Y,Zhang Z J,Zhu J,Zhang Y.An information diffusion model based on retweeting mechanism for online social media.Physics Letters A,2012,376(30):2103-2108
15 Boccaletti S,Bianconi G,Criado R,del Genio C I,G′omezGarde?nes J,Romance M,et al.The structure and dynamics of multilayer networks.Physics Reports,2014,544(1):1-122
16 KivelaM,Arenas A,Barthelemy M,Gleeson J P,Moreno Y,Porter M A.Multilayer networks.Journal of Complex Networks,2013,2(3):261-268
17 Yaˇgan O,Qian D J,Zhang J S,Cochran D.Information diffusion in overlaying social-physical networks.In:Proceedings of the 2012 46th Annual Conference on Information Sciences and Systems(CISS).Princeton,NJ,USA:IEEE,2012.1038-1048
18 Zhuang Y,Yagan O.Information propagation in clustered multilayer networks.IEEE Transactions on Network Science and Engineering,2016,3(4):211-224
19 Kim M,Newth D,Christen P.Modeling dynamics of diffusion across heterogeneous social networks:news diffusion in social media.Entropy,2013,15(15):4215-4242
20 Xiong X,Li Y Y,Qiao S J,Han N,Wu Y,Peng J,et al.An emotional contagion model for heterogeneous social media with multiple behaviors.Physica A:Statistical Mechanics and its Applications,2018,490:185-202
21 Ferrara E,Yang Z Y.Measuring emotional contagion in social media.PLoS One,2015,10(11):Article No.e0142390
22 Kramer A D I.The spread of emotion via Facebook.in:Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.Austin,Texas,USA:ACM,2012.767-770
23 De Domenico M,Lima A,Mougel P,Musolesi M.The anatomy of a scientific rumor.Scientific Reports,2013,3:Article No.2980
24 Thelwall M,Buckley K,Paltoglou G.Sentiment strength detection for the social web.Journal of the Association for Information Science and Technology,2012,63(1):163-173
25 Zhang Yun.The practice on the development of software on the Chinese academic bibliometrics based on the open source software.New Technology of Library and Information Service,2010,26(4):87-91(张云.基于开源软件的中文学术文献计量软件的开发实践.现代图书情报技术,2010,26(4):87-91)
26 The Boson Data.Sentiment analysis[Online],available:http://docs.bosonnlp.com/sentiment.html,2017.
27 Powers D M W.Evaluation:from precision,recall and F-measure to ROC,informedness,markedness&correlation.Journal of Machine Learning Technologies,2011,2(1):37-63