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山东大学学报（理学版） ›› 2016, Vol. 51 ›› Issue (1): 71-76.doi: 10.6040/j.issn.1671-9352.1.2015.053

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Twitter中的情绪传染现象

张少群,魏晶晶,廖祥文*,简思远,陈国龙   

  1. 福州大学数学与计算机科学学院, 福建 福州 350116
  • 收稿日期:2015-07-27 出版日期:2016-01-16 发布日期:2016-11-29
  • 通讯作者: 廖祥文(1980— ),男,副教授,研究方向为网络文本倾向性分析.E-mail:liaoxw@fzu.edu.cn E-mail:2105257@qq.com
  • 作者简介:张少群(1989— ),男,硕士研究生,研究方向为社交媒介中的情绪传染现象. E-mail:2105257@qq.com
  • 基金资助:
    国家自然科学基金青年项目(61300105);教育部博士点基金联合资助项目(2012351410010);福建省科技重大专项项目(2013H6012);福州市科技计划项目(2012-G-113,2013-PT-45)

Emotional contagion in Twitter

ZHANG Shao-qun, WEI Jing-jing, LIAO Xiang-wen*, JIAN Si-yuan, CHEN Guo-long   

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2015-07-27 Online:2016-01-16 Published:2016-11-29

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摘要： 在Twitter中是否存在情绪传染现象是社会科学中一个待解决的问题。首先通过LIWC2007获取了包含106 641个用户的Twitter社交网络中所有用户的情绪时间序列,然后采用一系列的单位根检验证明了相关时间序列的平稳性,通过格兰杰因果检验,在预测用户情绪值的回归式中加入了用户关注好友过去时间的情绪值作为自变量,并采用统计假设检验的方法证明了该自变量的系数不为0,从而说明了用户的情绪会显著地被其关注好友过去的情绪所影响,即用户关注好友的情绪是用户情绪的格兰杰原因。用同样的方法证明了用户情绪并不是用户关注好友情绪的格兰杰原因,由于社交选择现象是一种双向关系,所以该结果是由情绪传染现象造成的。此外,通过统计分析发现Twitter中绝大部分单向关注好友都是非熟人,而绝大部分双向关注好友都是熟人。格兰杰因果检验的结果说明了人们的情绪既会被熟人的情绪所传染,也会被非熟人的情绪所传染。

关键词: 格兰杰因果检验, 社交网络, 情绪传染, Twitter

Abstract: It is a problem to be solved in social science whether there exists the phenomenon of emotional contagion in Twitter. The emotion time series of 106 641 users in the Twitter social network were got by the LIWC2007. Then a serious of unit root tests were used to validate that the time series are stable. Through the Granger causality test, the emotion variable of the users' followees in the past time was added to the regression equation to predict the users' emotion and then the statistical hypothesis tests was used to prove the regression coefficient of the variable was significantly not equal to 0, which indicated that the users' emotion could be influenced by their followees' emotional expression in the past time, which meaned that the emotion expressed by users' followees was the Granger cause of the emotion expressed by users. At the same time, the same method proved that the emotion expressed by users was not the Granger cause of the emotion expressed by users followees. Since the social selection was a type of bidirectional relationship, this phenomenon was caused by emotional contagion. Furthermore, the statistical results showed that most of the unidirectional followees in Twitter were not acquaintances in real life but most of the bidirectional followees in Twitter were acquaintances in real life. The results of the Granger causality also suggested that the people, either acquaintances or not acquaintances, could spread their emotion to others in Twitter.

Key words: social network, Granger causality tests, Twitter, emotional contagion

中图分类号: 

  • TP391
[1] HATFIELD E, CACIOPPO J T, RAPSON R L. Emotional contagion[M]. Cambridge: Cambridge University Press, 1994.
[2] KAHNEMAN D, KRUEGER A B, SCHKADE D, et al. Would you be happier if you were richer? A focusing illusion[J]. Science, 2006, 312(5782):1908-1910.
[3] EASTERLIN R A. Explaining happiness[J]. University of South California Legal Working Paper, 2003, 100(19):11176-11183.
[4] BURKE K A, FRANZ T M, MILLER D N, et al. The role of the orbitofrontal cortex in the pursuit of happiness and more specific rewards[J]. Nature, 2008, 454(7202):340-344.
[5] BOLLEN J, GONÇALVES B, RUAN G, et al. Happiness is assortative in online social networks[J]. Artificial Life, 2011, 17(3):237-251.
[6] HOFFMAN M L. How automatic and representational is empathy, and why[J]. Behavioral and Brain Sciences, 2002, 25(1):38-39.
[7] FOWLER J H, CHRISTAKIS N A. Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study[J]. BMJ, 2008, 337:a2338.
[8] ROSENQUIST J N, FOWLER J H, CHRISTAKIS N A. Social network determinants of depression[J]. Molecular Psychiatry, 2011, 16(3):273-281.
[9] CACIOPPO J T, FOWLER J H, CHRISTAKIS N A. Alone in the crowd: the structure and spread of loneliness in a large social network[J]. Journal of Personality and Social Psychology, 2009, 97(6):977.
[10] GOLEMAN D. E-mail is easy to write(and to misread)[N]. New York: Times, 2007-10-07.
[11] WALTHER J B, LOH T, GRANKA L. Let me count the ways the interchange of verbal and nonverbal cues in computer-mediated and face-to-face affinity[J]. Journal of Language and Social Psychology, 2005, 24(1):36-65.
[12] HANCOCK J T, LANDRIGAN C, SILVER C. Expressing emotion in text-based communication[C] //Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2007: 929-932.
[13] HANCOCK J T, GEE K, CIACCIO K, et al. Im sad youre sad: emotional contagion in CMC[C] //Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work. New York: ACM, 2008: 295-298.
[14] GUILLORY J, SPIEGEL J, DRISLANE M, et al. Upset now?: emotion contagion in distributed groups[C] //Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2011: 745-748.
[15] KRAMER A D I. The spread of emotion via Facebook[C] //Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2012: 767-770.
[16] KRAMER A D I, GUILLORY J E, HANCOCK J T. Experimental evidence of massive-scale emotional contagion through social networks[J]. Proceedings of the National Academy of Sciences, 2014, 111(24):8788-8790..
[17] COVIELLO L, SOHN Y, KRAMER A D I, et al. Detecting emotional contagion in massive social networks[J]. PloS One, 2014, 9(3):e90315.
[18] KWAK H, LEE C, PARK H, et al. What is Twitter, a social network or a news media?[C] //Proceedings of the 19th International Conference on World Wide Web. New York: ACM, 2010: 591-600.
[19] JAVA A, SONG X, FININ T, et al. Why we twitter: understanding microblogging usage and communities[C] //Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. New York: ACM, 2007: 56-65.
[20] JOINSON A N. Looking at, looking up or keeping up with people?: motives and use of facebook[C] //Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 2008:1027-1036.
[21] PANGER G. Social comparison in social media: a look at facebook and twitter[C] //CHI'14 Extended Abstracts on Human Factors in Computing Systems. New York: ACM, 2014:2095-2100.
[22] PENNEBAKER J W, CHUNG C K, IRELAND M, et al. The development and psychological properties of LIWC2007[J]. Austin, 2007, 29(11):1020-1025.
[23] AHN Y Y, HAN S, KWAK H, et al. Analysis of topological characteristics of huge online social networking services[C] //Proceedings of the 16th International Conference on World Wide Web. New York: ACM, 2007:835-844.
[24] MCPHERSON M, SMITH-LOVIN L, COOK J M. Birds of a feather: Homophily in social networks[J]. Annual Review of Sociology, 2001, 27:415-444.
[25] CENTOLA D. The spread of behavior in an online social network experiment[J]. Science, 2010, 329(5996):1194-1197.
[26] FOWLER J H, CHRISTAKIS N A. Cooperative behavior cascades in human social networks[J]. Proceedings of the National Academy of Sciences, 2010, 107(12):5334-5338.
[27] CENTOLA D. An experimental study of homophily in the adoption of health behavior[J]. Science, 2011, 334(6060):1269-1272.
[28] ARAL S, WALKER D. Identifying influential and susceptible members of social networks[J]. Science, 2012, 337(6092):337-341.
[29] GRANGER C W J. Investigating causal relations by econometric models and cross-spectral methods[J]. Econometrica: Journal of the Econometric Society, 1969,37(3):424-438.
[30] HURLIN C, VENET B. Granger causality tests in panel data models with fixed coefficients[J]. Document De Recherche Leo, 2001:1-31.
[31] DICKEY D A, BELL W R, MILLER R B. Unit roots in time series models: Tests and implications[J]. The American Statistician, 1986, 40(1):12-26.
[32] LEVIN A, LIN C F, CHU C S J. Unit root tests in panel data: asymptotic and finite-sample properties[J]. Journal of Econometrics, 2002, 108(1):1-24.
[33] I M K S, PESARAN M H, SHIN Y. Testing for unit roots in heterogeneous panels[J]. Journal of Econometrics, 2003, 115(1):53-74.
[34] MADDALA G S, WU S. A comparative study of unit root tests with panel data and a new simple test[J]. Oxford Bulletin of Economics and Statistics, 1999, 61(S1):631-652.
[35] PHILLIPS P C B, PERRON P. Testing for a unit root in time series regression[J]. Biometrika, 1986, 75(2):335-346.
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