全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2016 

Twitter中的情绪传染现象
Emotional contagion in Twitter

DOI: 10.6040/j.issn.1671-9352.1.2015.053

Keywords: 格兰杰因果检验,社交网络,情绪传染,Twitter,
social network
,Granger causality tests,Twitter,emotional contagion

Full-Text   Cite this paper   Add to My Lib

Abstract:

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。
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

References

[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, GON?ALVES B, RUAN G, et al. Happiness is assortative in online social networks[J]. Artificial Life, 2011, 17(3):237-251.
[6]  GOLEMAN D. E-mail is easy to write(and to misread)[N]. New York: Times, 2007-10-07.
[7]  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.
[8]  CENTOLA D. An experimental study of homophily in the adoption of health behavior[J]. Science, 2011, 334(6060):1269-1272.
[9]  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.
[10]  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.
[11]  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.
[12]  PHILLIPS P C B, PERRON P. Testing for a unit root in time series regression[J]. Biometrika, 1986, 75(2):335-346.
[13]  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.
[14]  PENNEBAKER J W, CHUNG C K, IRELAND M, et al. The development and psychological properties of LIWC2007[J]. Austin, 2007, 29(11):1020-1025.
[15]  CENTOLA D. The spread of behavior in an online social network experiment[J]. Science, 2010, 329(5996):1194-1197.
[16]  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.
[17]  ARAL S, WALKER D. Identifying influential and susceptible members of social networks[J]. Science, 2012, 337(6092):337-341.
[18]  HURLIN C, VENET B. Granger causality tests in panel data models with fixed coefficients[J]. Document De Recherche Leo, 2001:1-31.
[19]  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.
[20]  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.
[21]  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.
[22]  HOFFMAN M L. How automatic and representational is empathy, and why[J]. Behavioral and Brain Sciences, 2002, 25(1):38-39.
[23]  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.
[24]  ROSENQUIST J N, FOWLER J H, CHRISTAKIS N A. Social network determinants of depression[J]. Molecular Psychiatry, 2011, 16(3):273-281.
[25]  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.
[26]  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.
[27]  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.
[28]  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.
[29]  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.
[30]  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.
[31]  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..
[32]  COVIELLO L, SOHN Y, KRAMER A D I, et al. Detecting emotional contagion in massive social networks[J]. PloS One, 2014, 9(3):e90315.
[33]  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.
[34]  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.
[35]  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.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133