Abstract
Web-based social relations mirror several known phenomena identified by Social Sciences, such as Homophily. Social circles are inferable from those relations and there are already solutions to find the underlying sentiment of social interactions. We present an empirical study that combines existing Graph Clustering and Sentiment Analysis techniques for reasoning about Sentiment dynamics at cluster level and analyzing the role of social influence on sentiment contagion, based on a large dataset extracted from Twitter during the 2014 FIFA World Cup. Exploiting WebGraph and LAW frameworks to extract clusters, and SentiStrength to analyze sentiment, we propose a strategy for finding moments of Sentiment Homophily in clusters. We found that clusters tend to be neutral for long ranges of time, but denote volatile bursts of sentiment polarity locally over time. In those moments of polarized sentiment homogeneity there is evidence of an increased, but not strong, chance of one sharing the same overall sentiment that prevails in the cluster to which he belongs.
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Notes
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Medical study about cardiovascular disease—https://www.framinghamheartstudy.org/.
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References
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. WSDM ’11, pp. 65–74. ACM, NY (2011)
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web. WWW ’12, pp. 519–528. ACM, NY (2012)
Boldi, P., Rosa, M., Santini, M., Vigna, S.: Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks. In: Proceedings of the 20th International Conference on World Wide Web. WWW ’11, pp. 587–596. ACM, NY (2011)
Boldi, P., Vigna, S.: The webgraph framework I: compression techniques. In: Proceedings of the 13th International Conference on World Wide Web. WWW ’04, pp. 595–602. ACM, NY (2004)
Bollen, J., Gonçalves, B., Ruan, G., Mao, H.: Happiness is assortative in online social networks. Artif. Life 17(3), 237–251 (2011)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: the million follower fallacy. In: ICWSM ’10: Proceedings of international AAAI Conference on Weblogs and Social (2010)
Conover, M., Gonçalves, B., Ratkiewicz, J., Flammini, A., Menczer, F.: Predicting the political alignment of twitter users. In: SocialCom/PASSAT, pp. 192–199. IEEE (2011)
Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, NY (2010)
Fan, R., Zhao, J., Chen, Y., Xu, K.: Anger is more influential than joy: sentiment correlation in weibo. PLoS ONE 9(10), e110184 (2014)
Fowler, J., Christakis, N.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study. Br. Med. J. 337, a2338 (2008)
Gruzd, A., Doiron, S., Mai, P.: Is happiness contagious online? A case of twitter and the 2010 winter olympics. In: Proceedings of the 2011 44th Hawaii International Conference on System Sciences. HICSS ’11, pp. 1–9. IEEE Computer Society, Washington (2011)
Hodeghatta, U.R.: Sentiment analysis of hollywood movies on twitter. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ASONAM ’13, pp. 1401–1404. ACM, NY (2013)
Huberman, B., Romero, D., Wu, F.: Social networks that matter: twitter under the microscope. First Monday 14(1) (2008)
Krippendorff, K.: Computing krippendorff’s alpha reliability. Technical report, University of Pennsylvania, Annenberg School for Communication (2011)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’10, pp. 1361–1370. ACM, NY (2010)
Mcauley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data 8(1), 4:1–4:28 (2014)
Newman, M.: Networks: An Introduction. Oxford University Press Inc, NY (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web. WWW ’10, pp. 851–860. ACM, NY (2010)
Shalizi, C.R., Thomas, A.C.: Homophily and contagion are generically confounded in observational social network studies (2010)
Tang, J., Chang, Y., Liu, H.: Mining social media with social theories: a survey. SIGKDD Explor. Newsl. 15(2), 20–29 (2014)
Tang, J., Gao, H., Hu, X., Liu, H.: Exploiting homophily effect for trust prediction. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. WSDM ’13, pp. 53–62. ACM, NY (2013)
Thelwall, M.: Homophily in myspace. J. Am. Soc. Inf. Sci. Technol. 60(2), 219–231 (2009)
Thelwall, M.: Emotion homophily in social network site messages. First Monday 15(4) (2010)
Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63(1), 163–173 (2012)
Acknowledgments
This work was partly supported by national funds through Fundação para a Ciência e Tecnologia (FCT), under projects EXCL/EEI-ESS/0257/2012 and UID/CEC/50021/2013.
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Lopes, H., Pinto, H.S., Francisco, A.P. (2016). Influence and Sentiment Homophily on Twitter Social Circles. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_27
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