Abstract
Social media presents an opportunity for people to share content that they find to be significant, funny, or notable. No single piece of content will appeal to all users, but are there systematic variations between users that can help us better understand information propagation? We conducted an experiment exploring social media usage during disaster scenarios, combining electroencephalogram (EEG), personality surveys, and prompts to share social media, we show how personality not only drives willingness to engage with social media, but also helps to determine what type of content users find compelling. As expected, extroverts are more likely to share content. In contrast, one of our central results is that individuals with depressive personalities are the most likely cohort to share informative content, like news or alerts. Because personality and mood will generally be highly correlated between friends via homophily, our results may be an import factor in understanding social contagion.
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References
Adali, S., Golbeck, J.: Predicting personality with social behavior, pp. 302–309. IEEE (2012)
Almehmadi, A., Bourque, M., El-Khatib, K.: A tweet of the mind: automated emotion detection for social media using brain wave pattern analysis, pp. 987–991. IEEE (2013)
Chen, J., Luo, X., Ren, B., Song, X., Chen, J., Luo, X., Ren, B., Song, X.: Revealing the ‘invisible gorilla’ in construction: assessing mental workload through time-frequency analysis. In: ISARC Proceedings 2015 Proceedings of the 32nd ISARC, Oulu, Finland, pp. 1–8 (2015)
Correa, K.A., Stone, B.T., Stikic, M., Johnson, R.R., Berka, C.: Characterizing donation behavior from psychophysiological indices of narrative experience. Decis. Neurosci. 301 (2015)
Correa, T., Hinsley, A.W., de Zúñiga, H.G.: Who interacts on the web?: the intersection of users’ personality and social media use. Comput. Hum. Behav. 26, 247–253 (2010)
Davidson, R.J.: What does the prefrontal cortex ‘do’ in affect: perspectives on frontal EEG asymmetry research. Biolo. Psychol. 67, 219–234 (2004)
Evers, C.W., Albury, K., Byron, P., Crawford, K.: Young people, social media, social network sites and sexual health communication in Australia: ‘this is funny, you should watch it’. Int. J. Commun. 7, 18 (2013)
Golbeck, J., Robles, C., Edmondson, M., Turner, K.: Predicting personality from twitter. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 149–156. IEEE (2011)
Gündel, H., O’connor, M.-F., Littrell, L., Fort, C., Lane, R.D.: Functional neuroanatomy of grief: an FMRI study. Am. J. Psychiatry (2003)
Hodas, N.O., Kooti, F., Lerman, K.: Friendship paradox redux: your friends are more interesting than you. In: ICWSM 2013 (2013)
Hodas, N.O., Lerman, K.: The simple rules of social contagion. Sci. Rep. 4, 4343 (2014)
Hughes, D.J., Rowe, M., Batey, M., Lee, A.: A tale of two sites: twitter vs. facebook and the personality predictors of social media usage. Comput. Hum. Behav. 28, 561–569 (2012)
Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! understanding the functional building blocks of social media. Bus. Horiz. 54, 241–251 (2011)
Kramer, A.D.I., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. 111, 8788–8790 (2014)
Lee, C.S., Ma, L.: News sharing in social media: the effect of gratifications and prior experience. Comput. Hum. Behav. 28, 331–339 (2012)
Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from eeg using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14, 186–197 (2010)
Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our twitter profiles, our selves: predicting personality with twitter. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 180–185. IEEE (2011)
Ryan, T., Xenos, S.: Who uses facebook? An investigation into the relationship between the big five, shyness, narcissism, loneliness, and facebook usage. Comput. Hum. Behav. 27, 1658–1664 (2011)
Schmidtke, J.I., Heller, W.: Personality, affect and EEG: predicting patterns of regional brain activity related to extraversion and neuroticism. Pers. Individ. Differ. 36, 717–732 (2004)
Stikic, M., Johnson, R.R., Tan, V., Berka, C.: EEG-based classification of positive and negative affective states. Brain-Comput. Interfaces 1, 99–112 (2014)
Sumner, C., Byers, A., Boochever, R., Park, G.J.: Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets, pp. 386–393. IEEE (2012)
Yazdani, A., Lee, J.-S., Ebrahimi, T.: Implicit emotional tagging of multimedia using EEG signals and brain computer interface. In: Proceedings of the first SIGMM workshop on Social media, pp. 81–88. ACM (2009)
Lin, Y.-P., Wang, C.-H., Jung, T.-P., Wu, T.-L., Jeng, S.-K., Duann, J.-R., Chen, J.-H.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57, 1798–1806 (2010)
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Appendix
The following are plots of average squared EEG on the F8 channel for response for messages the users decided to share. We believe F8 the most discriminative channel during retweeting. Each trait is broken down according to the top quartile (users have the “most” of that trait), and bottom quartile (users with the “least” of that trait). Higher signals indicate more engagement and attention to the message.
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Hodas, N.O., Butner, R., Corley, C. (2016). How a User’s Personality Influences Content Engagement in Social Media. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_30
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DOI: https://doi.org/10.1007/978-3-319-47880-7_30
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