Abstract
Background. The spread of affective content on social media, as well as user grouping based on affect [1], has been a focus of scholarly attention for over a decade. But, despite this, we lack evidence on what roles various particular emotions play in the dynamics of discussions on social media. Emotional contagion theory (Hatfield et al. 2014) adapted for social media suggests that diffusion of emotions happens on individual level, via direct one-time contact with emotionalized content [2]. Other theories, like theories of social influence or social learning [3], thought, suggest multiple, hierarchical, and/or topically-restricted contacts. The idea of affective agenda [4] implies that the dynamics of an emotional discussion needs to be assessed on the aggregate level. The question remains – what role the emotions taken on aggregate level play in the discussion dynamics, being either catalyzers or inhibitors of the discussions. One may suggest that emotions of different stance (positive/negative) may spur/slow down the discussions in various ways. Objectives. We analyze the spread of two polar emotions – anger and compassion – in three Twitter discussions on inter-ethnic conflicts, namely Ferguson protests (the USA, 2014), Charlie Hebdo massacre (France, 2015), and mass harassment in Cologne (Germany, 2015–2016). By analyzing the co-dynamics of the overall discussions and these two emotions we can conclude whether the pattern of the spread of emotions and its link with the discussion dynamics is the same in various language segments of Twitter. Data collection and methods. The data we use were collected by our patented Twitter crawler in the aftermath of the conflicts and include altogether over 2,5 M tweets. We used manual coding by native speakers and machine learning to detect the emotions; then, we visualized the dynamics of growth of the emotional content of the discussions and used Granger test to see whether anger or compassion gave a spur to the discussions. Results. We have received moderate results in terms of the dependence of the number of neutral users upon that of emotional users, but have spotted that the beginnings of the discussions, as well as the discussion outbursts, depend more on compassion, not on angry users, which needs more exploration. We have also shown that the hourly dynamics of emotions replicates that of the larger discussion, and the numbers of angry and compassionate users per hour highly correlate in all the cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Papacharissi, Z.: Affective publics: Sentiment, Technology, and Politics. Oxford University Press, Oxford (2015)
Coviello, L., et al.: Detecting emotional contagion in massive social networks. PloS One 9(3), e90315 (2014)
Young, R.: Discursive Practice in Language Learning and Teaching, vol. 58. Wiley-Blackwell, Malden (2009)
Coleman, R., Wu, H.D.: Proposing emotion as a dimension of affective agenda setting: separating affect into two components and comparing their second-level effects. Journalism Mass Commun. Q. 87(2), 315–327 (2010)
Cortese, A.J.P.: Opposing Hate Speech. Greenwood Publishing Group, Santa Barbara (2006)
Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759–760 (2017)
Burnap, P., Williams, M.L.: Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7(2), 223–242 (2015)
Park, J.H., Fung, P.: One-step and two-step classification for abusive language detection on twitter (2017). arXiv preprint arXiv:1706.01206
Georgakopoulos, S.V., Tasoulis, S.K., Vrahatis, A.G., Plagianakos, V.P.: Convolutional neural networks for toxic comment classification. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1–6 (2018)
Lyman, P.: The domestication of anger: the use and abuse of anger in politics. Eur. J. Soc. Theory 7(2), 133–147 (2004)
Ticineto Clough, P., Halley, J.: The Affective Turn: Theorizing the Social. Duke University, Durham (2007)
Heaney, J.G., Flam, H. (eds.) Power and Emotion. Routledge, London (2015)
Wahl-Jorgensen, K.: Emotions, Media and Politics. Wiley, Hoboken (2019)
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)
Dorsett, D.M.: Hate speech debate and free expression. S. Cal. Interdisc. LJ 5, 259 (1996)
Cammaerts, B.: Radical pluralism and free speech in online public spaces: the case of North Belgian extreme right discourses. Int. J. Cult. Stud 12(6), 555–575 (2009)
Waseem, Z., Davidson, T., Warmsley, D., Weber, I.: Understanding abuse: a typology of abusive language detection subtasks. arXiv preprint arXiv:1705.09899 (2017)
Hatfield, E., Bensman, L., Thornton, P.D., Rapson, R.L.: New perspectives on emotional contagion: a review of classic and recent research on facial mimicry and contagion (2014)
Bruns, A., Burgess, J.E.: The use of Twitter hashtags in the formation of ad hoc publics. In: Proceedings of the 6th European Consortium for Political Research (ECPR) General Conference (2011)
Bodrunova, S.S., Litvinenko, A.A., Blekanov, I.S.: Comparing influencers: activity vs. connectivity measures in defining key actors in twitter Ad hoc discussions on migrants in Germany and Russia. In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10539, pp. 360–376. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67217-5_22
Bodrunova, S.S., Blekanov, I., Smoliarova, A., Litvinenko, A.: Beyond left and right: real-world political polarization in Twitter discussions on inter-ethnic conflicts. Media Commun. 7, 119–132 (2019)
Wessa, P.: Bivariate Granger Causality version 1.0.4 in Free Statistics Software version 1.2.1, Office for Research Development and Education (2016). http://www.wessa.net/rwasp_grangercausality.wasp/
Acknowledgements
This research has been supported in full by the Presidential Grant of the Russian Federation to young Doctors, grant MD-6259.2018.6 (2018–2019).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bodrunova, S.S., Nigmatullina, K., Blekanov, I.S., Smoliarova, A., Zhuravleva, N., Danilova, Y. (2020). When Emotions Grow: Cross-Cultural Differences in the Role of Emotions in the Dynamics of Conflictual Discussions on Social Media. In: Meiselwitz, G. (eds) Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science(), vol 12194. Springer, Cham. https://doi.org/10.1007/978-3-030-49570-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-49570-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49569-5
Online ISBN: 978-3-030-49570-1
eBook Packages: Computer ScienceComputer Science (R0)