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Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube

  • Ema Kušen
  • Mark StrembeckEmail author
  • Mauro Conti
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

In this paper, we present a study on 5.6 million messages that have been sent via Twitter, Facebook, and YouTube. The messages in our data set are related to 24 systematically chosen real-world events. For each of the 5.6 million messages, we first extracted emotion scores based on the eight basic emotions according to Plutchik’s wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the messaging behavior of social media users. In particular, we found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted (i.e., positive) emotions in the corresponding messages. To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis assumes that positive emotions serve as an antidote during negative events.

Notes

Acknowledgements

Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission (agreement PCIG11-GA-2012-321980). This work is also partially supported by the EU TagItSmart! Project (agreement H2020-ICT30-2015-688061), the EU-India REACH Project (agreement ICI+/2014/342-896), the project CNR-MOST/Taiwan 2016-17 “Verifiable Data Structure Streaming,” the grant n. 2017-166478 (3696) from Cisco University Research Program Fund and Silicon Valley Community Foundation, and the grant “Scalable IoT Management and Key security aspects in 5G systems” from Intel.

References

  1. 1.
    A. Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, Sentiment analysis of twitter data, in Proceedings of the Workshop on Languages in Social Media (Association for Computational Linguistics, Stroudsburg, 2011), pp. 30–38Google Scholar
  2. 2.
    M. Alarid, Recruitment and Radicalization: The Role of Social Media and New Technology. CCO Publications (2016)Google Scholar
  3. 3.
    R.F. Baumeister, M.R. Leary, The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychol. Bull. 117, 497–529 (1995)CrossRefGoogle Scholar
  4. 4.
    M. Bayer, W. Sommer, A. Schacht, Font size matters—emotion and attention in cortical responses to written words. PLoS One 7(05), 1–6 (2012)CrossRefGoogle Scholar
  5. 5.
    BBC.com, Study: social networks like Facebook can spread moods (2014), http://www.bbc.com/news/technology-26556295
  6. 6.
    J. Berger, Arousal increases social transmission of information. Psychol. Sci. 22(7), 891–893 (2011)CrossRefGoogle Scholar
  7. 7.
    A. Bessi, E. Ferrara, Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21(11) (2016). http://firstmonday.org/ojs/index.php/fm/article/view/7090
  8. 8.
    J. Boucher, C.E. Osgood, The Pollyanna hypothesis. J. Verbal Learn. Verbal Behav. 8(1), 1–8 (1969)CrossRefGoogle Scholar
  9. 9.
    L. Coviello, Y. Sohn, A.D.I. Kramer, C. Marlow, M. Franceschetti, N.A. Christakis, J.H. Fowler, Detecting emotional contagion in massive social networks. PLoS One 9, 1–6 (2014)CrossRefGoogle Scholar
  10. 10.
    M. Faraon, G. Stenberg, M. Kaipainen, Political campaigning 2.0: the influence of online news and social networking sites on attitudes and behavior. eJ. eDemocr. Open Gov. 6(3), 231–247 (2014)Google Scholar
  11. 11.
    E. Ferrara, Z. Yang, Measuring emotional contagion in social media. PLoS One 10(11), 1–14 (2015)CrossRefGoogle Scholar
  12. 12.
    E. Ferrara, Z. Yang, Quantifying the effect of sentiment on information diffusion in social media. PeerJ Comput. Sci. 1, 1–15 (2015)CrossRefGoogle Scholar
  13. 13.
    B.L. Fredrickson, The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. Am. Psychol. 56, 218–226 (2001)CrossRefGoogle Scholar
  14. 14.
    J.J. Freyd, In the wake of terrorist attack, hatred may mask fear. Anal. Soc. Issues Public Policy 2(1), 5–8 (2002)CrossRefGoogle Scholar
  15. 15.
    R. González-Ibáñez, S. Muresan, N. Wacholder, Identifying sarcasm in twitter: a closer look, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers - Volume 2 (2011), pp. 581–586Google Scholar
  16. 16.
    A. Gruzd, S. Doiron, P. Mai, Is happiness contagious online? a case of twitter and the 2010 winter olympics, in Proceedings of the 44th Hawaii International Conference on System Sciences (IEEE Computer Society, Washington, 2011), pp. 1–9Google Scholar
  17. 17.
    C. Heath, Do people prefer to pass along good or bad news? valence and relevance of news as predictors of transmission propensity. Organ. Behav. Hum. Decis. Process. 68(2), 79–94 (1996)CrossRefGoogle Scholar
  18. 18.
    I. Heimbach, O. Hinz, The impact of content sentiment and emotionality on content virality. Int. J. Res. Mark. 33(3), 695–701 (2016)CrossRefGoogle Scholar
  19. 19.
    Y. Hu, J. Zhao, J. Wu, Emoticon-based ambivalent expression: a hidden indicator for unusual behaviors in Weibo. PLoS One 11(1), 1–14 (2016)Google Scholar
  20. 20.
    H.S. Kim, S. Lee, J.N. Cappella, L. Vera, S. Emery, Content characteristics driving the diffusion of antismoking messages: implications for cancer prevention in the emerging public communication environment. J. Natl. Cancer Inst. Monogr. 47, 182–187 (2013)CrossRefGoogle Scholar
  21. 21.
    A.D.I. Kramer, J.E. Guillory, J.T. Hancock, Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. 111(24), 8788–8790 (2014)CrossRefGoogle Scholar
  22. 22.
    E. Kušen, G. Cascavilla, K. Figl, M. Conti, M. Strembeck, Identifying emotions in social media: comparison of word-emotion lexicons, in Proceedings of the 4th International Symposium on Social Networks Analysis, Management and Security (SNAMS), August 2017 (IEEE, Piscataway, 2017)Google Scholar
  23. 23.
    E. Kušen, M. Strembeck, G. Cascavilla, M. Conti, On the influence of emotional valence shifts on the spread of information in social networks, in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (ACM, New York, 2017), pp. 321–324Google Scholar
  24. 24.
    H. Kwak, C. Lee, H. Park, S. Moon, What is Twitter, a social network or a news media?, in Proceedings of the 19th International Conference on World Wide Web (2010), pp. 591–600Google Scholar
  25. 25.
    R. Lin, S. Utz, The emotional responses of browsing Facebook: happiness, envy, and the role of tie strength. Comput. Hum. Behav. 52(Suppl. C), 29–38 (2015)Google Scholar
  26. 26.
    B. Mcmanus, An expert explains how social media can lead to the ‘self-radicalisation’ of terrorists (2015), https://www.vice.com/en_uk/article/we-asked-an-expert-how-social-media-can-help-radicalize-terrorists Google Scholar
  27. 27.
    S.M. Mohammad, P.D. Turney, Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)CrossRefGoogle Scholar
  28. 28.
    J.G. Myrick, Emotion regulation, procrastination, and watching cat videos online: who watches Internet cats, why, and to what effect? Comput. Hum. Behav. 52(Suppl. C), 168–176 (2015)Google Scholar
  29. 29.
    R.L. Nabi, Exploring the framing effects of emotion. Commun. Res. 30(2), 224–247 (2003)CrossRefGoogle Scholar
  30. 30.
    N. Naveed, T. Gottron, J. Kunegis, A.C. Alhadi, Bad news travel fast: a content-based analysis of interestingness on twitter, in Proceedings of the 3rd International Web Science Conference (ACM, New York, 2011), pp. 8:1–8:7Google Scholar
  31. 31.
    F.Å. Nielsen, Afinn (2011), http://www2.imm.dtu.dk/pubdb/p.php?6010
  32. 32.
    R. Plutchik, The nature of emotions. Am. Sci. 89(4), 344 (2001)Google Scholar
  33. 33.
    M.G. Rordriguez, J. Leskovec, D. Balduzzi, B. Scholkopf, Uncovering the structure and temporal dynamics of information propagation. Netw. Sci. 2(1), 26–65 (2014)CrossRefGoogle Scholar
  34. 34.
    D.A. Savage, B. Torgler, The emergence of emotions and religious sentiments during the September 11 disaster. Motiv. Emot. 37(3), 586–599 (2013)CrossRefGoogle Scholar
  35. 35.
    C. St Louis, G. Zorlu, Can Twitter predict disease outbreaks? Br. Med. J. 344, 1–3 (2012)CrossRefGoogle Scholar
  36. 36.
    M. Steinbach, ISIL Online: Countering Terrorist Radicalization and Recruitment on the Internet and Social Media (2016), https://www.fbi.gov/news/testimony/isil-online-countering-terrorist-radicalization-and-recruitment-on-the-internet-and-social-media- Google Scholar
  37. 37.
    S. Stieglitz, D.X. Linh, Emotions and information diffusion in social media- sentiment of microblogs and sharing behavior. J. Manag. Inf. Syst. 29(4), 217–247 (2013)CrossRefGoogle Scholar
  38. 38.
    S. Stuart, Why do we pay more attention to negative news than to positive news? (2015), http://blogs.lse.ac.uk/politicsandpolicy/why-is-there-no-good-news/ Google Scholar
  39. 39.
    B. Suh, L. Hong, P. Pirolli, E.H. Chi, Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network, in Proceedings of the 2010 IEEE Second International Conference on Social Computing (2010), pp. 177–184Google Scholar
  40. 40.
    M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, A. Kappas, Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61, 2544–2558 (2010)CrossRefGoogle Scholar
  41. 41.
    D.N. Trung, T.T. Nguyen, J.J. Jung, D. Choi, Understanding effect of sentiment content toward information diffusion pattern in online social networks: a case study on TweetScope, in Context-Aware Systems and Applications: Second International Conference, ICCASA 2013 (2014), pp. 349–358Google Scholar
  42. 42.
    S. Tsugawa, H. Ohsaki, Negative messages spread rapidly and widely on social media, in Proceedings of the ACM on Conference on Online Social Networks (ACM, New York, 2015), pp. 151–160Google Scholar
  43. 43.
    S. Vieweg, A.L. Hughes, K. Starbird, L. Palen, Microblogging during two natural hazards events: what Twitter may contribute to situational awareness, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2010), pp. 1079–1088Google Scholar
  44. 44.
    G. Weimann, Terror on Facebook, Twitter, and Youtube. Brown J. World Affairs 16(2), 45–54 (2010)Google Scholar
  45. 45.
    Y. Yang, J. Jia, B. Wu, J. Tang, Social role-aware emotion contagion in image social networks, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016), pp. 65–71Google Scholar
  46. 46.
    Z. Zhang, S.Y. Zhang, How do explicitly expressed emotions influence interpersonal communication and information dissemination? A field study of emoji’s effects on commenting and retweeting on a microblog platform, in 20th Pacific Asia Conference on Information Systems (2016), pp. 1–14Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Vienna University of Economics and BusinessWienAustria
  2. 2.Secure Business Austria Research Center (SBA)WienAustria
  3. 3.Complexity Science Hub Vienna (CSH)WienAustria
  4. 4.Università di PadovaPaduaItaly

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