Skip to main content
Springer Nature Link
Log in

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

  • Chapter
  • First Online:
Influence and Behavior Analysis in Social Networks and Social Media (ASONAM 2018)

Part of the book series: Lecture Notes in Social Networks ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook
USD 12.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://dev.twitter.com/overview/api.

  2. 2.

    https://github.com/philbot9/youtube-comment-api.

  3. 3.

    https://developers.facebook.com/docs/graph-api.

  4. 4.

    https://pypi.python.org/pypi/langdetect.

  5. 5.

    The AFINN lexicon [31] contains scores corresponding to the emotional valence intensity of a given word. For example, words such as sad and depressed are classified as negative words, but the latter has a weaker intensity compared to the former word.

  6. 6.

    Note that the increased limit of 280 characters that has been introduced by Twitter in November 2017 was not in effect during our data extraction period.

  7. 7.

    Set difference refers to the difference between the count of the expected emotions and shifted emotions, while sd stands for standard deviation.

References

  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. 30–38

    Google Scholar 

  2. M. Alarid, Recruitment and Radicalization: The Role of Social Media and New Technology. CCO Publications (2016)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  5. BBC.com, Study: social networks like Facebook can spread moods (2014), http://www.bbc.com/news/technology-26556295

  6. J. Berger, Arousal increases social transmission of information. Psychol. Sci. 22(7), 891–893 (2011)

    Article  Google Scholar 

  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. J. Boucher, C.E. Osgood, The Pollyanna hypothesis. J. Verbal Learn. Verbal Behav. 8(1), 1–8 (1969)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. E. Ferrara, Z. Yang, Measuring emotional contagion in social media. PLoS One 10(11), 1–14 (2015)

    Article  Google Scholar 

  12. E. Ferrara, Z. Yang, Quantifying the effect of sentiment on information diffusion in social media. PeerJ Comput. Sci. 1, 1–15 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  14. J.J. Freyd, In the wake of terrorist attack, hatred may mask fear. Anal. Soc. Issues Public Policy 2(1), 5–8 (2002)

    Article  Google Scholar 

  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. 581–586

    Google Scholar 

  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. 1–9

    Google Scholar 

  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)

    Article  Google Scholar 

  18. I. Heimbach, O. Hinz, The impact of content sentiment and emotionality on content virality. Int. J. Res. Mark. 33(3), 695–701 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 321–324

    Google Scholar 

  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. 591–600

    Google Scholar 

  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. 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. S.M. Mohammad, P.D. Turney, Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  Google Scholar 

  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. R.L. Nabi, Exploring the framing effects of emotion. Commun. Res. 30(2), 224–247 (2003)

    Article  Google Scholar 

  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. 8:1–8:7

    Google Scholar 

  31. F.Å. Nielsen, Afinn (2011), http://www2.imm.dtu.dk/pubdb/p.php?6010

  32. R. Plutchik, The nature of emotions. Am. Sci. 89(4), 344 (2001)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  35. C. St Louis, G. Zorlu, Can Twitter predict disease outbreaks? Br. Med. J. 344, 1–3 (2012)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 177–184

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 349–358

    Google Scholar 

  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. 151–160

    Google Scholar 

  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. 1079–1088

    Google Scholar 

  44. G. Weimann, Terror on Facebook, Twitter, and Youtube. Brown J. World Affairs 16(2), 45–54 (2010)

    Google Scholar 

  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. 65–71

    Google Scholar 

  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. 1–14

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Vienna University of Economics and Business, Wien, Austria

    Ema Kušen & Mark Strembeck

  2. Secure Business Austria Research Center (SBA), Wien, Austria

    Mark Strembeck

  3. Complexity Science Hub Vienna (CSH), Wien, Austria

    Mark Strembeck

  4. Università di Padova, Padua, Italy

    Mauro Conti

Authors
  1. Ema Kušen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Strembeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauro Conti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark Strembeck .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Firat University, Elazig, Türkiye

    Mehmet Kaya

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kušen, E., Strembeck, M., Conti, M. (2019). Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube. In: Kaya, M., Alhajj, R. (eds) Influence and Behavior Analysis in Social Networks and Social Media. ASONAM 2018. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-02592-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02592-2_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02591-5

  • Online ISBN: 978-3-030-02592-2

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics