Advertisement

Good Friends, Bad News - Affect and Virality in Twitter

  • Lars Kai Hansen
  • Adam Arvidsson
  • Finn Aarup Nielsen
  • Elanor Colleoni
  • Michael Etter
Part of the Communications in Computer and Information Science book series (CCIS, volume 185)

Abstract

The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. The basic measure of virality in Twitter is the probability of retweet and we are interested in which dimensions of the content of a tweet leads to retweeting. We hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. Our findings may be summarized ‘If you want to be cited: Sweet talk your friends or serve bad news to the public’.

Keywords

Sentiment Analysis News Medium Negative Content Negative Sentiment News Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ahonen, T., Moore, A.: Communities Dominate Brands:Business and Marketing Challenges for the 21st Century. FutureText, London, UK (2005)Google Scholar
  2. 2.
    Berger, J., Milkman, K.: Social transmission, emotion, and the virality of online content. Wharton Research Paper (2010)Google Scholar
  3. 3.
    Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In: Hawaii Int. Conf. on System Sciences, pp. 1–10 (2010)Google Scholar
  4. 4.
    Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): Stimuli, instruction manual, and affective ratings. Technical report, Center for Research in Psychophysiology, University of Florida, Gainesville, Florida (1999)Google Scholar
  5. 5.
    Dobele, A., Lindgreen, A., Beverland, M., Vanhamme, J., Vanwijk, R.: Why pass on viral messages? because they connect emotionally. Business Horizons 50(4), 291–304 (2007)CrossRefGoogle Scholar
  6. 6.
    Eilders, C.: News factors and news decisions. theoretical and methodological advances in germany. Communications: The European Journal of Communication Research 31(1), 5–24 (2006)CrossRefGoogle Scholar
  7. 7.
    Francis, W.N., Kucera, H.: Brown corpus manual. Technical report, Department of Linguistics, Brown University, Providence, Rhode Island, US (1979)Google Scholar
  8. 8.
    Galtung, J., Ruge, M.: The structure of foreign news: The presentation of the congo, cuba and cyprus crises in four norwegian newspapers. Journal of Peace Research 2(1), 64–90 (1965)CrossRefGoogle Scholar
  9. 9.
    Godin, S.: Unleashing the Ideavirus (2001)Google Scholar
  10. 10.
    Harcup, T., O’Neill, D.: What is news? galtung and ruge revisited. Journalism Studies 2(2), 261–280 (1981)CrossRefGoogle Scholar
  11. 11.
    Honeycutt, C., Herring, S.C.: Beyond microblogging: Conversation and collaboration via twitter. In: Proc. 42nd Hawaii Int. Conf. on System Sciences, pp. 1–10. IEEE Computer Society Press, Washington, DC, USA (2009)Google Scholar
  12. 12.
    Huberman, B., Romero, D., Wu, F.: Social networks that matter: Twitter under the microscope (December 2008), http://arxiv.org/pdf/0812.1045
  13. 13.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: WebKDD/SNA-KDD 2007, pp. 56–65. ACM, New York (2007)Google Scholar
  14. 14.
    Kozinets, R., de Valck, K., Wojnicki, A., Wilner, S.: Networked narratives. understanding word of mouth marketing in online communities. Journal of Marketing 74, 71–89 (2010)CrossRefGoogle Scholar
  15. 15.
    Kwak, H., Lee, C., Parkand, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600. ACM, New York (2010)Google Scholar
  16. 16.
    Lazarsfeldt, P., Katz, E.: Personal Influence. The Free Press, Glecoe (1955)Google Scholar
  17. 17.
    Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web (2007)Google Scholar
  18. 18.
    Lippman, W.: Public Opinion. MacMillan, New York (1922)Google Scholar
  19. 19.
    Loper, E., Bird, S.: NLTK: The natural language toolkit. In: Proc. of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics, vol. 1, pp. 63–70. Association for Computational Linguistics, Morristown (2002)CrossRefGoogle Scholar
  20. 20.
    Miller, V.: New media, networking and phatic culture. Convergence: The Int. Journal of Research into New Media Technologies 14(4), 387–400 (2008)Google Scholar
  21. 21.
    Nelder, J.A., Wedderburn, R.W.M.: Generalized linear models. Journal of the Royal Statistical Society, Series A, General 135, 370–384 (1972)CrossRefGoogle Scholar
  22. 22.
    Peterson, S.: International news selection by the elite press: A case study. Public Opinion Quarterly 45(2), 143–163 (1981)CrossRefGoogle Scholar
  23. 23.
    Ravikant, N., Rifkin, A.: Why twitter is massively undervalued compared to facebook (2010), http://techcrunch.com
  24. 24.
    Rish, I.: An empirical study of the naive Bayes classifier. In: Int. Joint Conf. on Artificial Intelligence, pp. 41–46 (2001)Google Scholar
  25. 25.
    Rogers, E.: Diffusion of Innovations. The Free Press, New York (1962)Google Scholar
  26. 26.
    Ruhrmann, G., Woelke, J., Maier, M., Diehlmann, N.: Der Wert von Nachrichten im deutschen Fernsehen: Ein Modell zur Validierung von Nachrichtenfaktoren. Leske Budrich, New York (2003)CrossRefGoogle Scholar
  27. 27.
    Schulz, W.: News structure and people’s awareness of political events. International Communication Gazette 30, 139–153 (1982)CrossRefGoogle Scholar
  28. 28.
    Schwarz, A.: The theory of newsworthiness applied to Mexico’s press. Communications: The European Journal of Communication Research 31(1), 45–64 (2006)CrossRefGoogle Scholar
  29. 29.
    Staab, F.: The role of news factors in news selection: A theoretical reconsideration. European Journal of Communication 5, 423–443 (1990)CrossRefGoogle Scholar
  30. 30.
    Straughan, M.: An experiment on the relation between news values and reader. International Communication Gazette 43, 93–107 (1989)CrossRefGoogle Scholar
  31. 31.
    Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: Social Computing / IEEE Int. Conf. on Privacy, Security, Risk and Trust, pp. 177–184 (2010)Google Scholar
  32. 32.
    Tarde, G.: The law of Imitation. Holt and Company, New York (1903)Google Scholar
  33. 33.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topicsensitive influential twitterers. In: Proc. of the Third ACM Int. Conf. on Web Search and Data Mining, WSDM 2010, pp. 261–270. ACM, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lars Kai Hansen
    • 1
  • Adam Arvidsson
    • 2
  • Finn Aarup Nielsen
    • 1
  • Elanor Colleoni
    • 3
  • Michael Etter
    • 3
  1. 1.DTU InformaticsTechnical University of DenmarkLyngbyDenmark
  2. 2.University of MilanMilanItaly
  3. 3.Copenhagen Business SchoolFrederiksbergDenmark

Personalised recommendations