The effect of interventions on Twitter in four target groups using different measures of influence

  • Peter-Paul van MaanenEmail author
  • Remco Wijn
  • Erik Boertjes
Original Article


In this paper, the influence of interventions on Twitter users is studied. We define influence in (a) number of participants, (b) size of the audience, (c) amount of activity, and (d) reach. Influence is studied for four different target groups: (a) politicians, (b) journalists, (c) employees and (d) the general public. Furthermore, two types of interventions are studied: (a) by all Twitter users (i.e., uncontrolled interventions), and (b) those tweeted by an organization that benefits from any resulting influence (i.e., controlled interventions). As a case study, tweets about a large Dutch governmental organization are used. Results show a relation between the number of uncontrolled interventions and influence in all four target groups, for each of the defined types of influence. Controlled interventions show less influence: significant influence was found for the general public, but influence for politicians and employees was only mildly significant, and no influence was found for journalists. The effect found for uncontrolled interventions, however, suggests that this influence is indeed reachable for some target groups, even when the number of interventions is small, and very well reachable for all target groups, provided the number of interventions is large enough. In addition to this, we found that interventions influence groups to a different extent. Own employees were influenced strongest, differing significantly from the other groups.


Social media Twitter Social influence 



This research has been funded by the TNO Enabling Technology Program “Behavior and Innovation”. The authors would furthermore like to thank Olav Aarts, Jan Maarten Schraagen, Nadia Jansen and Tineke Hof for their efforts to make this research possible.


  1. Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web, pp 519–528Google Scholar
  2. Cacioppo JT, Petty RE (1980) Persuasiveness of communications is affected by exposure frequency and message quality: a theoretical and empirical analysis of persisting attitude change. Current Issues Research Advert 3(1):97–122Google Scholar
  3. Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in Twitter: the million follower fallacy. ICWSM 10:10–17Google Scholar
  4. Cialdini RB (2001) Influence: science and practice, vol 4. Allyn and Bacon, BostonGoogle Scholar
  5. Dugan L (2011) 5 Tools to measure your Twitter influence.
  6. Van der Eijk C (2000) The Netherlands: media and politics between segmented pluralism and market forces. In: Gunther R, Mughan A (eds.) Democracy and the media: a comparative perspective, chap. 9, Cambridge University Press, Cambridge, pp 303–342Google Scholar
  7. Grabowicz PA, Ramasco JJ, Moro E, Pujol JM, Eguiluz VM (2012) Social features of online networks: the strength of intermediary ties in online social media. PLoS One 7(1):E29358CrossRefGoogle Scholar
  8. Huberman BA, Romero DM, Wu F (2009) Social networks that matter: Twitter under the microscope. First Monday 14(1)Google Scholar
  9. Kaptein M, Nass C, Parvinen P, Markopoulos P (2013) Nice to know you: familiarity and influence in social networks. In: Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS), pp 2745–2752Google Scholar
  10. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), pp 137–146Google Scholar
  11. Knowles ES, Linn JA (2003) Resistance and persuasion. Lawrence ErlbaumGoogle Scholar
  12. Mainwaring S (2011) The new power of consumers to influence brands.
  13. Mehta R, Mehta D, Chheda D, Shah C, Chawan PM (2012) Sentiment analysis and influence tracking using Twitter. Int J Adv Research Comput Sci Electron Eng 1(2):72–79Google Scholar
  14. Meyer T, Hinchman LP (2002) Media democracy: how the media colonize politics. Polity Press, OxfordGoogle Scholar
  15. Pornpitakpan C (2004) The persuasiveness of source credibility: a critical review of five decades’ evidence. J Appl Social Psychol 34(2):243–281CrossRefGoogle Scholar
  16. Rosenman ETR (2012) Retweets–but not just retweets: Quantifying and predicting influence on Twitter, Bachelor’s thesis, applied mathematics. Harvard College, CambridgeGoogle Scholar
  17. Rotemberg JJ (1999) A heuristic method for extracting smooth trends from economic time series. Tech. rep, National Bureau of Economic Research, IncGoogle Scholar
  18. Sassenberg K, Boos M (2003) Attitude change in computer-mediated communication: effects of anonymity and category norms. Group Process Intergroup Relat 6(4):405–422CrossRefGoogle Scholar
  19. Sullivan D (2011) Why “second chance” tweets matter: After 3 hours, few care about socially shared links.
  20. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558CrossRefGoogle Scholar
  21. Wijn R, van den Bos K (2010) On the social-communicative function of justice: the influence of communication goals and personal involvement on the use of justice assertions. Personal Soc Psychol Bulletin 36(2):161–172CrossRefGoogle Scholar
  22. Yang J, Counts S (2010) Predicting the speed, scale, and range of information diffusion in Twitter. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social MediaGoogle Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Peter-Paul van Maanen
    • 1
    Email author
  • Remco Wijn
    • 1
  • Erik Boertjes
    • 1
  1. 1.Netherlands Organisation for Applied Scientific Research (TNO)DelftThe Netherlands

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