Social roles and structural signatures of top influentials in the #prayforparis Twitter network

  • Miyoung ChongEmail author
  • Hae Jung Maria Kim


Scholars have shown much interest in whether diffusion is inflated through planting a piece of information by influential people (influentials). Although a few attempts have been made to discover structural gaps or gap fillers in the Twitter network, these efforts mainly concentrated on applying topological approaches to detect influentials in online networks. Further, though many studies explored diffusion on the Twitter network, they rarely examined the phenomenon with a theoretical framework. Through the #prayforparis Twitter network, this study attempted (1) to identify top influentials by applying multiple centrality measures and word frequency measures and (2) to examine social roles based on structural signatures of the Twitter network through the lens of the Diffusion of Innovation Theory. To fulfill the objectives of this study, the authors employed an innovative multi-method approach combining Social Network Analysis, word frequency analysis via NodeXL and R, and a qualitative approach to examine behavioral and structural relationships of the #prayforparis Twitter network. Top influentials of the network were pop music celebrities who shared condolences to the victims of the 2015 Paris attacks through their tweets. This study identified “celebrity” and “fan” as social roles based on the structural and qualitative analysis of the network as well as metrical examinations, including indegree and outdegree counts of the social roles of the “celebrities” and “fans.” Justin Bieber, the most dominant influential individual in the #prayforparis Twitter network, functioned as a breaking news provider through his tweet about the death of his friend during the Paris attacks. By filling the gap from the past studies, this study utilizes the theoretical improvement in the diffusion research as well as contributes to the methodological approach about influentials and social roles in the Twitter network.


Twitter Diffusion of innovation Social network analysis Prayforparis Social roles Influentials Structural signatures 



  1. About different types of Tweets. (n.d.). Retrieved from
  2. Al-garadi, M.A., Varathan, K.D., Ravana, S.D.: Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method. Phys. A Stat. Mech. Appl. 468, 278–288 (2017)CrossRefGoogle Scholar
  3. Anger, I., Kittl, C.: Measuring influence on Twitter. In: Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies 31 (2011 September)Google Scholar
  4. Bartal, A., Ravid, G.: Member behavior in dynamic online communities: role affiliation frequency model. IEEE Trans. Knowl. Data Eng. (2019). CrossRefGoogle Scholar
  5. Bhowmick, A.K., Gueuning, M., Delvenne, J.C., Lambiotte, R., Mitra, B.: Temporal sequence of retweets help to detect influential nodes in social networks. IEEE Trans. Comput. Soc. Syst. 6(3), 441–455 (2019)CrossRefGoogle Scholar
  6. Burkhalter, B., Smith, M.: Inhabitant’s uses and reactions to usenet social accounting data. Inhabited Information Spaces, pp. 291–305. Springer, London (2004)CrossRefGoogle Scholar
  7. Burt, R.S.: The social capital of opinion leaders. Ann. Am. Acad. Political Soc. Sci. 566(1), 37–54 (1999)CrossRefGoogle Scholar
  8. Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: the million follower fallacy. ICWSM 10(10–17), 30 (2010)Google Scholar
  9. Chang, H.C.: A new perspective on Twitter hashtag use: diffusion of innovation theory. Proc. Am. Soc. Inf. Sci. Technol. 47(1), 1–4 (2010)Google Scholar
  10. Chen, B., Kirkley, D., Raible, J.: Applying diffusion of innovation model to embrace Web 2.0 technologies: implementing an institutional strategy. In: Presentation at the Sloan-C International Symposium, pp. 7–9. Carefree, AZ, May (2008)Google Scholar
  11. Cvetojevic, S., Hochmair, H.H.: Analyzing the spread of tweets in response to Paris attacks. Comput. Environ. Urb. Syst. 71, 14–26 (2018)CrossRefGoogle Scholar
  12. Chong, M.: Sentiment analysis and topic extraction of the twitter network of #prayforparis. Proc. Assoc. Inf. Sci. Technol. 53(1), 1–4 (2016)CrossRefGoogle Scholar
  13. Chong, M., Chang, H.C.: Social Media Analytics. In: Hawamdeh, S., Chang, H.C. (eds.) Analytics and Knowledge Management, pp. 215–240. Auerbach Publications, New York (2018)Google Scholar
  14. Collins, S.G.: Twitter in place examining Seoul’s Gwanghwamun plaza through social media activism. Digit. Cult. Soc. 3(2), 99–122 (2017)CrossRefGoogle Scholar
  15. Dong, R., Li, L., Zhang, Q., Cai, G.: Information diffusion on social media during natural disasters. IEEE Trans. Comput. Soc. Syst. 5(1), 265–276 (2018)CrossRefGoogle Scholar
  16. Elgot, J., Phipps, C., Bucks, J.: Paris attacks: Islamic state says killings were response to Syria strikes (14 November 2015).
  17. Eleni, S., Milaiou, E., Karyotis, V., Papavassiliou, S.: Temporal dynamics of information diffusion in Twitter: modeling and experimentation. IEEE Trans. Comput. Soc. Syst. 5(1), 256–264 (2018)CrossRefGoogle Scholar
  18. Faust, K., Skvoretz, J.: Comparing networks across space and time, size and species. Sociol. Methodol. 32(1), 267–299 (2002)CrossRefGoogle Scholar
  19. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  20. Goldenberg, D., Sela, A., Shmueli, E.: Timing matters: influence maximization in social networks through scheduled seeding. IEEE Trans. Comput. Soc. Syst. 99, 1–18 (2018)Google Scholar
  21. Golder, S.A.: A Typology of Social Roles in Usenet. Unpublished Senior Honors, Harvard University, Cambridge (2003)Google Scholar
  22. Hansen, D.L., Shneiderman, B., Smith, M.A.: Analyzing Social Media Networks with NodeXL: Insights from a Connected World. Morgan Kaufmann, Boston (2011)Google Scholar
  23. Halvey, M.J., Keane, M.T.: An assessment of tag presentation techniques. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1313-1314 (2007, May)Google Scholar
  24. Hara, N., Sanfilippo, M.R.: Analysis of roles in engaging contentious online discussions in science. J. Assoc. Inf. Sci. Technol. 68(8), 1953–1966 (2017)CrossRefGoogle Scholar
  25. He, L., Lu, C.T., Ma, J., Cao, J., Shen, L., Yu, P.S.: Joint community and structural hole spanner detection via harmonic modularity. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.875–884 (2016, August)Google Scholar
  26. Huang, S., Lv, T., Zhang, X., Yang, Y., Zheng, W., Wen, C.: Identifying node role in social network based on multiple indicators. PLoS ONE 9(8), e103733 (2014)CrossRefGoogle Scholar
  27. Johann, M., Bülow, L.: One does not simply create a meme: conditions for the diffusion of internet memes. Int. J. Commun. 13, 23 (2019)Google Scholar
  28. Knoke, D., Kuklinski, J.H.: Network analysis. In: Saga University Paper Series on Quantitative Applications in the Social Sciences 7(028), (1982)Google Scholar
  29. Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, Princeton (2011)Google Scholar
  30. Lee, J., Agrawal, M., Rao, H.R.: Message diffusion through social network service: the case of rumor and non-rumor related tweets during Boston bombing 2013. Inf. Syst. Front. 17(5), 997–1005 (2015)CrossRefGoogle Scholar
  31. Lee, A.J., Yang, F.-C., Tsai, H.-C., Lai, Y.-Y.: Discovering content-based behavioral roles in social networks. Decis. Support Syst. 59, 250–261 (2014)CrossRefGoogle Scholar
  32. Liang, H., Fung, I.C.H., Tse, Z.T.H., Yin, J., Chan, C.H., Pechta, L.E., Fu, K.W.: How did Ebola information spread on twitter: broadcasting or viral spreading? BMC Public Health 19(1), 438 (2019)CrossRefGoogle Scholar
  33. Liu, Y., Du, F., Sun, J., Silva, T., Jiang, Y., Zhu, T.: Identifying social roles using heterogeneous features in online social networks. J. Assoc. Inf. Sci. Technol. 70(7), 660–674 (2019)Google Scholar
  34. Maia, M., Almeida, J., Almeida, V.: Identifying user behavior in online social networks. In: Paper presented at the 1st Workshop on Social Network Systems. Glasgow: Scotland, UK (2008, April)Google Scholar
  35. McCullen, N.J., Rucklidge, A.M., Bale, C.S., Foxon, T.J., Gale, W.F.: Multiparameter models of innovation diffusion on complex networks. SIAM J. Appl. Dyn. Syst. 12(1), 515–532 (2013)CrossRefGoogle Scholar
  36. Morris, M., Ogan, C.: The internet as mass medium. J. Commun. 45(1), 39–50 (1996)CrossRefGoogle Scholar
  37. Nadel, S.F.: Theory of Social Structure. Macmillan, New York (1964)Google Scholar
  38. Newman, D., Bonilla, E.V., Buntine, W.: Improving topic coherence with regularized topic models. In: Paper Presented at the 24th International Conference on Neural Information Processing Systems Granada, Spain (2011, December)Google Scholar
  39. Otte, E., Rousseau, R.: Social network analysis: a powerful strategy, also for the information sciences. J. Inf. Sci. 28(6), 441–453 (2002)CrossRefGoogle Scholar
  40. Park, H.W., Thelwall, M.: Link analysis: hyperlink patterns and social structure on politicians’ web sites in South Korea. Qual. Quant. 42(5), 687–697 (2008)CrossRefGoogle Scholar
  41. Rosenberg, S.: #PrayforParis: The virality of international terrorism and Western media’s insidious nature. The Michigan Daily (2015, November 15). Retrieved from
  42. Rogers Everett, M.: Diffusion of Innovations. Free Press, New York (2003)Google Scholar
  43. Rogers Everett, M.: Diffusion of Innovations. Free Press, New York (1995)Google Scholar
  44. Rogers Everett, M.: Diffusion of Innovations. Free Press, New York (1962)Google Scholar
  45. Rogers E. M., Seidel, N.: Diffusion of news of the terrorist attacks of September 11, 2001. Prometheus 20(3):209–219 (2002)CrossRefGoogle Scholar
  46. Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704 (2011, March)Google Scholar
  47. Rossi, R.A., Gallagher, B., Neville, J., Henderson, K.: Modeling dynamic behavior in large evolving graphs. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 667–676 (2013, February)Google Scholar
  48. Scott, J.: Social Network Analysis. Sage, Thousand Oaks (2017)Google Scholar
  49. Smith, M., Rainie, L., Shneiderman, B., Himelboim, I.: Mapping Twitter topic networks: from polarized crowds to community clusters, Pew Research Center (2014). Accessed 28 Nov 2018
  50. Statisca.: Social Media Advertising - worldwide | Statista Market Forecast (2018). Accessed 16 Jan 2019
  51. Stefanone, M.A., Saxton, G.D., Egnoto, M.J., Wei, W., Fu, Y.: Image attributes and diffusion via Twitter: the case of# guncontrol. In: 2015 48th Hawaii International Conference on System Sciences, pp. 1788–1797. IEEE (2015, January)Google Scholar
  52. The New York Times.: Three Hours of Terror in Paris, Moment by Moment (2015, Nov 13). Retrieved from
  53. The R foundation.: Accessed 18 Jan 2019
  54. Tonkin, E., Pfeiffer, H.D., Tourte, G.: Twitter, information sharing and the London riots? ASIST 38(2), 49–57 (2012)Google Scholar
  55. Toptrends/2015., 2015. Accessed 1 Jun 2016
  56. Valente, T.W., Davis, R.L.: Accelerating the diffusion of innovations using opinion leaders. Ann. Am. Acade. Political Soc. Sci. 566(1), 55–67 (1999)CrossRefGoogle Scholar
  57. Vollmer, C., Precourt, G.: Always on: Advertising, Marketing, and Media in An Era of Consumer Control. McGraw-Hill, New York (2008)Google Scholar
  58. Wallach, H.M., Murray, I., Salakhutdinov, R., Mimno, D.: Evaluation methods for topic models. In: Paper Presented at the 26th Annual International Conference on Machine Learning, Montreal, Quebec, Canada (2009, June)Google Scholar
  59. Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (pp. 1031–1040). ACM (2011, October)Google Scholar
  60. Weimann, G., Tustin, D.H., Van Vuuren, D., Joubert, J.P.R.: Looking for opinion leaders: traditional versus modern measures in traditional societies. Int. J. Pub. Opin. Res. 19(2), 173–190 (2007)CrossRefGoogle Scholar
  61. Welser, H.T., Cosley, D., Kossinets, G., Lin, A., Dokshin, F., Gay, G., Smith, M.: Finding social roles in wikipedia. In: Paper Presented at the 2011 iConference, Seattle, WA (2011, February)Google Scholar
  62. Welser, H.T., Gleave, E., Fisher, D., Smith, M.: Visualizing the signatures of social roles in online discussion groups. J. Soc. Struct. 8(2), 1–32 (2007)Google Scholar
  63. Wetherell, C., Plakans, A., Wellman, B.: Social networks, kinship, and community in eastern Europe. J. Interdiscip. Hist. 24(4), 639–663 (1994)CrossRefGoogle Scholar
  64. Winship, C.: Thoughts about roles and relations: an old document revisited. Soc. Netw. 10(3), 209–231 (1988)CrossRefGoogle Scholar
  65. Xu, W.W., Sang, Y., Blasiola, S., Park, H.W.: Predicting opinion leaders in Twitter activism networks: the case of the Wisconsin recall election. Am. Behav. Sci. 58(10), 1278–1293 (2014)CrossRefGoogle Scholar
  66. Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: Paper Presented at the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil (2013, May)Google Scholar
  67. Zhang, J., Tang, J., Zhuang, H., Leung, C. W. K., Li, J.: Role-Aware Conformity Modeling and Analysis in Social Networks. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014, June)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of InformationUniversity of North TexasDentonUSA
  2. 2.Department of Merchandizing and Digital Retailing, College of Merchandising, Hospitality and TourismUniversity of North TexasDentonUSA

Personalised recommendations