Recruiting from the Network: Discovering Twitter Users Who Can Help Combat Zika Epidemics

  • Paolo Missier
  • Callum McClean
  • Jonathan CarltonEmail author
  • Diego Cedrim
  • Leonardo Silva
  • Alessandro Garcia
  • Alexandre Plastino
  • Alexander Romanovsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)


Tropical diseases like Chikungunya and Zika have come to prominence in recent years as the cause of serious health problems. We explore the hypothesis that monitoring and analysis of social media content streams may effectively complement institutional disease prevention efforts. Specifically, we aim to identify selected members of the public who are likely to be sensitive to virus combat initiatives. Focusing on Twitter and on the topic of Zika, our approach involves (i) training a classifier to select topic-relevant tweets from the Twitter feed, and (ii) discovering the top users who are actively posting relevant content about the topic. In this short paper we describe our analytical approach and prototype architecture, discuss the challenges of dealing with noisy and sparse signal, and present encouraging preliminary results.


Relevant Content Topic Focus Social Graph Social Sensor Disease Prevention Program 
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.

Supplementary material


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Carvalho, J., Plastino, A.: An assessment study of features and meta-level features in twitter sentiment analysis. In: ECAI 2016–22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, pp. 769–777 (2016)Google Scholar
  3. 3.
    CDC: Centers for Disease Control and Prevention (2015). Accessed 15 Dec 2015
  4. 4.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)zbMATHGoogle Scholar
  5. 5.
    Chen, C., Gao, D., Li, W., Hou, Y.: Inferring topic-dependent influence roles of twitter users. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, NY, USA, pp. 1203–1206. ACM, New York (2014)Google Scholar
  6. 6.
    Horne, B.D., Nevo, D., Freitas, J., Ji, H., Adali, S.: Expertise in social networks: how do experts differ from other users? In: Proceedings of the Tenth International AAAI Conference on Web and Social Media, vol. 10, pp. 583–586 (2016)Google Scholar
  7. 7.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)CrossRefzbMATHGoogle Scholar
  8. 8.
    Miles, T., Hirschler, B.: Zika virus set to spread across americas, spurring vaccine hunt, January 2016Google Scholar
  9. 9.
    Missier, P., Mcclean, C., Carlton, J., Cedrim, D., Silva, L., Garcia, A., Plastino, A., Romanovsky, A.: Recruiting from the network: discovering Twitter users who can help combat Zika epidemics. Research report, School of Computing Science, Newcastle University (2017).
  10. 10.
    Missier, P., Romanovsky, A., Miu, T., Pal, A., Daniilakis, M., Garcia, A., Cedrim, D., Silva Sousa, L.: Tracking dengue epidemics using twitter content classification and topic modelling. In: Casteleyn, S., Dolog, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9881, pp. 80–92. Springer, Cham (2016). doi: 10.1007/978-3-319-46963-8_7 CrossRefGoogle Scholar
  11. 11.
    Nagarajan, M., Gomadam, K., Sheth, A.P., Ranabahu, A., Mutharaju, R., Jadhav, A.: Spatio-temporal-thematic analysis of citizen sensor data: challenges and experiences. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 539–553. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04409-0_52 CrossRefGoogle Scholar
  12. 12.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of WWW 2010, p. 851 (2010)Google Scholar
  13. 13.
    Wei, W., Cong, G., Miao, C., Zhu, F., Li, G.: Learning to find topic experts in twitter via different relations. IEEE Trans. Knowl. Data Eng. 28(7), 1764–1778 (2016)CrossRefGoogle Scholar
  14. 14.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)Google Scholar
  15. 15.
    Yamaguchi, Y., Takahashi, T., Amagasa, T., Kitagawa, H.: TURank: twitter user ranking based on user-tweet graph analysis. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 240–253. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17616-6_22 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Paolo Missier
    • 1
  • Callum McClean
    • 1
  • Jonathan Carlton
    • 1
    Email author
  • Diego Cedrim
    • 2
  • Leonardo Silva
    • 2
  • Alessandro Garcia
    • 2
  • Alexandre Plastino
    • 3
  • Alexander Romanovsky
    • 1
  1. 1.School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
  2. 2.PUC-RioRio de JaneiroBrazil
  3. 3.Universidad Federal FluminenseNiteròiBrazil

Personalised recommendations