Skip to main content

Beyond Groups: Uncovering Dynamic Communities on the WhatsApp Network of Information Dissemination

  • Conference paper
  • First Online:
Book cover Social Informatics (SocInfo 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12467))

Included in the following conference series:

Abstract

In this paper, we investigate the network of information dissemination that emerges from group communication in the increasingly popular WhatsApp platform. We aim to reveal properties of the underlying structure that facilitates content spread in the system, despite limitations the application imposes in group membership. Our analyses reveal a number of strongly connected user communities that cross the boundaries of groups, suggesting that such boundaries offer little constraint to information spread. We also show that, despite frequent changes in community membership, there are consistent co-sharing activities among some users which, even while holding broad content diversity, lead to high coverage of the network in terms of groups and individual users.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Indeed, our data contains only cellular phone numbers. Thus, we are not able to identify the same user with multiple phone numbers.

References

  1. Abbe, E.: Community detection and stochastic block models: recent developments. J. Mach. Learn. Res. 18, 6446–6531 (2017)

    MathSciNet  Google Scholar 

  2. Agrawal, K., Garg, S., Patel, P.: Spatio-temporal outlier detection technique. Int. J. Comput. Sci. Commun. 6, 330–337 (2015)

    Google Scholar 

  3. Barabási, A.L., et al.: Network Science. Cambridge University Press, Cambridge (2016)

    MATH  Google Scholar 

  4. Benson, A.R., Abebe, R., Schaub, M.T., Jadbabaie, A., Kleinberg, J.: Simplicial closure and higher-order link prediction. In: Proceedings of the National Academy of Sciences, pp. 11221–11230 (2018)

    Google Scholar 

  5. Benson, A.R., Kumar, R., Tomkins, A.: Sequences of sets. In: Proceedings of the 24th ACM International Conference on Knowledge Discovery & Data Mining (2018)

    Google Scholar 

  6. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008, 10008 (2008)

    Article  MATH  Google Scholar 

  7. Bursztyn, V.S., Birnbaum, L.: Thousands of small, constant rallies: a large-scale analysis of partisan Whatsapp groups. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2019)

    Google Scholar 

  8. Caetano, J.A., Magno, G., Gonçalves, M.A., Almeida, J.M., Marques-Neto, H.T., Almeida, V.A.F.: Characterizing attention cascades in whatsapp groups. In: Boldi, P., Welles, B.F., Kinder-Kurlanda, K., Wilson, C., Peters, I., Jr., W.M. (eds.) Proceedings of the 10th ACM Conference on Web Science, pp. 27–36 (2019)

    Google Scholar 

  9. Coscia, M., Neffke, F.M.: Network backboning with noisy data. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (2018)

    Google Scholar 

  10. Gomes Ferreira, C.H., de Sousa Matos, B., Almeira, J.M.: Analyzing dynamic ideological communities in congressional voting networks. In: Staab, S., Koltsova, O., Ignatov, D.I. (eds.) SocInfo 2018. LNCS, vol. 11185, pp. 257–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01129-1_16

    Chapter  Google Scholar 

  11. Ferreira, C.H., Murai, F., de Souza Matos, B., de Almeida, J.M.: Modeling dynamic ideological behavior in political networks. J. Web Sci. 1, 1–14 (2019)

    Google Scholar 

  12. Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)

    Article  MathSciNet  Google Scholar 

  13. de Freitas Melo, P., Vieira, C.C., Garimella, K., de Melo, P.O.S.V., Benevenuto, F.: Can Whatsapp counter misinformation by limiting message forwarding? In: Cherifi, H., Gaito, S., Mendes, J.F., Moro, E., Rocha, L.M. (eds.) COMPLEX NETWORKS 2019. SCI, vol. 881, pp. 372–384. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36687-2_31

    Chapter  Google Scholar 

  14. Fu, X., Yu, S., Benson, A.R.: Modelling and analysis of tagging networks in stack exchange communities. J. Complex Netw. (2019)

    Google Scholar 

  15. Ghalmane, Z., Cherifi, C., Cherifi, H., El Hassouni, M.: Centrality in complex networks with overlapping community structure. Sci. Rep. 9, 1–29 (2019)

    Article  Google Scholar 

  16. Gilbert, F., Simonetto, P., Zaidi, F., Jourdan, F., Bourqui, R.: Communities and hierarchical structures in dynamic social networks: analysis and visualization. Soc. Netw. Anal. Min 1, 83–95 (2011). https://doi.org/10.1007/s13278-010-0002-8

    Article  Google Scholar 

  17. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences, pp. 7821–7826 (2002)

    Google Scholar 

  18. Goel, V.: How whatsapp leads mobs to murder in India, July 2018. https://www.nytimes.com/interactive/2018/07/18/technology/whatsapp-india-killings.html. Posted on 18 July 2018

  19. Hoffmann, T., Peel, L., Lambiotte, R., Jones, N.S.: Community detection in networks without observing edges. Sci. Adv. 6(4), eaav1478 (2020)

    Article  Google Scholar 

  20. Jaccard, P.: Etude de la distribution florale dans une portion des alpes et du jura. Bulletin de la Societe Vaudoise des Sciences Naturelles, pp. 547–579 (1901)

    Google Scholar 

  21. Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 117–128 (2011)

    Article  Google Scholar 

  22. Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! understanding the functional building blocks of social media. Bus. Horiz. 54, 241–251 (2011)

    Article  Google Scholar 

  23. Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., Kompatsiaris, I.: FIVR: fine-grained incident video retrieval. IEEE Trans. Multimedia 21, 2638–2652 (2019)

    Article  Google Scholar 

  24. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Yu, P., Han, J., Faloutsos, C. (eds.) Link Mining: Models, Algorithms, and Applications. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6515-8_13

    Chapter  Google Scholar 

  25. Kumar, S., Shah, N.: False information on web and social media: a survey. In: Advances and Applications, Social Media Analytics (2018)

    Google Scholar 

  26. Lancichinetti, A., Fortunato, S.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11, 033015 (2009)

    Article  Google Scholar 

  27. Magenta, M., Gragnani, J., Souza, F.: How Whatsapp is being abused in Brazil’s election, October 2018. https://www.bbc.com/news/technology-45956557. Acessed on 24 May 2020

  28. Maros, A., Almeida, J., Benevenuto, F., Vasconcelos, M.: Analyzing the use of audio messages in Whatsapp groups. In: Proceedings of The Web Conference (2020)

    Google Scholar 

  29. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates, Inc. (2013)

    Google Scholar 

  30. Newman, M.: Network structure from rich but noisy data. Nat. Phys. 14, 542–545 (2018)

    Article  Google Scholar 

  31. Onnela, J.P., Saramäki, J., Kertész, J., Kaski, K.: Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E 71, 065103 (2005)

    Article  Google Scholar 

  32. Papadopoulos, S., Kompatsiaris, I., Vakali, A., Spyridonos, P.: Community detection in social media. Data Min. Knowl. Disc. 24, 515–554 (2012). https://doi.org/10.1007/s10618-011-0224-z

    Article  Google Scholar 

  33. Resende, G., Melo, P., C.dS. Reis, J., Vasconcelos, M., Almeida, J.M., Benevenuto, F.: Analyzing textual (mis)information shared in Whatsapp groups. In: Proceedings of the 10th ACM Conference on Web Science. WebSci 2019. Association for Computing Machinery (2019)

    Google Scholar 

  34. Resende, G., Melo, P.F., Sousa, H., Messias, J., Vasconcelos, M., Almeida, J.M., Benevenuto, F.: (Mis)information dissemination in whatsapp: Gathering, analyzing and countermeasures. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13–17, 2019, pp. 818–828. ACM (2019)

    Google Scholar 

  35. Rossetti, G., Cazabet, R.: Community discovery in dynamic networks: a survey. ACM Comput. Surv. (CSUR) 51, 1–37 (2018)

    Article  Google Scholar 

  36. Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F.: Tiles: an online algorithm for community discovery in dynamic social networks. Mach. Learn. 106(8), 1213–1241 (2016). https://doi.org/10.1007/s10994-016-5582-8

    Article  MathSciNet  Google Scholar 

  37. Serrano, M.A., Boguna, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. Proc. Natl. Acad. Sci. 106, 6483–6488 (2009)

    Article  Google Scholar 

  38. Song, J., Yang, Y., Huang, Z., Shen, H.T., Hong, R.: Multiple feature hashing for real-time large scale near-duplicate video retrieval. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 423–432 (2011)

    Google Scholar 

  39. Tang, L., Liu, H., Zhang, J., Nazeri, Z.: Community evolution in dynamic multi-mode networks. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 677–685 (2008)

    Google Scholar 

  40. WhatsApp: two billion users on WhatsApp. https://blog.whatsapp.com/10000666/Two-Billion-Users-Connecting-the-World-Privately. Accessed 19 May 2020

  41. WhatsApp: about WhatsApp (2020). https://www.whatsapp.com/about/. Accessed 19 May 2020

  42. WhatsApp: keeping WhatsApp personal and private. https://blog.whatsapp.com/Keeping-WhatsApp-Personal-and-Private (2020). Accessed 19 May 2020

  43. Zauner, C., Steinebach, M., Hermann, E.: Rihamark: perceptual image hash benchmarking. In: Memon, N.D., Dittmann, J., Alattar, A.M., Delp III, E.J. (eds.) Media Watermarking, Security, and Forensics III, vol. 7880. SPIE, Bellingham (2011)

    Google Scholar 

  44. Zhang, X.S., et al.: Modularity optimization in community detection of complex networks. EPL (Europhys. Lett.) 87, 38002 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by grants from FAPEMIG, CNPQ and CAPES.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gabriel Peres Nobre .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nobre, G.P., Ferreira, C.H.G., Almeida, J.M. (2020). Beyond Groups: Uncovering Dynamic Communities on the WhatsApp Network of Information Dissemination. In: Aref, S., et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60975-7_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60974-0

  • Online ISBN: 978-3-030-60975-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics