Skip to main content

Can WhatsApp Counter Misinformation by Limiting Message Forwarding?

  • Conference paper
  • First Online:
Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Abstract

WhatsApp is the most popular messaging app in the world. The closed nature of the app, in addition to the ease of transferring multimedia and sharing information to large-scale groups make WhatsApp unique among other platforms, where an anonymous encrypted messages can become viral, reaching multiple users in a short period of time. The personal feeling and immediacy of messages directly delivered to the user’s phone on WhatsApp was extensively abused to spread unfounded rumors and create misinformation campaigns during recent elections in Brazil and India. WhatsApp has been deploying measures to mitigate this problem, such as reducing the limit for forwarding a message to at most five users at once. Despite the welcomed effort to counter the problem, there is no evidence so far on the real effectiveness of such restrictions. In this work, we propose a methodology to evaluate the effectiveness of such measures on the spreading of misinformation circulating on WhatsApp. We use an epidemiological model and real data gathered from WhatsApp in Brazil, India and Indonesia to assess the impact of limiting virality features in this kind of network. Our results suggest that the current efforts deployed by WhatsApp can offer delays on the information spread, but are ineffective in blocking the propagation of misinformation campaigns in public groups.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://blog.whatsapp.com/10000631/Connectinganuser-users-all-days.

  2. 2.

    https://www.latimes.com/world/la-fg-india-whatsapp-2019-story.html.

  3. 3.

    blog.whatsapp.com/10000647/More-changes-to-forwarding.

  4. 4.

    https://www.bbc.com/news/world-asia-india-47797151.

  5. 5.

    https://www.bbc.com/news/technology-45956557.

  6. 6.

    https://time.com/5512032/whatsapp-india-election-2019/.

  7. 7.

    In our data, some groups have more than 256 members, because our data is a temporal snapshot and members can leave and join groups during this time.

References

  1. Arun, C.: On WhatsApp, rumours, and lynchings. Econ. Polit. Weekly 54(6), 30–35 (2019)

    Google Scholar 

  2. Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: The World Wide Web Conference, pp. 519–528 (2012)

    Google Scholar 

  3. Bessi, A., Ferrara, E.: Social bots distort the 2016 us presidential election online discussion. First Monday 21(11–7) (2016)

    Google Scholar 

  4. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  5. 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 (ASONAM) (2019)

    Google Scholar 

  6. Garimella, K., Tyson, G.: Whatapp doc? A first look at whatsapp public group data. In: International AAAI Conference on Web and Social Media (2018)

    Google Scholar 

  7. Lazer, D.M.J., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., Metzger, M.J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S.A., Sunstein, C.R., Thorson, E.A., Watts, D.J., Zittrain, J.L.: The science of fake news. Science 359(6380), 1094–1096 (2018)

    Article  Google Scholar 

  8. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187 (2005)

    Google Scholar 

  9. Li, G., Zhen, J.: Global stability of an SEI epidemic model with general contact rate. Chaos Solitons Fractals 23(3), 997–1004 (2005)

    MathSciNet  MATH  Google Scholar 

  10. McAuley, J., Leskovec, J.: Image labeling on a network: using social-network metadata for image classification. In: 12th European Conference on Computer Vision (ECCV12) (2012)

    Chapter  Google Scholar 

  11. Melo, P., Messias, J., Resende, G., Garimella, K., Almeida, J., Benevenuto, F.: WhatsApp monitor: a fact-checking system for WhatsApp. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 676–677, July 2019

    Google Scholar 

  12. Newman, N., Fletcher, R., Kalogeropoulos, A., Nielsen, R.K.: Reuters institute digital news report 2019 . Reuters Institute for the Study of Journalism (2019)

    Google Scholar 

  13. Olson, R.S., Neal, Z.P.: Navigating the massive world of reddit: using backbone networks to map user interests in social media. PeerJ Comput. Sci. 1, e4 (2015)

    Article  Google Scholar 

  14. Resende, G., Melo, P., Reis, J.C.S., Vasconcelos, M., Almeida, J.M., Benevenuto, F.: Analyzing textual (mis)information shared in WhatsApp groups. In: Proceedings of the 10th Conference on Web Science (WebSci19), pp. 225–234 (2019)

    Google Scholar 

  15. Resende, G., Melo, P., Sousa, H., Messias, J., Vasconcelos, M., Almeida, J., Benevenuto, F.: (Mis)Information dissemination in WhatsApp: gathering, analyzing and countermeasures. In: The World Wide Web Conference, pp. 818–828 (2019)

    Google Scholar 

  16. Ribeiro, F.N., Saha, K., Babaei, M., Henrique, L., Messias, J., Benevenuto, F., Goga, O., Gummadi, K.P., Redmiles, E.M.: On microtargeting socially divisive ads: a case study of Russia-linked ad campaigns on Facebook. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 140–149 (2019)

    Google Scholar 

  17. Rushkoff, D., Pescovitz, D., Dunagan, J.: The biology of disinformation: memes, media viruses, and cultural inoculation (2018)

    Google Scholar 

  18. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998)

    Article  Google Scholar 

  19. Zannettou, S., Caulfield, T., De Cristofaro, E., Sirivianos, M., Stringhini, G., Blackburn, J.: Disinformation warfare: understanding state-sponsored trolls on Twitter and their influence on the web. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 218–226 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philipe de Freitas Melo .

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

de Freitas Melo, P., Vieira, C.C., Garimella, K., de Melo, P.O.S.V., Benevenuto, F. (2020). Can WhatsApp Counter Misinformation by Limiting Message Forwarding?. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_31

Download citation

Publish with us

Policies and ethics