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Can WhatsApp Counter Misinformation by Limiting Message Forwarding?

  • Philipe de Freitas MeloEmail author
  • Carolina Coimbra Vieira
  • Kiran Garimella
  • Pedro O. S. Vaz de Melo
  • Fabrí­cio Benevenuto
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

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.

Keywords

WhatsApp Misinformation Fake news Virality Epidemiological model Complex network 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Philipe de Freitas Melo
    • 1
    Email author
  • Carolina Coimbra Vieira
    • 1
  • Kiran Garimella
    • 2
  • Pedro O. S. Vaz de Melo
    • 1
  • Fabrí­cio Benevenuto
    • 1
  1. 1.Departamento de Ciência da Computação - UFMGBelo HorizonteBrazil
  2. 2.Institute for Data, Society and Systems - MITCambridgeUSA

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