A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp

• URL: http://arxiv.org/abs/2109.10462v1
• Date: Wed, 22 Sep 2021 00:00:02 GMT
• Title: A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp
• Authors: Gabriel Peres Nobre, Carlos H. G. Ferreira and Jussara M. Almeida
• Abstract summary: We present a hierarchical network-oriented characterization of the users engaged in misinformation spread on WhatsApp. Our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation on the platform.
• Score: 0.9774299772405469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation on the platform.

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