From Algorithmic Filter Bubbles to Socially Mediated Echo Chambers: Explaining the Spread of Pro-Bolsonaro Disinformation on WhatsApp during Brazil’s 2018 Presidential Election

Abstract

The spread of false information and political polarization on social media is often explained through the theories of “filter bubbles” and “echo chambers”. These models are applicable to information channels that rely on algorithmic recommendations, but their applicability is limited for private, encrypted, and user-driven platforms such as WhatsApp.

To examine how information spreads through private groups, social trust, and forwarding networks on digital platforms, this paper adopts a theory-guided case study approach, based on the spread of pro-Bolsonaro disinformation during Brazil’s 2018 presidential election. The findings show the extent to which theories such as information disorder, echo chambers, filter bubbles, and populist communication can explain information dissemination on a private, networked interpersonal communication platform.

This paper proposes the concept of socially mediated echo chambers to extend explanations of information dissemination from the traditional emphasis on algorithmic filtering to social trust, private communication structures, identity, and limited correction mechanisms, which may also help explain information dissemination and polarization on closed or semi-closed digital platforms such as Telegram, WeChat, and Discord.