Don’t talk to strangers? The role of network composition, WhatsApp groups, and partisanship in explaining beliefs in misinformation about COVID-19 in Brazil

Rossini, P. and Kalogeropoulos, A. (2023) Don’t talk to strangers? The role of network composition, WhatsApp groups, and partisanship in explaining beliefs in misinformation about COVID-19 in Brazil. Journal of Information Technology and Politics, (doi: 10.1080/19331681.2023.2234902) (Early Online Publication)

Abstract

The spread of disinformation has been a topic of heightened concern, particularly during the COVID-19 pandemic, as the response to a public health crisis relies on the ability for public officials to inform citizens. Using a representative two-wave panel of internet users in Brazil, we examine the relationship between pathways to information, WhatsApp use, and the persistence of misinformed beliefs about the pandemic. We find a strong relationship between presidential support, right-wing news sources, and participating in WhatsApp groups with strangers, and becoming more misinformed over time. Conversely, most media diets (traditional news media, social media and WhatsApp for news) had no effect. However, Bolsonaro supporters, using WhatsApp and Facebook for news was strongly associated with increasing and persistent misinformation. Our findings provide further evidence that political leaders undermine a country’s ability to respond to a pandemic insofar as they breed mistrust in other institutions by instrumentalizing public health measures to win political fights.

• Item Type: Articles
• Additional Information: Fieldwork for this project was supported by the University of Liverpool’s ODA Rapid Response Fund.
• Keywords: Misinformation, disinformation, news use, WhatsApp, social media, COVID-19.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rossini, Dr Patricia
• Authors: Rossini, P., and Kalogeropoulos, A.
• College/School: College of Social Sciences > School of Social and Political Sciences > Politics
• Journal Name: Journal of Information Technology and Politics
• Publisher: Taylor & Francis
• ISSN: 1933-1681
• ISSN (Online): 1933-169X
• Published Online: 11 July 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Journal of Information Technology and Politics 2023
• Publisher Policy: Reproduced under a Creative Commons License