Open AccessDigital Lighthouse Platform: Understanding the Misinformation Phenomenon on WhatsApp
Author(s) -
José Maria da Silva Monteiro Filho,
Ivandro Claudino de Sá,
Lucas Cabral Carneiro da Cunha,
Helena Martins do Rego Barreto,
Pedro Jorge Chaves Mourão
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd_estendido.2021.18178
Subject(s) - misinformation , internet privacy , social media , phenomenon , context (archaeology) , computer science , data science , computer security , world wide web , geography , physics , archaeology , quantum mechanics
In the past few years, the large-scale dissemination of misinformation through social media has become a critical issue, harming the trustworthiness of legit information, social stability, democracy and public health. In many developing countries such as Brazil, India, and Mexico, one of the primary sources of misinformation is the messaging application WhatsApp. In February 2020, the Panorama Mobile Time/Opinion Box survey on mobile messaging in Brazil revealed that WhatsApp was installed on 99% of Brazilian smartphones. Among users of the application, 98% said they access it every day or almost every day. In this context, WhatsApp provides an important feature: the public groups. Many of these groups have been used to spread misinformation, especially as part of articulated political or ideological campaigns. Despite this scenario, due to WhatsApp's private messaging nature, few methods were explicitly developed to investigate the misinformation phenomenon on this platform. This tutorial provides an overview of recent developments in monitoring misinformation spreading, automatic misinformation detection, and identifying misinformation spreaders. In addition, we provide an overview of the leading open problems associated with the misinformation phenomenon and briefly examine some of the existing solutions. We hope that our tutorial can help researchers better understand Brazil's misinformation propagation and use data science methods to face this critical phenomenon.