One of the defining characteristics of metropolitan areas in Brazil is the geographic segregation of different socio-economic groups, in particular, the prevalence of favelas and slums, which could be compared to the ghettos for the poor. Almost 55 million Brazilians live below the poverty line of R$406/£60 household income per person per month, of which 11.4 million live in Favelas, and an additional 101.8 thousand are homeless (IBGE, 2019). These areas are characterised by higher levels of crime, limited access to public services and underdeveloped infrastructure. These factors, along with the extremely high density of population, pose higher risks in times of crisis, such as the COVID-19 pandemic.

Brazil is among the countries most severely affected by the pandemic, with the second-highest number of cases in the world (514,992 cases and 29,341 deaths registered on 1st June 2020). Most of the deaths occur in big cities, e.g. 17% of total deaths are in Rio de Janeiro and 26% are in Sao Paulo. In the state of Goiás, 46% of deaths are registered in its capital, Goiânia. The official numbers are most likely underreported due to a lack of testing and an ambiguous definition of the causes of deaths from “Severe Acute Respiratory Syndrome”, which are not included in the official count. Deprived areas and peripheries in large cities are disproportionally hit by the effects of the pandemic as there are fewer efforts to increase awareness of the pandemic risks compared with higher-income districts, as well as limited access to specialized care in hospitals. As a result, the average death rate in poor areas is significantly higher: 20% in the poorest communities vs. 5-8% in wealthier neighbourhoods.

Understanding the cultural landscape of diverse disadvantaged socio-economic areas is essential to shape effective communication strategies that support local pandemic responses, including culture-specific grassroots initiatives. For instance, when the government failed to take action and impose a lockdown, criminal gangs in favelas imposed strict curfews to slow coronavirus spread (Briso & Phillips 2020), while a support group in Rio de Janeiro started a newspaper to help develop good preventive habits based on their own experience. However, the growing influence of social media (Nemer 2016) has brought with it the proliferation of fake news (de Morais et al. 2019).

Examples of fake news include the rumours that the vaccine under development is designed to kill older people, or the perception that it might be a ‘socialist’ vaccine that might ‘infect’ people with ‘wrong’ ideas. Thus, our research is timely and meaningful as it will contribute to existing local (state) efforts to mitigate the impact of COVID-19. By exploring participants’ perceptions of valuable information in naturalistic settings and through persuasive design, we have been able to contribute novel ways of exploring and furthering communication among vulnerable groups.

In addition, our research constitutes an original and novel contribution to methodology at various levels:

1. We combine ethnographic approaches in observations and interviews with participatory action-research),
2. we study three under-researched settings (favelas, cooperatives and shelters),
3. we involve participants from three different vulnerable groups (favela dwellers, garbage pickers and homeless) and
4. we implement novel procedures to develop critical reading at various literacy levels through scaffolding, and educational games).

Uncovering how participants perceive information and news, how and why they assign trust to a source, exploring their beliefs about and behaviours towards others area and the interconnections with their decision-making power is more pressing than ever today. Given the life threat of this current pandemic, being misled by fake information/news can be the difference between life and death.

References

de Morais, J. I., Abonizio, H. Q., Tavares, G. M., da Fonseca, A. A., & Barbon Jr, S. (2019). Deciding among Fake, Satirical, Objective and Legitimate news: A multi-label classification system. In Proceedings of the XV Brazilian Symposium on Information Systems, 1-8.