Da solidariedade à “casa da Mãe Joana” : análise do discurso digital sobre os refugiados no Facebook - Hora, Lidia Gurgel Neves (2025)
This study employed a qualitative-quantitative method to analyze discourses concerning refugees on Facebook during 2020 and 2021, the initial phase of the COVID-19 pandemic. By integrating Data Science — specifically, the Perspectivist Method of Social Network Analysis — and Digital Discourse Analysis, the research examined 38,410 posts and 2.8 million interactions. The connections between words and actors were graphically represented as discursive perspectives. From the word-based perspectives, key themes emerged, including solidarity with refugees, the Brazilian government’s Welcoming Operation, and vulnerabilities associated with gender, race, and poverty. The actor-based perspective revealed collaborations between institutions and influencers that amplified the visibility of refugee mobilization and social initiatives. Notably, Brazilian far-right military and political figures appeared unified in their support for refugee reception — a stance contrasting with European xenophobia. This divergence may be attributed to the strategic alignment of such actions with anti-communist ideologies concerning Venezuela and the persecution of Christians in Afghanistan. An analysis of commercial media perspectives presented limited coverage of refugee issues, whereas alternative media outlets actively denounced human rights abuses and potentially hazardous political alliances. In both word and actor-based perspectives, the prominence of international organizations was evident. Within user comments, despite prevailing sentiments of solidarity, instances of hate speech and xenophobia were identified, often framed around concerns regarding refugee numbers, public safety, and health risks. These discourses frequently aligned with the global far-right. The study underscores the necessity for enhanced social media regulation, including stricter comment moderation and strategies to counter hate speech in digital interactions. It also highlights the importance of discourse analysis in capturing Brazil’s sociopolitical specificities, and advocates for the systematic archiving and processing of the country’s linguistic data. Furthermore, the findings suggest that discourse analysis could play a pivotal role in mitigating biases within AI-driven language models.