Artificial Intelligence in Brazilian News: A Mixed-Methods Analysis

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Authors: Raphael Hernandes, Giulio Corsi

Published: 2024-10-22 20:52:51+00:00

AI Summary

This study analyzes 3,560 Brazilian news articles (July 1, 2023 - February 29, 2024) to understand AI representation in non-anglophone media. Using computational grounded theory, it reveals that Brazilian AI coverage prioritizes workplace applications and product launches, with limited attention to societal concerns like deepfakes, despite their relevance to Brazilian democracy.

Abstract

The current surge in Artificial Intelligence (AI) interest, reflected in heightened media coverage since 2009, has sparked significant debate on AI's implications for privacy, social justice, workers' rights, and democracy. The media plays a crucial role in shaping public perception and acceptance of AI technologies. However, research into how AI appears in media has primarily focused on anglophone contexts, leaving a gap in understanding how AI is represented globally. This study addresses this gap by analyzing 3,560 news articles from Brazilian media published between July 1, 2023, and February 29, 2024, from 13 popular online news outlets. Using Computational Grounded Theory (CGT), the study applies Latent Dirichlet Allocation (LDA), BERTopic, and Named-Entity Recognition to investigate the main topics in AI coverage and the entities represented. The findings reveal that Brazilian news coverage of AI is dominated by topics related to applications in the workplace and product launches, with limited space for societal concerns, which mostly focus on deepfakes and electoral integrity. The analysis also highlights a significant presence of industry-related entities, indicating a strong influence of corporate agendas in the country's news. This study underscores the need for a more critical and nuanced discussion of AI's societal impacts in Brazilian media.

Key findings

Brazilian AI news coverage focuses heavily on product launches and workplace applications, neglecting broader societal concerns. Industry-related entities dominate the narrative, suggesting corporate influence. The episodic nature of coverage, driven by specific events, hinders sustained discussion of crucial AI issues.

Approach

The research employs Computational Grounded Theory (CGT), combining Latent Dirichlet Allocation (LDA), BERTopic, and Named-Entity Recognition (NER) to analyze news articles. TF-IDF was used to filter articles where AI was a central topic. Results were validated through qualitative analysis of top articles for each identified topic.

Datasets

3,560 news articles from 13 popular Brazilian online news outlets published between July 1, 2023, and February 29, 2024.

Model(s)

Latent Dirichlet Allocation (LDA), BERTopic, Named-Entity Recognition (NER), TF-IDF

Author countries

United Kingdom

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