The spread of online misinformation has made fake news detection an essential tool for mitigating its negative impact, but many studies often disregard the temporal information, and existing datasets become outdated as news evolve. Some modern solutions using Retrieval-Augmented Generation (RAG) can solve the problem of unseen news events by providing context to the models. However, there are no studies evaluating the feasibility of web searches to attain context to decide whether a news article is true or not. This work aims to address this gap by conducting a comparative study between RAG-based solutions, traditional fake news classification methods, and deep learning-based methods. The results show that although RAG is a modern and promising technique, it cannot outperform techniques already adopted in the literature.

Robust text-to-speech (TTS) systems must be trained on speech that mirrors the variability and imperfections of spontaneous dialogues. However, TTS systems trained on existing Brazilian Portuguese datasets are typically limited to clean, scripted, or studio-recorded speech. Certas Palavras (CP) bridges this gap with 70 hours of spontaneous, multi-speaker dialogues from a Brazilian radio program broadcast in the 1980s–1990s. The extensive manual annotation process captures conversational dynamics, including orality markers, filled pauses, and hesitations. For the analog medium, we annotated non-verbal phenomena as musical interference, noise and segmental corrections, describing a challenging acoustic environment for synthesis. Baseline YourTTS and F5-TTS models were trained in a 9-hour subset featuring one of the two main hosts of Certas Palavras. Baseline YourTTS and F5-TTS models were trained on a 9-hour single-speaker subset corresponding to one of the main program hosts. Objective evaluation shows that the synthesized speech remains intelligible, with moderate WER and CER. In contrast, subjective evaluation reveals a clear gap in perceived naturalness, with lower MOS scores and higher inter-rater variability compared to ground-truth audio. Together, these properties make the dataset a strong benchmark for TTS robustness.

Notícias falsas são um grande problema para a sociedade. Com a Inteligência Artificial generativa, notícias falsas produzidas pela máquina têm se proliferado, tornando o cenário mais desafiador. Apesar da relevância desse problema, em línguas sub-representadas como o Português, as pesquisas que buscam diferenciar notícias falsas de humanos e de máquinas são incipientes. Buscando preencher essa lacuna, este artigo explora os corpora Fake.br e FakeTrueBR expandidos com notícias falsas geradas automaticamente, caracterizando lexical e sintaticamente as notícias falsas produzidas por humanos e por máquina. Os resultados mostram que textos gerados por máquina apresentam palavras significativamente mais longas, maior uso de modificadores adjetivais e menor diversidade sintática, apesar de utilizarem mais regras sintáticas por sentença. Em contrapartida, textos humanos exibem maior variabilidade estilística em todas as dimensões analisadas.