Julio C. S. Reis

Jônatas H. dos Santos, Julio C. S. Reis, Philipe Melo et al.

We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years. The dataset was constructed through a rigorous, carefully designed pipeline that integrates keyword-based data collection, semantic deduplication to remove near-duplicate content, and a multi-stage annotation protocol conducted by medical specialists. This process produced a high-quality gold-standard corpus, characterized by substantial inter-annotator agreement and strong reliability for downstream analysis. Additionally, we provide a detailed characterization of WhatsApp misinformation, revealing distinctive linguistic, structural, lexical, temporal, and group-level patterns, as well as a meaningful layer of ambiguous cases that reflect the complexity of health discourse in private messaging. We also benchmark classical models, fine-tuned Small Language Models, and zero- or few-shot Large Language Models under realistic data-scarcity constraints, demonstrating that strong embeddings and LLM approaches perform competitively, while domain alignment and data availability remain critical factors. This study provides a rare, high-quality resource to support misinformation research and computational modeling in encrypted communication environments.

Carlos Eduardo P. Silva, João Pedro M. Sena, Julio C. S. Reis et al.

In this paper, we propose a context-aware recommender system that models students' programming skills using embeddings of the source code they submit throughout a course. These embeddings predict students' skills across multiple programming topics, producing profiles that are matched to the skills required by unseen homework problems. To generate recommendations, we compute the cosine similarity between student profiles and problem skill vectors, ranking exercises according to their alignment with each student's current abilities. We evaluated our approach using real data from students and exercises in an introductory programming course at our university. First, we assessed the effectiveness of our source code embeddings for predicting skills, comparing them with token-based and graph-based alternatives. Results showed that Jina embeddings outperformed TF-IDF, CodeBERT-cpp, and GraphCodeBERT across most skills. Additionally, we evaluated the system's ability to recommend exercises aligned with weekly course content by analyzing student submissions collected over seven course offerings. Our approach consistently produced more suitable recommendations than baselines based on correctness or solution time, indicating that predicted programming skills provide a stronger signal for problem recommendation.

Julio C. S. Reis, Philipe de Freitas Melo, Kiran Garimella et al.

WhatsApp was alleged to be widely used to spread misinformation and propaganda during elections in Brazil and India. Due to the private encrypted nature of the messages on WhatsApp, it is hard to track the dissemination of misinformation at scale. In this work, using public WhatsApp data, we observe that misinformation has been largely shared on WhatsApp public groups even after they were already fact-checked by popular fact-checking agencies. This represents a significant portion of misinformation spread in both Brazil and India in the groups analyzed. We posit that such misinformation content could be prevented if WhatsApp had a means to flag already fact-checked content. To this end, we propose an architecture that could be implemented by WhatsApp to counter such misinformation. Our proposal respects the current end-to-end encryption architecture on WhatsApp, thus protecting users' privacy while providing an approach to detect the misinformation that benefits from fact-checking efforts.