The proliferation of online hate speech requires a rigorous examination of the datasets used to train detection models. In this work, we analyze six Brazilian Portuguese datasets annotated for hate speech or toxicity to investigate how their lexical "anatomy" and domain characteristics affect cross-domain generalization. We combine HurtLex-based lexical profiling with cross-dataset evaluation in a feature-based transfer-learning setup, using BERTimbau embeddings and an XGBoost classifier. Our analysis shows that, although the datasets share a broadly similar macro-level focus, they diverge substantially in how specific terms are used and labeled across platforms and topics. Results indicate that lexical breadth and annotation practices strongly predict transferability: datasets with broader and more heterogeneous toxic vocabulary yield better cross-domain performance, whereas resources with narrow, profanity-centered labeling lead to severe generalization gaps, even when lexical overlap is high. These findings underscore the impact of collection and labeling strategies on the curation and evaluation of Portuguese hate speech datasets. Warning! This work and the referenced datasets contain examples of offensive and hateful language.

Linking citizen complaints to the public services they concern remains a major challenge in the Brazilian federal administration. In 2025, over 1.2 million manifestations were submitted across 328 agencies, yet only about 1.8% are currently associated with a specific service, limiting large-scale monitoring and evidence-based management. We cast this task as an extreme multi-class text classification problem marked by severe class imbalance and strong lexical–semantic gaps between citizen language and official service descriptions. Building on recent work that reframes the task as information retrieval, we combine sparse retrieval with BM25 over representative complaint corpora and dense retrieval enriched with RAG-labels: semantically expanded label descriptions generated via Retrieval-Augmented Generation and Small Language Models. This approach markedly reduces vocabulary mismatch and semantic ambiguity, yielding substantial gains over direct text or embedding matching. To our knowledge, this is the first Portuguese-language application of RAG-labels for service–complaint association. In real operational data from the Federal Ombudsman Office, our method can automatically assign plausible services to roughly 73% of previously unlabeled cases, improving coverage and supporting more effective public service evaluation.

We investigate two essential challenges in the context of hierarchical topic modeling (HTM)—(i) the impact of data representation and (ii) topic evaluation. The data representation directly influences the performance of the topic generation, and the impact of new representations such as contextual embeddings in this task has been under-investigated. Topic evaluation, responsible for driving the advances in the field, assesses the overall quality of the topic generation process. HTM studies exploit the exact topic modeling (TM) evaluation metrics as traditional TM to measure the quality of topics. One significant result of our work is demonstrating that the HTM’s hierarchical nature demands novel ways of evaluating the quality of topics. As our main contribution, we propose two new topic quality metrics to assess the topical quality of the hierarchical structures. Uniqueness considers topic topological consistency, while the Semantic Hierarchical Structure (SHS) captures the semantic relatedness of the hierarchies. We also present an additional advance to the state-of-the-art by proposing the c-CluHTM. To the best of our knowledge, c-CluHTM is the first method that exploits contextual embeddings into NMF in HTM tasks. c-CluHTM enhances the topics’ semantics while preserving the hierarchical structure. We perform an experimental evaluation, and our results demonstrate the superiority of our proposal with gains between 12% and 21%, regarding NPMI and Coherence over the best baselines. Regarding the newly proposed metrics, our results reveal that Uniqueness and SHS can capture relevant information about the structure of the hierarchical topics that traditional metrics cannot.

Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. Despite advantages over traditional topic modeling, HTM poses its own challenges, such as (1) topic incoherence, (2) unreasonable (hierarchical) structure, and (3) issues related to the definition of the “ideal” number of topics and depth of the hierarchy. In this paper, we advance the state-of-the-art on HTM by means of the design and evaluation of CluHTM, a novel non-probabilistic hierarchical matrix factorization aimed at solving the specific issues of HTM. CluHTM’s novel contributions include: (i) the exploration of richer text representation that encapsulates both, global (dataset level) and local semantic information – when combined, these pieces of information help to solve the topic incoherence problem as well as issues related to the unreasonable structure; (ii) the exploitation of a stability analysis metric for defining the number of topics and the “shape” the hierarchical structure. In our evaluation, considering twelve datasets and seven state-of-the-art baselines, CluHTM outperformed the baselines in the vast majority of the cases, with gains of around 500% over the strongest state-of-the-art baselines. We also provide qualitative and quantitative statistical analyses of why our solution works so well.