## 3 Materials and Methods
### Annotation Procedure and Contextual Grounding in Dialogue  
To construct a high-quality corpus for toxic language detection in Spanish-language online discourse, we implemented a systematic and context-aware annotation protocol. The dataset comprises approximately 1000 three-turn dialogue samples for each of two primary bias categories: sexism and racism. These samples were drawn from multi-user conversational threads on the Mediavida forum. Each unit consisted of the target comment and its two preceding turns, preserving the flow of interaction and enabling nuanced interpretation of pragmatics, tone, and intention. Additionally, we initially launched annotation efforts for two further bias dimensions, homophobia and aporophobia, using the same three-turn contextual structure. However, as detailed in Section 4.1, the frequency of positively labeled examples in these categories proved extremely low, which ultimately led us to exclude them from model training and evaluation due to insufficient representation. To construct the dataset, we initially sampled dialogues at random; however, this approach yielded very few positive instances of bias. To address this, we developed targeted lists of slurs and keywords for racism, sexism, homophobia, and aporophobia, and used these terms to filter and pre-select dialogues more likely to contain the phenomena of interest. This strategy substantially increased the number of positive examples for racism and sexism, while instances of homophobia and aporophobia remained extremely rare despite targeted filtering. Ultimately, the final datasets for sexism and racism comprised a mix of naturally occurring, randomly sampled dialogues and examples identified through lexical filtering, ensuring both diversity and adequate representation of toxic content in these two categories. Unlike traditional hate speech datasets that present isolated comments, our approach centers on contextual grounding. This methodology acknowledges that toxicity often emerges from discursive interplay: a phrase may appear innocuous when decontextualized but reveal discriminatory implications when situated within a broader interaction. For example, humor, irony, or quoted speech can mask latent sexism or racism, which are patterns only reliably detected by examining the turn-taking structure and preceding provocations or justifications. We selected a three-turn context window (the annotated comment plus two preceding turns) after a preliminary analysis of Mediavida dialogues. Shorter windows often lacked sufficient discourse information for interpreting nuanced or implicit bias, while longer contexts increased annotator load and topical drift. Thus, three turns offered an optimal balance, capturing key interactional context without sacrificing annotation quality. Tables 4 (racism) and 5 (sexism) provide representative examples demonstrating the necessity and sufficiency of this window. To ensure consistency and analytical rigor, annotation was conducted using Prodigy, a modern annotation tool built on the spaCy NLP library. Prodigy was configured in manual text classification mode (textcat.manual) and customized to present full dialogue units, allowing annotators to assess semantic and pragmatic dependencies between turns. Its support for custom JSONL schemas enabled us to format and render three-turn dialogues clearly and coherently, which was essential for preserving interactional structure during the annotation task. Two human annotators with prior experience in bias detection and online moderation, completed the labeling process following a detailed annotation guide grounded in contemporary definitions of the biases considered. Each comment was assigned a binary label \"toxic\" or \"non-toxic\" for the relevant phenomenon. Prodigy's lightweight interface facilitated focused, uninterrupted work while simultaneously tracking metadata and versioning for reproducibility. To clarify our quality assurance and annotation methodology, Figure 1 summarizes the complete annotation workflow used in this study.