## 6 Conclusions  
This paper introduced the _EsCorpiusBias_ corpus, a contextually grounded and rigorously annotated resource specifically designed for detecting and mitigating online toxicity and bias in Spanish-language forums. Our methodological approach, focusing on multi-turn dialogue interactions sourced from the Mediavida forum, allowed us to capture nuanced manifestations of sexism and racism that emerge distinctly within conversational contexts. Through extensive annotation, we demonstrated moderate to substantial inter-annotator agreement (Cohen's Kappa of 0.55 for sexism and 0.79 for racism), underscoring both the effectiveness of our detailed guidelines and the complexity inherent in identifying subtler forms of toxicity. The final dataset, comprising approximately 1000 dialogues each for sexism and racism, reflects realistic class distributions and provides a robust basis for training sophisticated NLP models. Experimental results highlighted the critical role of domain-specific fine-tuning and contextual embedding strategies. Our transformer-based models, notably the BETO architecture fine-tuned within the SpaCy pipeline, consistently outperformed simpler logistic regression and TextCatBOW baselines across metrics (precision, recall, F1, and ROC-AUC). The findings further emphasized the inadequacy of externally trained general-purpose models in accurately identifying nuanced toxicity, reinforcing the importance of targeted training datasets and methods. Keyword overlap analysis confirmed models' heavy reliance on explicit lexical cues, suggesting ongoing challenges in accurately capturing implicit and context-dependent toxicity. This points toward critical avenues for future research, including the development of advanced pragmatic modeling and more effective handling of implicit bias. However, our approach is not without limitations. One notable challenge is the inherent subjectivity in contextual labeling, particularly in distinguishing subtle or implicit forms of bias, such as sarcasm, irony, and microaggressions. Annotators, despite rigorous training, might still be influenced by individual biases, cultural perceptions, and subjective interpretations, potentially affecting label consistency. Additionally, the three-turn context window, while effective for capturing immediate conversational dynamics, might occasionally fail to encompass sufficient contextual depth necessary for interpreting complex or evolving interactions. Furthermore, our dataset reflects a specific online community (Mediavida forum), potentially limiting the generalizability of our findings to other contexts, platforms, or demographic groups. The scarcity of annotated examples for homophobia and aporophobia also highlights challenges related to the availability and representation of certain bias categories within our corpus. Overall, our contributions provide significant methodological and resource advancements for socially responsible NLP, facilitating improved automated moderation systems tailored explicitly to Spanish-language online interactions. Future work should continue to refine contextual modeling capabilities, integrate richer pragmatic reasoning frameworks, and explore multilingual and multimodal approaches to enhance the adaptability and effectiveness of hate speech detection systems. Conceptualization: Z.C., D.G. and D.P.-F.; methodology: Z.C., D.G., A.G.-F., K.K. and D.P.-F.; software: K.K. and D.P.-F.; validation: Z.C., D.G. and J.G.-H.; formal analysis: Z.C., D.G., A.G.-F., J.G.-H., K.K. and D.P.-F.; investigation: Z.C., D.G., A.G.-F., J.G.-H., K.K. and D.P.-F.; resources, Z.C. and D.G. ; data curation, K.K. and J.G.-H.; writing--original draft preparation, K.K. and D.G. ; writing--review and editing, Z.C., D.G. and D.P.-F.; visualization, K.K. ; supervision, Z.C. and D.G. ; project administration, Z.C. and D.G. ; funding acquisition: Z.C. and D.G. All authors have read and agreed to the published version of the manuscript.