## 2 Related Work
Third, multilingual models, while promising, tend to underperform in domain-specific tasks compared to monolingual alternatives like BETO, due to the reduced linguistic and cultural alignment. Finally, implicit forms of toxicity, such as coded language, sarcasm, and microaggressions, remain difficult to detect, and few corpora provide reliable annotation of such subtleties. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Base Transformer** & **Target Phenomena (Labels)** & **Additional Features** \\ \hline BETO & \multirow{2}{*}{BETO (Spanish BERT)} & Offensiveness (5 classes: & Evaluated on multiple platforms: \\  & & Non-offensive, Offensive, etc.) & Twitter, YouTube, Instagram \\ \hline Contextualized Hate & \multirow{2}{*}{BETO (Spanish BERT)} & Hate speech (multilass: & Context-aware embeddings; \\ Speech [12] & & sexism, racism, LGBT+ hate, & trained on news-site comments \\  & & disability hate, etc.) & (multi-turn context) \\ \hline \multirow{3}{*}{HateNet [52]} & CNN + linguistic & Hate speech (binary: hate, & Uses user-level metadata and \\  & features & non-hate) & linguistic features; specifically \\ \hline Multilingual & mBERT, & Hate speech, aggressiveness, & \\ Transformers [58] & XLM-RoBERTa & offensive language (binary & \\  & & and multiclass) & \\ \hline Multilingual Toxic- & \multirow{2}{*}{XLM-RoBERTa} & General toxicity (multiple: & \\  & XLM-RoBERTa & toxicity, severe\_toxicity, & \\  & & obscene, threat, etc.) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep learning models fine-tuned for toxicity and hate speech detection in Spanish. To proactively address these limitations and significantly advance the automated detection of toxicity and bias in Spanish online dialogues, this paper presents the EsCorpiusBias corpus. This innovative dataset stands out by meticulously annotating multi-turn, multi-user dialogues specifically extracted from Mediavida forum discussions. This approach allows for more nuanced and interactional analyses of toxicity, sexism, and racism, capturing the dynamic development of prejudice in conversation. Overall, the continuous refinement of annotated resources such as _EsCorpiusBias_, combined with advanced transformer-based methodologies, provides an essential foundation for developing robust, contextually sensitive, and socially responsible NLP systems to detect and mitigate toxic discourse in Spanish online communities. In addition, to improve the reliability and cultural inclusivity of bias detection in LLMs, especially for under-represented languages and non-Western communities, future research should prioritize the following: engaging local experts and communities to capture culturally relevant language and bias; tailoring annotation guidelines to local hierarchies, discourse, and intersectional identities beyond binary taxonomies; adopting interdisciplinary methods to account for nuanced phenomena like code-switching, humor, and microaggressions; leveraging cross-lingual and active learning to support low-resource settings; ensuring transparency through open data, code, and ongoing community involvement. These steps are vital to addressing global diversity in social bias and building equitable LLM-based systems.