## 1 Introduction
In this line, this paper presents the _EsCorpiusBias_ corpus, a novel dataset of annotated multi-turn, multi-user dialogues specifically sourced from discussions on the Mediavida forum [[https://www.mediavida.com/foro/](https://www.mediavida.com/foro/)]([https://www.mediavida.com/foro/](https://www.mediavida.com/foro/)), (accessed on 16 June 2025). We originally focused on sexism, racism, homophobia, and aporophobia, the last of which relies on a split between \"deserved\" and \"undeserved\" poverty to flag cues such as \"the poor are responsible for their poverty\" or \"social aid only encourages laziness\". Pilot annotation on Mediavida data, however, revealed that clear cases of aporophobia and homophobia were scarce (less than two dozen positives in several thousand dialogues), so we reduced the final release to the two well-represented biases: sexism and racism, with approximately 1000 dialogue units each. Each dialogue unit consisted of three turns in order to preserve humor, irony, and quoted speech, signals that are often lost in single-comment corpora. To concretely illustrate the challenges and nuances of online racism and sexism in Spanish, Section 3.2 presents full examples of annotated multi-turn Mediavida forum dialogues in Spanish, alongside their English translations. These examples highlight how context can reveal both explicit and subtle expressions of prejudice that may be missed in isolated comments. The main contributions of our paper are as follows:  
* A novel context-aware corpus: We release _EsCorpusBias_, the first large Spanish dataset of annotated multi-turn, multi-user forum dialogues for sexism and racism detection. Unlike many previous datasets in Spanish that focus on isolated Twitter comments or decontextualized comment fragments, our approach focuses on contextual grounding, recognizing that toxicity often arises from discursive interaction. * Rich contextual annotation protocol: Three-turn dialogues were annotated following meticulously developed guidelines that covered both explicit and implicit manifestations of sexism and racism. * Reliable annotation quality: Annotations were conducted following a well-defined protocol using the Prodigy tool, resulting in moderate to substantial inter-annotator agreement (Cohen's Kappa: 0.55 for sexism and 0.79 for racism). Discrepancies were resolved using a manual adjudication protocol. * Comprehensive model evaluation: We trained and evaluated models such as logistic regression, SpaCy's n-gram bag-of-words model (TextCatBOW) and the BETO transformer-based model. Our experiments show that contextualized transformer-based approaches (BETO) significantly outperform baseline models (such as logistic regression and TextCatBOW). Better recall and F1 score performance were observed for the contextualized variants of our models, underscoring the critical role of the preceding dialog context. Comparison with external models (such as piuba-bigdata/beto-contextualized-hate-speech and unitary/multilingual-toxic-xlm-roberta from Hugging Face) without domain-specific fine-tuning confirmed their shortcomings, especially in recall, highlighting the need to tune the models specifically for the linguistic characteristics and nuances of forum dialogues. * Error and lexical bias analysis: We provide confusion matrices, error examples, and lexical overlap analysis, revealing current model strengths and limitations in detecting implicit bias. The remainder of the paper is structured as follows. Section 2 reviews the main datasets and models developed to date for toxicity and hate speech detection, with a focus on Spanish-language resources. Section 3 details the data sources and collection procedures, specifying the focus on Mediavida forum discussions due to ethical compliance. This section also thoroughly describes the annotation procedure, including the development of guidelines for four distinct types of bias: sexism, racism, homophobia, and aporophobia, and emphasizing the contextual grounding in multiturn dialogues using tools like Prodigy.