## 3 Materials and Methods
### Annotation Framework and Theoretical Foundations  
Toxicity in online communication refers broadly to language, behavior, or attitudes expressed through digital interactions that cause harm, reinforce negative stereotypes, or perpetuate discrimination against specific groups or individuals. According to recent studies, toxicity encompasses diverse phenomena including hate speech, offensive language, aggressiveness, and implicit forms of prejudice such as stereotypes and microaggressions [11; 12]. Given the complexity and context-dependence of toxic behavior, precise and operational definitions are crucial for accurate annotation and effective automated detection. For the purpose of our dataset annotation, we specifically focused on four main categories of toxicity: sexism, racism/xenophobia, homophobia, and aporophobia. Each category was meticulously defined based on prior literature, ensuring clarity and consistency across annotations. To ensure both transparency and replicability of our annotation process, Table 3 provides a structured overview of all primary annotation labels, their operational definitions, concise annotation guidelines, and illustrative examples drawn directly from our corpus. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Definition** & **Annotation Guidelines** & **Sample Dialogue (with Translation)** \\ \hline \multirow{4}{*}{Sexism} & Discrimination or & Annotators evaluate dialogues for & & \\  & prejudiced & manifestations such as hostile, & & Sexist Example: \\  & statements based on & benevolent, objectiving, & \"Es que es obvio, los videojuegos de \\  & gender, reinforcing & ideological, or stereotypical sexism. & siempre han sido cosa de hompres...” \\  & stereotypes & Annotation is context-dependent & (”It’s obvious, videogames have always \\  & or inequalities. & and requires assessing subtle cues & been a guy thing...”) \\  & & within the conversation. & & Non-Sexist Example: \\  & Absence of & Annotators confirm no sexist & \"Que yo sepa la mayoria de \\  & gender-based & elements exist within dialogue & & \\  & discriminatory or & context, ensuring neutral or & & \\  & prejudiced statements. & inclusive expressions. & (”As far as I know, most competitions \\  & & & & allow both sexes...”) \\ \hline \multirow{4}{*}{Racism} & Expressions & Annotators identify dialogues & & Racist Example: \\  & involving prejudice & containing affective, evaluative, & \"El camarero no puso mesa de \\  & or discrimination & judgmental, overt or covert racism, & infrasresres...ha puseto mesa acorde a lo \\  & based on race, & and stereotyping. Contextual & que son, gitanos...” \\  & ethnicity, or national & understanding is crucial to detect & (”The waiter didn’t write ‘table of \\  & origin, whether overt & subtle or & subhumans’...he wrote a table \\  & or implicit. & ambiguous manifestations. & according to what they are, gypysies...”) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Label definitions, annotation guidelines, and sample dialogues. Given that toxic language and bias frequently manifest in nuanced or implicit forms only detectable within multi-turn conversations, the importance of dialogical context is emphasized in both our guidelines and example selection. We present two examples in Tables 4 (racism) and 5 (sexism) that highlight how context shapes annotation decisions, illustrating why single utterances may be insufficient to reliably capture subtle or pragmatic forms of bias.