High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus

Currently, a significant portion of published research on online hate speech relies on existing textual corpora. However, when examining a specific context, there is a lack of preexisting datasets that include the particularities associated with various conditions (e.g., geographic and cultural). This issue is evident in the case of online anti-immigrant speech in Mexico, where available data to study this emergent and often overlooked phenomenon are scarce. In light of this situation, we propose a novel methodology wherein three domain experts annotate a certain number of texts related to the subject. We establish a precise control mechanism based on these annotations to evaluate non-expert annotators. The evaluation of the contributors is implemented in a custom annotation platform, enabling us to conduct a controlled crowdsourcing campaign and assess the reliability of the obtained data. Our results demonstrate that a combination of crowdsourced and expert data leads to iterative improvements, not only in the accuracy achieved by various machine learning classification models (reaching 0.8828) but also in the model's adaptation to the specific characteristics of hate speech in the Mexican Twittersphere context. In addition to these methodological innovations, the most significant contribution of our work is the creation of the first online Mexican anti-immigrant training corpus for machine-learning-based detection tasks.