# High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus
## 5 Remarks and Limitations  
The research project described in this paper contributes to the field of HS detection by providing several resources for other machine-learning-focused works. Firstly, we created the first corpus of anti-immigrant tweets written in Mexican Spanish and, to our knowledge, the first dataset in Latin American Spanish on this specific type of HS. At the time of writing, 3326 positive and 8256 negative instances compose the \"Mexican Anti-Immigrant Speech Training Corpus\" (MAISTC). Although the number of negative and positive examples might seem low, we focused on the quality of the data over quantity, while still retaining sufficient instances to train an automatic detection model with acceptable performance. Our dataset has \"real\" and concrete examples of HS and non-HS produced in the Mexican Twittersphere and collected through a rigorous annotation protocol based on empirical knowledge and solid definitions. In addition, we can always include new examples in the corpus. The unanticipated changes in the Twitter API politics regarding academic research were a setback for this project and any research on social media. Despite this situation, with our data collection protocol proposal, we could expand the analysis to other social media platforms, such as Facebook, Instagram, or YouTube. Including quality control mechanisms in text annotation is a noteworthy contribution, as it remains independent of the specific investigation phenomenon and language. Consequently, these mechanisms can be readily applied to other research projects, enhancing their reliability in corpus annotation. However, the choice of including non-experts is not without drawbacks. Finding non-experts is not as simple as advertising the project on social media. The low interest from the online community users that we targeted to partake in the annotation task meant that we had to rely on more traditional social networks, mainly professional and personal. Overall, the participation of non-experts and the number of data annotated through this process was lower than expected, but this does not diminish the validity of the methodology. A workaround solution would involve non-experts with existing interest in research, such as students. Furthermore, this research proposes a sophisticated definition of online Mexican anti-immigrant speech based on the empirical observation of the phenomenon. This definition is functional in the Mexican context but is also general enough to be used in other contexts. The greatest challenge that we had to overcome during the definition work was the rapid evolution of migration and migration policies, which transformed the terms of the online debate throughout the period of interest (2018-2022). Not identifying these moments of change and not adapting the data collection strategy accordingly meant taking the risk that the final corpus would be particularly sensitive to certain events. In this work, such a risk was controlled by including \"temporal filters\" over the entire period, defined based on detailed knowledge of the context and continuous monitoring of migration-related topics in Mexico, which corresponded to the various significant elements that detonated the discussion on migration. Thanks to our baseline automatic classification model, we have achieved the ability to automate classification processes on large volumes of data. However, our experimentation has highlighted the criticality of being mindful of potential biases induced by the data preparation methods. Furthermore, it is crucial to acknowledge the limitation of interpretability in our best-performing baseline model, which relies on a neural network. To address this limitation comprehensively, we have devised a strategy to leverage white box learners that enhance interpretability and transparency.