# High-Quality Data from Crowdsourcing towards the Creation of a Mexican Anti-Immigrant Speech Corpus
## 4 Results and Discussion
### Result 4: Interpretability Analysis  
To complete the analysis of our corpus, we attempted to establish more effective terms associated with anti-immigrant speech in this context. We used a representative sample of the collected data to obtain a classification model via a white box learner that gives a classification result and also allows us to infer the relationships between the results and the terms or words given as explanatory variables. We used a bag-of-words vectorizer and a linear learner (logistic regression) to map directly between individual words and the model coefficients, and also to describe the mathematical relationship between each word and the dependent variable associated with the class to be predicted (positive and negative anti-immigrant speech). The coefficient values indicate how much the class likelihood changes if there is a one-unit shift in an independent word while holding the remaining words in the model constant. The coefficient sign dictates the direction of the change. We resort to this coefficient interpretation to quantify each word's contribution to the prediction of classes. These contributions are subsequently ranked in decreasing order to create a plot such as the one illustrated in Figure 13. The results show that the terms that contribute the most to predicting anti-immigrant speech correspond to elements specific to the migration phenomenon in Mexico. Furthermore, we can see that the terms that contribute the least allude to hate speech, even to particular groups usually targeted by anti-immigrant speech elsewhere, thus confirming the context-specific nature of our corpus. For illustration, we have chosen a sample of tweets containing both high and low contributory terms (highlighted in blue and pink, respectively), as shown below. 1. \"_No los dejen ingresar al pals_ [Mexico] _Veraciano_ _[PERSON] pde con todo respeto regreselos por donde vinieron si ellos no nos repeatan mi lo haemos nosotros nadas por que uds dice\"_. Translation: \"Do not let them enter the country (Mexico), Mr. President. Report them where they came from. If they do not respect us, we won't either, just because you say so\". 2. \"_Travel_ [Mexico] _Aqui no los querenos! [PERSON] serior alcalde, siga con su postura, le aseguro que los Tijuaness se lo van a agradecer de sobremaneral! Ulstedes no tienen por que soporat semejantes transgresiones por parte de los [Mexico] _Hontluresl_. _@INAMI_mx deberau deportarlos!_\" Translation: \"Get out, Hondurans. We don't want you here! Thank you, Mr. Mayor, keep your stance. I assure you that the people of Tijuana will thank you greatly! You do not have to put up with the Honduran's transgressions. @INAMI_mx should report them\". 3. \"_Asi es! Confirmado por ellos mismos de que en la caranaana_ [Mexico] _Mijrante_ [vienen]  
6. _\"Me encantaria darte unas nalgadas rics. A mi me encantaria_ _pegarte_ _una hostia en la cara_\". Translation: \"I would love to spank you. I would love to punch you in the face\". Figure 13: Sample of the terms that contribute more in predicting the speech category of a text (positive or negative anti-immigrant speech). (**a**) Positive values are biased towards anti-immigrant speech. (**b**) Negative values are biased towards negative cases of anti-immigrant speech. The first three tweets correspond to positive anti-immigrant speech cases, whereas the remaining tweets are negative. As can be observed, the highly contributory terms are coherent in predicting positive anti-immigrant speech cases and vice versa. Negative contributions are not considered determinant in predicting anti-immigrant speech, although they could be pejorative or tied to anti-immigrant speech in some contexts, such as Western Europe or the United States (e.g., _Arab_). Instead, the model gives more relevance to pejorative terms contextualized within the geographical context of the problem--for instance, _perrito hondureno (Honduran dog)_, _(Mara migrant)_, _migrante centroamericano (Central American migrant)_.