## 6 Results and Discussion
### Experiment II: Victims vs. Threat  
In Table 7, we can see all the performances are above the 0.70 of _accuracy_. The results are, in general, smaller than in Experiment I; this could be because the size of the training set for the current experiment is smaller or due to the difficulty that we have already mentioned in Section 5, describing the scenario of Experiment II: as some humans annotators reported, one of the common rhetorical strategies in political discourse is to precede any critical statement towards the immigrant collective with an expression of compassion towards the human drama that it represents in order not to be accused of xenophobia. For instance, the speaker is going to say that Spain could not admit more immigrants (threat) but she starts speaking about how many people died trying to arrive (_Victims_). Methodologically, we decided to assign only one label per sentence, and perhaps it would have been more effective to annotate different syntagmas within the same sentence, at least in those sentences in which this discursive strategy was developed. Similar to the previous experiment, the highest accuracy was obtained by BETO, but this time with 0.834 of accuracy and a standard deviation of 0.034. This performance was significantly better than the one of SpanBERTa for \(p<0.05\) using the Mann-Whitney U Test. In Table 8, we show some of the n-grams (without including stopwords) more relevant for each label, for example: _atencion humanitarian_ (humanitarian care), _atencion sanitaria_ (healthcare), _acogida personas_ (welcome of people). These relevant n-grams allow us to figure out that the phrases are more likely to reflect the needs and pain of immigrants when they are seen as _victims_. In Table 9, we present the confusion matrix of BETO at obtaining its result from the Table 7. We can see that BETO is almost equally effective at detecting _Victims_ and _Threat_ dimensions. In Table 10 we show some texts that were classified correctly and wrongly, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**N-Grams with Highest Mutual Information**} \\ \hline \multirow{4}{*}{**Nonsstereotype**} & _immigrantes irregulares; sehor [PERSON]; sehor zapatero; patses africanos; abordar asunto; abordar problema;_ \\  & _absolutamente acuerdo; accion exterior; accion polplica; acoger personas; acogida refugialos; acogida temporal;_ \\  & _acorde derechos; acuerdo materia; acuerdos gobierno; acuerdos marruecos; acuerdos mauritania; acuerdos_ \\  & _readnision; adquisicion nicanolidad; afecta union europea; agencia europea fronteras; agusa canarias;_ \\  & _agusa territoriales; asilio inmigracion; asunto precupa; atencion inmigracion; autoridad moral;_ \\  & _ayuda refugialo; ayuda africa;..._ \\ \hline \multirow{4}{*}{**Stereotype**} & _efecto lamada; inmigrantes ilegales; polplica inmigracion; inmigracion irregular; union europea; materia_ \\  & _immigracion; derechos humanos; inmigrantes iregulares; consejo europeo; inmigracion legal; drama humano;_ \\  & _regularization_ \\  & _regularization_ \\  & _italiscana; seles humans; politic politicia comunic; process regularization; immigrationcion_ \\  & _delitencencia; lilegada masiva; respeto derechos; situacion irregular; economic stumpida; unjeres immigrantse;_ \\  & _centros acogida; orden expulsion; centros interamento; costas canarias; miles personas; europea inmigracion;_ \\  & _poltica migratoria; politicia exterior; respeto derechos humanos; menores acompalados;_ \\  & _acogida canarias; drama humanitario; empresarios sindicatos;_ \\  & _menores inmigrantes; crecimiento economico; acogida immigrantse;..._ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bigrams and trigrams with highest mutual information with respect to Stereotype and Nonsstereotype labels.