# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 7 Discussion  
The data collection provided by the eRisk 2019 challenge [16] has served as a starting point for the scientific community interested in anorexia detection using NLP methods. In this challenge, approaches based on neural networks have been previously explored by [46; 47] with satisfactory results. Moreover, [PERSON] et al. [48] developed an ensemble approach that employs several attention-based neural sub-models to extract features and predict class probabilities. These features were later used as input features to an SVM that made the final estimation. They achieved the best F1-score (71%). [PERSON] [49] used a CNN incorporating different pre-trained word embeddings such as GloVe and ELMo. Subsequently, they conducted another study in which they used the CNN-ELMo architecture to improve their results since the word embeddings obtained from ELMo take into account the context of the words and therefore obtained better results (82% F1-score) [50]. Traditional machine learning approaches have also been commonly used in the task of detecting mental illness or more specifically in detecting signs of anorexia [8; 20; 51]. Specifically, using the SAD corpus, so far the only one available in Spanish and annotated with anorexia, [PERSON] et al. [33] have shown great effectiveness in detecting anorexia using traditional machine learning algorithms such as SVM and MLP reaching both an F1-score of 91.6%. However, these algorithms are not flexible enough to capture more complex relationships naturally and are not suitable for large datasets. Until now, most of the research presented has been focused on English; however, with this study we have focused on the second most spoken language in the world (with over 470 million native speakers). With respect to the methods proposed, few previous studies \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Tweet** & **Annotated** & **Predicted** \\ \hline \multirow{4}{*}{564} & Aquí yo haciendo mi mejor esfuerzo por & & \\  & sacar de mi cuerpo la cena navidefa & 1 & 0 \\  & discreatemente & & \\
5297 & MOTIVACION para mis queridas & & \\  & Anitas & & \\  & j’Y que arranquen los juegos del hambre! & & \\
1076 & (también conocidos como la dieta culpas & 1 & 0 \\  & post festividades) & & \\ \hline \multirow{2}{*}{362} & j’Menos pastilla y mas zapatilla! & 0 & 1 \\  & Esta vida que llevo me esta matando. & & 0 & 1 \\  & Debería darme a las drogas. & & \\
634 & Don’t stop. Cada dia mas cerca del & & \\  & objitvo! & & \\
4650 & Vamos que ya es Miércoles! Sin dolor no & & 1 \\  & hay évio... & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Misclassified samples from SAD in Spanish. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Tweet** & **Annotated** & **Predicted** \\ \hline \multirow{4}{*}{564} & Here I am doing my best to get the & & \\  & Christmas dinner out of my body discrecely & 1 & 0 \\
5297 & MOTIVATION for my dear Anitas & 1 & 0 \\  & And let them start the hunger games! (also & \\  & known as the post-holiday guilt diet) & 1 & 0 \\ \hline \multirow{2}{*}{362} & Less pill, more shoe! & 0 & 1 \\  & This life I lead is killing me. I should take & 0 & 1 \\  & drugs. & & \\
634 & Don’t stop. Every day closer to the goal!! & 0 & 1 \\
4650 & Come on, it’s already Wednesday! Without & 0 & 1 \\  & pain there is no success... & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Misclassified samples from SAD in English. have explored Transformer-based models to detect anorexia or other mental disorders early. These models have been of great interest in the NLP community as they are pre-trained on large corpora. During the pre-training phase, the models learn about the words, structure, morphology, grammar, and other linguistic features of the language that are beneficial for performing language representation and subsequently classify more efficiently.