# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 8 Conclusions and Future Work  
Recently, due to the large amount of data generated on social media, there is increasing concern about users who promote eating disordered behaviors through the diffusion of messages and images that encourage thinness and harmful weight loss control practices. In this paper, we assess the detection of anorexia in tweets written in Spanish and provide a deeper understanding of the capabilities of new techniques applied to the detection of this kind of disorder.  
As far as we know, there is only one dataset labeled with anorexia in Spanish called SAD, which is the one we employ to evaluate our models. The models that we have chosen reflect the state-of-the-art in NLP classification tasks including neural networks such as LSTM, CNN and Transformer-based models based on transfer learning. Moreover, we compare the performance of multilingual and monolingual pre-trained models including M-BERT, BETO and XLM.  
The results we have obtained are promising since as opposed to neural networks, Transformer-based models are most effective over small amounts of data. The best performance was achieved by the BETO model with an F1-score of 94.1%, outperforming the best result obtained by [PERSON] et al. (2018). In the error analysis we studied the vocabulary covered in the different pre-trained models and noticed that some words related to anorexia such as _gorda_ (fat) or _delgada_ (thin) were not found in the multilingual models' vocabulary but were included in the monolingual model BETO, showing the importance of trained a model for a specific language.  
As future work, we plan to apply techniques based on sentiment analysis or emotion detection to detect anorexia when there is no implicit information about the mental illness vocabulary. We would also like to test the effectiveness of pre-trained models in detecting other mental disorders such as depression, anxiety, or stress. Regarding language modeling, we plan to use Spanish word embeddings trained on Twitter, both the existing embeddings ([PERSON] et al., 2018) and self-trained embedding.  
**Author Contributions:** Conceptualization, M.-T.M.-V. and M.C.D.-G.; methodology, M.-T.M.-V. and M.C.D.-G.; software, P.L.-U. and F.M.P.-A.; validation, M.-T.M.-V. and M.C.D.-G.; formal analysis, P.L.-U., F.M.P.-d.-A. and M.C.D.-G.; investigation, F.M.P.-d.-A. and M.C.D.-G.; resources, P.L.-U. and F.M.P.d.-A.; data curation, P.L.-U. and F.M.P.d.-A.; writing--original draft preparation, P.L.-U., F.M.P.-d.-A. and M.C.D.-G.; writing--review and editing, M.-T.M.-V. and M.C.D.-G.; visualization, P.L.-U. and F.M.P.d.-A.; supervision, M.-T.M.-V. and M.C.D.-G.; project administration, M.-T.M.-V. and M.C.D.-G.; funding acquisition, M.-T.M.-V. and M.C.D.-G. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by a grant from European Regional Development Fund (ERDF), LIVING-LANG project [RTI2018-094653-B-C21], and the Ministry of Science, Innovation, and Universities (scholarship [FPI-PRE2019-089310]) from the Spanish Government.  
Not applicable.  
**Data Availability Statement:** The dataset used in this study is available at: [[https://github.com/plubeda/SAD](https://github.com/plubeda/SAD)]([https://github.com/plubeda/SAD](https://github.com/plubeda/SAD)).  
The authors declare no conflict of interest.