# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 2 Related Work  
NLP techniques can be used to make predictions about people with mental health problems by analyzing the messages they write on Facebook, Twitter, and other social media [4; 5]. These texts contain informal language in which users give information and express their thoughts, feelings, and moods about their daily lives [6; 7]. There are several studies that cover a range of mental health topics including predicting depression diagnosis [8], assessing suicide risk [9; 10], schizophrenia [11; 12], stress [13] and obsessive-compulsive disorder [14]. As the prediction of the first symptoms of mental illness has received considerable attention, several competitions such as eRisk and CLPsych have recently taken place. In 2017, eRisk ([[https://erisk.irlab.org/](https://erisk.irlab.org/)]([https://erisk.irlab.org/](https://erisk.irlab.org/))) at CLEF (Conference and Labs of the Evaluation Forum) proposed an author profiling task where the aim is to identify specific mental conditions such as depression [15]. Presently eRisk encompasses the groups interested in this type of challenge, that of presenting computer models that can early identify users with symptoms of depression and anorexia [16]. Another workshop is CLPsych ([[https://clpsych.org/](https://clpsych.org/)]([https://clpsych.org/](https://clpsych.org/))) (Computational Linguistics and Clinical Psychology) which has been held until 2019 as part of the yearly conference organized by the American Association of Computational Linguistics (NAACL). The participation in this conference shows the advances in the detection of current problems such as depression, Alzheimer's, autism, violence, and aphasia [17]. However, all these clinics and workshops are focused on English texts. Eating disorders are serious mental illnesses, and anorexia nervosa is associated with the highest mortality rate of any psychiatric illness [18], and for this reason, there is a need to study these types of diseases and detect them early. [PERSON] et al. [19] conducted a study in which they found that the writings of pro-food-disordered bloggers have a more closed style and are less emotionally expressive. These findings indicate that writing can be analyzed with NLP techniques to detect whether or not a person may have signs of anorexia. Many studies have been conducted with the aim of detecting mental illness automatically using ML techniques. [PERSON] et al. [20] recently conducted a review of the most widely used methods in NLP to identify cancer, mental health issues, substance abuse, and so on, and the most commonly used social networks in those studies. Most of the papers that use classical ML approaches [8; 21; 22; 23], report that the most widely used model is Logistic Regression. Some of these studies employ the LIWC tool [24] to add new features to the model. In addition, it should be noted that they use different methods for word representation such as word embeddings, bag-of-words, and tf-idf. Regarding neural networks, these have been successfully applied to many different domains and scenarios [25; 26; 27]. Specifically, in the detection of mental illness, we have found a small amount of related work. [PERSON] et al. [28] used a Recurrent Neural Network (RNN) with an attention mechanism that improved their baseline for detecting diseases such as bipolarity, addictions, autism, and depression, among others. Convolutional Neural Networks (CNN) have also been applied to the analysis of mental illness [29], obtaining values above 91% accuracy in binary classification and above 62% in multi-class classification. Recent state-of-the-art studies in NLP have been exploring techniques with pre-training and bidirectional language representation, to understand the semantic complexity of a language [30]. Bidirectional Encoder Representations from Transformers (BERT) [31] was used in a multimodal approach for automatic depression detection [32]. However, we realize that several Transformer-based models have not yet been tested for the detection of anorexia in Spanish tweets.