Background Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. Objective This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. Methods We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). Results We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. Conclusions The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.

Anorexia Nervosa (AN) is a serious mental disorder that has been proved to be traceable on social media through the analysis of users' written posts. Here we present an approach to generate word embeddings enhanced for a classification task dedicated to the detection of Reddit users with AN. Our method extends Word2vec's objective function in order to put closer domain-specific and semantically related words. The approach is evaluated through the calculation of an average similarity measure, and via the usage of the embeddings generated as features for the AN screening task. The results show that our method outperforms the usage of fine-tuned pre-learned word embeddings, related methods dedicated to generate domain adapted embeddings, as well as representations learned on the training set using Word2vec. This method can potentially be applied and evaluated on similar tasks that can be formalized as document categorization problems. Regarding our use case, we believe that this approach can contribute to the development of proper automated detection tools to alert and assist clinicians.

This paper proposes an approach for the early detection of anorexia nervosa (AN) on social media. We present a machine learning approach that processes the texts written by social media users. This method relies on a set of features based on domain-specific vocabulary, topics, psychological processes, and linguistic information extracted from the users' writings. This approach penalizes the delay in detecting positive cases in order to classify the users in risk as early as possible. Identifying anorexia early, along with an appropriate treatment, improves the speed of recovery and the likelihood of staying free of the illness. The results of this work showed that our proposal is suitable for the early detection of AN symptoms.

Substance abuse and mental health issues are severe conditions that affect millions. Signs of certain conditions have been traced on social media through the analysis of posts. In this paper we analyze textual cues that characterize and differentiate Reddit posts related to depression, eating disorders, suicidal ideation, and alcoholism, along with control posts. We also generate enhanced word embeddings for binary and multi-class classification tasks dedicated to the detection of these types of posts. Our enhancement method to generate word embeddings focuses on identifying terms that are predictive for a class and aims to move their vector representations close to each other, while moving them away from the vectors of terms that are predictive for other classes. Variations of the embeddings are defined and evaluated trough predictive tasks, a cosine similarity-based method, and a visual approach. We generate predictive models using variations of our enhanced representations with statistical and deep learning approaches. We also propose a method that leverages the properties of the enhanced embeddings in order to build features for predictive models. Results show that variations of our enhanced representations outperform in Recall, Accuracy and F1-Score the embeddings learned with Word2vec, DistilBERT, GloVe's fine-tuned pre-learned embeddings and other methods based on domain adapted embeddings. The approach presented has the potential to be used on similar binary or multi-class classification tasks that deal with small domain-specific textual corpora.

BACKGROUND Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia Nervosa (AN) is defined by the thought of being overweight despite being dangerously underweight. Psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can be manifested on social media, where both harmful and beneficial content is shared daily. OBJECTIVE The aim of this work is to characterize Spanish speaking users with Anorexia signs on Twitter through the extraction and inference of behavioral, demographical, relational, and multi-modal data. This analysis is focused on characterizing and comparing users at different stages of the process to overcome the illness, including treatment and full recovery periods considering the Transtheoretical Model of Health Behavior Change (TTM). METHODS We analyze tweets published by users going through different stages of Anorexia. Users are characterized through their writings, posting patterns, relations, and images. We analyze the differences among users going through each stage of the illness and control users (users not suffering from AN). We also analyze the topics of interest of their followees (users followed by them). We perform a clustering approach to distinguish users at an early phase of the illness (precontemplation) from users that recognize that their behavior is problematic (contemplation); and generate models dedicated to the detection of tweets and images related to AN. We consider two types of control users: focused control users that use terms related to anorexia; and random control users. RESULTS We found significant differences between users at each stage of the recovery process (P<.001) and control groups. Users with AN tend to tweet more at night, with a median sleep period tweeting ratio of 0.05 in comparison to random control users (0.04) and focused control users (0.03). Pictures are relevant for the characterization of users. Focused and random control users are characterized by the usage of text on their profile pictures. We also found a strong polarization between focused control users, and users at the first stages of the disorder. There was a strong correlation (Spearman’s coefficient) among the shared interest between users with AN and their followees (0.96). Also, the interests of recovered users and users in treatment were more highly correlated to those corresponding to the focused control group (0.87 for both) in comparison to AN’s users (0.67), suggesting a shift on users’ interest during the recovery process. CONCLUSIONS We have mapped signs of Anorexia Nervosa to the Social media context. These results enforce the findings of related work on other languages and involve a deep analysis on the topics of interest of users at each phase of the disorder. The features and patterns identified provide a basis for the development of detection tools and recommender systems.