Understanding Patterns of Anorexia Manifestations in Social Media Data with Deep Learning (2021)

Abstract: Eating disorders are a growing problem especially among young people, yet they have been under-studied in computational research compared to other mental health disorders such as depression. Computational methods have a great potential to aid with the automatic detection of mental health problems, but state-of-the-art machine learning methods based on neural networks are notoriously difficult to interpret, which is a crucial problem for applications in the mental health domain. We propose leveraging the power of deep learning models for automatically detecting signs of anorexia based on social media data, while at the same time focusing on interpreting their behavior. We train a hierarchical attention network to detect people with anorexia and use its internal encodings to discover different clusters of anorexia symptoms. We interpret the identified patterns from multiple perspectives, including emotion expression, psycho-linguistic features and personality traits, and we offer novel hypotheses to interpret our findings from a psycho-social perspective. Some interesting findings are patterns of word usage in some users with anorexia which show that they feel less as being part of a group compared to control cases, as well as that they have abandoned explanatory activity as a result of a greater feeling of helplessness and fear.

## introduction and previous work anorexia nervosa (an) is a type of eating disorder that leads to multiple psychiatric and somatic complications and constitutes a major public health concern. it involves a restriction of energy intake in relation to needs, leading to significantly low body weight in relation to age, sex, developmental course and physical health. it includes among its typical symptomatology an intense fear of gaining weight or becoming fat and a distortion of one's body image @xcite . the incidence of an, like that of other eating disorders (ed), has increased in recent decades. in a systematic literature review for the 2000-2018 period @xcite , the reported weighted means of lifetime ed (proportion of eds at any point in life) were 8.4% (3.3-18.6%) for women and 2.2% (0.8-6.5%) for men. the authors also report that the weighted means of point ed prevalence increased over the study period from 3.5% for the 2000-2006 period to 7.8% for the 2013-2018 period. this highlights a real challenge for public health and healthcare providers. in an attempt to understand the psychosocial origins of anorexia nervosa, some studies have investigated how body image is shaped in people suffering from this mental disorder @xcite @xcite . from early research in social psychology we already know that body image, in an existential context, is the revelation of an identity that the subject constructs in the frame of concrete social relations @xcite . from a sociological perspective, some research proposes to understand bodies attending the interaction with social forces @xcite . from anthropology, new uses of bodies (tattoos, piercings, etc) support le breton's idea about the study of body in modernity as an unfinished material, as "a place of self-presentation" @xcite . this body of research could be applied to the study of anorexia nervosa without forgetting the enormous symbolism of the act of eating. it is well established that eating with others @xcite and eating the same food as the others is a major symbol of social integration @xcite . mental health disorders in general, as a very significant public health matter @xcite , have received attention in previous research in computational studies as well. the majority of research has focused on the study of depression @xcite @xcite , but other mental illnesses have also been studied, including generalized anxiety disorder @xcite , schizophrenia @xcite , post-traumatic stress disorder @xcite , risks of suicide @xcite , and self-harm @xcite . for anorexia, there are very few studies approaching the problem from a computational perspective. to our knowledge, the only publicly available social media dataset dedicated to anorexia is the erisk dataset @xcite . the winners of erisk's shared task on anorexia detection @xcite used a hierarchical attention network and obtain a state-of-the-art f1 score of 0.71. in @xcite the authors introduce a dataset annotated for multiple mental disorders including anorexia. another study (amini and kosseim) on the explainability of anorexia detection models analyzes attention weights of a neural network to show that attention at the user level correlates with the importance of individual texts for classification performance. explainability of machine learning models, especially in the field of mental health, is a very important issue. in practice, models based on neural networks are vastly successful for most nlp applications, even though they have been only briefly explored in existing computational studies on mental disorders. nevertheless, neural networks are notoriously difficult to interpret. while there is increasing interest in the field of explainability of machine learning models including in nlp @xcite , there are fewer such studies for mental health disorder detection. in the name of transparency, it is essential for any automatic system that can assist with mental health disorder detection to make its decision-making process understandable. especially in the medical domain, using black-box systems can be dangerous for patients @xcite . moreover, recently, the need of explanatory systems is required by regulations like the general data protection regulation (gdpr) adopted by the european union. while many quantitative studies in the computational analysis of mental health use features such as lexicons @xcite to study the manifestations of mental disorders in user-generated data, these models are very limited computationally in comparison to deep learning models. the behavior of powerful classifiers modelling complex patterns in the data has the potential to help uncover manifestations of the disease that are potentially difficult to observe with the naked eye, and thus be useful not only as tools for the detection of disorders, but also as analysis instruments for generating insights and potentially assisting clinicians in the diagnosis process. in our study, we propose using deep learning as a tool to aid with a deeper investigation of anorexia manifestations in social media texts. we train a hierarchical attention network to classify people with anorexia against control cases based on their social media activity. this architecture has been shown to provide good results for anorexia detection, and additionally includes in the model a series of features that encode different levels of the language (style, emotions, topics etc). to our knowledge this has not been done in previous work, and allows us the advantage of a more interpretable model. we interpret the predictions of the network as well as its hidden layers as a way to identify different patterns of anorexia symptoms in social media users, which we analyze in view of the different features, and offer hypotheses on the different patterns observed from a psychological perspective. thus, we aim to answer the following research questions: rq1. is it possible to leverage complex deep learning models and their encoding power in order to identify different patterns of anorexia symptoms in social media texts? rq2. can we characterize the differences between different groups of people with anorexia based on psycho-linguistic and emotion features, and measures of personality traits? rq3. could we identify some features to explore the hypothesis that anorexia nervosa is a way to express some degree of conflict with one's own group? ## classification experiments in order to explore the proposed hypotheses, we start by building a deep learning model in order to automatically classify texts belonging to users with anorexia and control cases. ## explaining predictions in this section we present different analyses meant to uncover insights into how the model arrives at its predictions, first looking at the attention weights and abstract internal representations of the data in the layers of the neural network, and secondly providing several feature-focused analyses, using emotions and liwc categories, as well as personality markers. ## identifiying different narratives and cognitive styles in rq3 we raised the possibility of exploring if some features from liwc and emotion lexicons allow us to identify among users with anorexia a higher degree of conflict with their own group and some degree of social isolation. as we can see in degree of social isolation. these results connect with a pattern that we find in the use of personal pronouns among the different clusters. as we can observe, users with anorexia employ significantly fewer words under the category we (we, us, our), a clear linguistic marker of a sense of belonging. users in ano1 use it significantly less than users in ano2 and than control cases, suggesting that a higher degree of conflict with one's own group or a higher degree of social isolation may be linked to the more severe manifestations of anorexia. the opposite pattern is found in the use of the first person pronouns that liwc collects under the category i (i, me, mine): users suffering from anorexia use it significantly more than control cases and cluster ano1 uses it significantly more than cluster ano2. three other features expressing social processes, family, friends and humans are more present in the narratives of users with anorexia than among control cases. the greater presence of these linguistic categories may indicate a greater centrality of social relations in the identity of these subjects suffering from anorexia. we would need to design new strategies to go deeper into this interpretation, but this greater centrality of social relations categories in people with anorexia could be derived, precisely, from a greater degree of conflict with the social environment. we also observed in cognitive style is a concept used in cognitive psychology to describe the way individuals think, perceive, and remember information @xcite . research in psychology suggests that some cognitive styles are more prevalent in some patients suffering from depression and anorexia @xcite . as we can see in within cognitive processes we find it interesting to analyze certain features such as certainty, tentative and causation where there are significant differences among clusters. certainty expresses a rigid or absolute style of thinking and tentative expresses a more flexible or less absolute style of thinking. we find significant differences in these two categories between users with anorexia and control cases. certainty is more used in narratives from users suffering anorexia and could indicate a major degree of cognitive conflict. we see the opposite pattern with tentative, that just expresses a flexible style. it could be expressing the two sides of the same phenomenon. with causation we only found statistically significant differences between ano1 and the other two clusters (similarly to what we found for the expression of fear, as seen in ## connection to clinical research trying to identify different groups of patients who claim to suffer from anorexia nervosa or have compatible symptomatology may be a way to develop a better understanding of this complex pathology @xcite . research such as that of @xcite shows a relationship between six clusters of young adolescents suffering from anorexia and higher levels of psychological difficulties and lower levels of body esteem. however, these clusters rely exclusively on symptomatology linked to eating behavior. we think that our results show that a deep learning model applied to social media texts allows us to identify clusters of patients considering more variables than those related to the eating disorder itself. from these initial results we can provide some insights to clinical discussion. more and more clinicians @xcite ) ask themselves about the intractability of anorexia nervosa, including the disconcerting aspect of the recovery of a significant number of patients not receiving formal treatment (keski-rahkonen, 2014). as @xcite state in a recent review of this issue, bruch's proposal regarding the charactization of typical anorexia in terms of body image disturbances (bid) @xcite and the relevance of this construct in different editions of the diagnostic and statistical manual of mental disorders (apa, 2014), may have over-directed clinical practice, rendering other aspects of psychopathology invisible and increasing the numbers of patients diagnosed as atypical cases. analyzing the language of people suffering from anorexia, we found traces of problems that could guide new approaches indicating the existence of a conflict between the patient and his or her own group: lower use of the first person plural and a greater presence of features expressing social processes. some research focusing on the discourse analysis of people suffering from anorexia points to the existence of a link between acting on one's own body as a mechanism to take control over their lives @xcite . our results may indicate that a possible origin of this need of control could be the social conflict with one's own group and the inability to communicate this conflictual situation. in this sense, @xcite have shown the relationship between parent-child conflict and family communication styles and the development of eating disorders. in addition, @xcite have demonstrated in their experimental research the relevance of a verbal expression of emotions in patients with anorexia nervosa. more research is needed, for instance it may be interesting to revisit one of bruch's ideas in her seminal work which has not been as ## ethical considerations powerful machine learning models that can be trained to detect or predict the development of mental health disorders can be very valuable, but any deployment of a tool for mental disorder detection should take into account potential ethical concerns. if such tools are used by third parties (such as employers seeking to filter candidates based on their mental health profile), this could compromise the privacy of the subjects. we suggest that the development of an ethical standard is necessary, and that launching such tools could be accompanied by an ethical statement to constrain its use. moreover, it is ethically necessary, and recently even required by regulations in some countries (such as countries in the european union) that artificial intelligence models used in the mental health domain have interpretable behavior. we hope that we were able to take a step forward in this direction, by providing an in-depth explanation of the representations generated by our neural network, and thus facilitating trust in the predictive model. ## conclusions and future work in this study, we have approached the problem of detecting people suffering from anorexia in social media thorugh training a deep learning model, and taken it a step further by explaining the behavior of the model. based on this, we identified different clusters of users suffering from anorexia with regards to the manifested symptoms, as encoded by the trained model (rq1). we presented several analyses for interpreting the decisions of the model trained to profile social media users at risk of developing anorexia, going beyond more common techniques such as attention weight analysis, and including hidden layer analysis and error analysis at different levels of the language for better understanding how mental disorders manifest in social media data (rq2). we have shown that we can interpret the detected clusters from the point of view of the social behavior of people with anorexia (rq3), and provided in-depth interpretations of these patterns as a sociopsychological phenomenon. to our knowledge, our approach and findings are novel in the domain of the computational study of anorexia, and encourage us to go deeper into the analysis of these patterns, such as looking into the use of pronouns in relation with emotions, which could help identify more clearly the existence of a social conflict. from a technical perspective, a more sophisticated analysis of the features included here (liwc categories, emotions and personality vectors) could be achieved by using more semantically rich representations than bag-of-words. one such approach would be using word embeddings to identify sub-emotions @xcite starting from plutchik's 8 emotions. moreover, including a temporal dimension as a variable could also reveal additional insights such as cases of patients with symptoms moving from one cluster to another over time. the possibility to categorize people with anorexia into different groups according to their symptoms might help with identifying those people at a higher risk of more serious developments of their disorder and also could give some inputs for for a more in-depth discussion of symptomatology in clinical forums. finally, it would also be interesting to explore the connection between anorexia and other mental disorders or manifestations such as suicide attempts.