Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction

[EN] This paper summarizes the contributions of the PRHLT- UPV team as a participant in the eRisk 2020 tasks on self-harm detection and prediction of depression levels from social media. Computational methods based on machine learning and natural language processing have a great potential to assist with early detection of mental disorders of social media users, based on their online activity.We use multi-dimensional representations of language, and compare various deep learning models' performance, exploring rarely approached avenues in previous research, including hierarchical deep learning architectures and pre-trained transformers and language models.The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Rosso, P. (2020). Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction. CEUR Workshop Proceedings. 2696:1-12. http://hdl.handle.net/10251/166536S112269

UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet

[EN] This paper presents the contributions of the UPV-Symanto team, a collaboration between Symanto Research and the PRHLT Center, in the eRisk 2021 shared tasks on gambling addiction, self-harm detection and prediction of depression levels. We have used a variety of models and techniques, including Transformers, hierarchical attention networks with multiple linguistic features, a dedicated early alert decision mechanism, and temporal modelling of emotions. We trained the models using additional training data that we collected and annotated thanks to expert psychologists. Our emotions-over-time model obtained the best results for the depression severity task in terms of ACR (and second best according to ADODL). For the self-harm detection task, our Transformer-based model obtained the best absolute result in terms of ERDE5 and we ranked equal first in terms of speed and latency.The authors from Universitat Politècnica de València thank the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana. We would like to thank the two anonymous reviewers who helped us improve this paper.Basile, A.; Chinea-Ríos, M.; Uban, A.; Müller, T.; Rössler, L.; Yenikent, S.; Chulvi-Ferriols, MA.... (2021). UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet. CEUR. 908-927. http://hdl.handle.net/10251/19067090892

An emotion and cognitive based analysis of mental health disorders from social media data

[EN] Mental disorders can severely affect quality of life, constitute a major predictive factor of suicide, and are usually underdiagnosed and undertreated. Early detection of signs of mental health problems is particularly important, since unattended, they can be life-threatening. This is why a deep understanding of the complex manifestations of mental disorder development is important. We present a study of mental disorders in social media, from different perspectives. We are interested in understanding whether monitoring language in social media could help with early detection of mental disorders, using computational methods. We developed deep learning models to learn linguistic markers of disorders, at different levels of the language (content, style, emotions), and further try to interpret the behavior of our models for a deeper understanding of mental disorder signs. We complement our prediction models with computational analyses grounded in theories from psychology related to cognitive styles and emotions, in order to understand to what extent it is possible to connect cognitive styles with the communication of emotions over time. The final goal is to distinguish between users diagnosed with a mental disorder and healthy users, in order to assist clinicians in diagnosing patients. We consider three different mental disorders, which we analyze separately and comparatively: depression, anorexia, and self-harm tendencies.The authors thank the EU-FEDER Comunitat Valenciana 2014- 2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Chulvi-Ferriols, MA.; Rosso, P. (2021). An emotion and cognitive based analysis of mental health disorders from social media data. Future Generation Computer Systems. 124:480-494. https://doi.org/10.1016/j.future.2021.05.032S48049412

NailP at eRisk 2023: search for symptoms of depression

Depression is a global health concern with severe consequences for individuals, making its recognition and understanding crucial. Recently, there has been a growing interest in utilizing social media platforms as valuable sources of information to gain insights into individuals’ experiences with depression. Analyzing textual data from diverse user populations enables the identification of common symptoms, triggers, coping mechanisms, and potential warning signs. Researchers have developed algorithms and machine learning models to automate the detection of depressive symptoms in text, facilitating more efficient screening and early intervention. This paper describes the participation of team NailP in the CLEF eRisk 2023 task 1, which focuses on ranking sentences from user writings based on their relevance to symptoms of depression. The goal is to evaluate the sentences and determine their level of relevance to each symptom outlined in the Beck Depression Questionnaire-II. Such participation contributes to the development of effective methods and tools for identifying and predicting potential risks and dangers associated with depression in online environments.The authors thank CNPq, CAPES, FAPERJ, and CEFET/RJ for partially funding this research. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Automatic Detection of Emotions and Distress in Textual Data

Online data can be analyzed for many purposes, including the prediction of stock market, business, and political planning. Online data can also be used to develop systems for the automatic emotion detection and mental health assessment of users. These systems can be used as complementary measures in monitoring online forums by detecting users who are in need of attention. In this thesis, we first present a new approach for contextual emotion detection, i.e. emotion detection in short conversations. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a final classifier, that can be neural or SVM-based. The results from our experiments showed that, by providing a higher and more robust performance, SVM can act as a better final classifier in comparison to a feed-forward neural network. We then extended our model for emotion detection, and created an ensemble approach for the task of distress detection from online data. This extended approach utilizes several attention-based neural sub-models to extract features and predict class probabilities, which are later used as input features to a Support Vector Machine (SVM) making the final classification. Our experiments show that using an ensemble approach which makes use different sub-models accessing diverse sources of information can improve classification in the absence of a large annotated dataset. The extended model was evaluated on two shared tasks, CLPsych and eRisk 2019, which aim at suicide risk assessment, and early risk detection of anorexia, respectively. The model ranked first in tasks A and C of CLPsych 2019 (with macro-average F1 scores of 0.481 and 0.268, respectively), and ranked first in the first task of eRisk 2019 in terms of F1 and latency-weighted F1 scores (0.71 and 0.69, respectively)

Reconocimiento de depresión en redes sociales basado en la detección de síntomas

Depression is a common mental disorder that affects millions of people around the world. Recently, several methods have been proposed that detect people suffering from depression by analyzing their language patterns in social media. These methods show competitive results, but most of them are opaque and lack of explainability. Motivated by these problems, and inspired by the questionnaires used by health professionals for its diagnosis, in this paper we propose an approach for the detection of depression based on the identification and accumulation of evidence of symptoms through the users’ posts. Results in a benchmark collection are encouraging, as they show a competitive performance with respect to state-of-the-art methods. Furthermore, taking advantage of the approach’s properties, we outline what could be a support tool for healthcare professionals for analyzing and monitoring depression behaviors in social networks.La depresión es un trastorno mental que afecta a millones de personas en todo el mundo. Recientemente, se han propuesto varios métodos que detectan personas que sufren depresión analizando sus patrones de lenguaje en las redes sociales. Estos métodos han mostrado resultados competitivos, sin embargo la mayoría son opacos y carecen de explicabilidad. Motivados por estos problemas, e inspirados en los cuestionarios utilizados por los profesionales de la salud para su diagnóstico, en este trabajo proponemos un método para la detección de depresión basado en la identificación y acumulación de evidencia de síntomas a través de las publicaciones de los usuarios. Los resultados obtenidos en una colección de referencia son prometedores, ya que muestran un desempeño competitivo con respecto a los mejores métodos actuales. Además, aprovechando las propiedades del método, describimos lo que podría ser una herramienta de apoyo para que los profesionales de la salud analicen y monitoreen las conductas depresivas en las redes sociales

DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media

Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns including user's social interactions. The downside is that these models are trained on several irrelevant content which might not be crucial towards detecting a depressed user. Besides, these content have a negative impact on the overall efficiency and effectiveness of the model. To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content. The content then goes to our novel deep learning framework comprising of a unified learning machinery comprising of Convolutional Neural Network (CNN) coupled with attention-enhanced Gated Recurrent Units (GRU) models leading to better empirical performance than existing strong baselines