46 research outputs found

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

    Full text link
    [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

    Semantic Similarity Models for Depression Severity Estimation

    Full text link
    Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting users' symptom severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\% improvement over state of the art in terms of measuring depression severity.Comment: Accepted at the EMNLP 2023 conferenc

    NailP at eRisk 2023: search for symptoms of depression

    Get PDF
    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

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

    Full text link
    [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

    Identificación del nivel de complejidad de texto para el entrenamiento de chatbots basado en Machine Learning: una revisión de literatura|

    Get PDF
    El nivel de complejidad textual puede ser un inconveniente para algunas personas al momento de usar Chatbots, debido a que estos programas podrían dar respuestas cuyo nivel de complejidad no sea el que entienda el usuario. Entonces, aquellos Chatbots deberían ser entrenados con un conjunto de datos cuya complejidad textual sea la deseada, para evitar confusiones con los usuarios. Para ello, se define una revisión sistemática, en la cual se usan las bases de datos de Google Scholar, ACM Digital Library e IEEE Xplore, de las cuáles se obtiene la información necesaria empleando las palabras claves definidas por el método PICOC, obteniendo un total de treinta y ocho documentos que evidencian la existencia de distintas métricas para analizar la complejidad textual de textos, así como experimentos de entrenamiento con Chatbots y los correspondientes resultados de sus interacciones con los usuarios. Además, analizando documentos de tesis asociadas al tema de investigación, se refuerzan los conceptos de que la complejidad textual puede ser analizado mediante conjunto de métricas. Finalmente, en base a lo desarrollado en la revisión de la literatura y documentos de tesis, se presentan las conclusiones deducidas.Trabajo de investigació

    Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features

    Get PDF
    Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies

    Early Detection of Depression: Social Network Analysis and Random Forest Techniques

    Get PDF
    [Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.Ministerio de Economía y Competitividad; TIN2015-70648-PXunta de Galicia; ED431G/01 2016-201

    Automatic Detection of Emotions and Distress in Textual Data

    Get PDF
    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)
    corecore