42 research outputs found

    Multi-task learning to detect suicide ideation and mental disorders among social media users

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    Mental disorders and suicide are considered global health problems faced by many countries worldwide. Even though advancements have been made to improve mental wellbeing through research, there is room for improvement. Using Artificial Intelligence to early detect individuals susceptible to mental illness and suicide ideation based on their social media postings is one way to start. This research investigates the effectiveness of using a shared representation to automatically extract features between the two different yet related tasks of mental illness and suicide ideation detection using data in parallel from social media platforms with different distributions. In addition to discovering the shared features between users with suicidal thoughts and users who self-declared a single mental disorder, we further investigate the impact of comorbidity on suicide ideation and use two datasets during inference to test the generalizability of the trained models and provide satisfactory evidence to validate the increased predictive accurateness of suicide risk when using data from users diagnosed with multiple mental disorders compared to a single mental disorder for the mental illness detection task. Our results also demonstrate different mental disorders' impact on suicidal risk and discover a noticeable impact when using data from users diagnosed with Post-Traumatic Stress Disorder. We use multi-task learning (MTL) with soft and hard parameter sharing to produce state-of-the-art results for detecting users with suicide ideation who require urgent attention. We further improve the predictability of the proposed model by demonstrating the effectiveness of cross-platform knowledge sharing and predefined auxiliary inputs

    Social media mental health analysis framework through applied computational approaches

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    Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

    Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

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    Progress on NLP for mental health — indeed, for healthcare in general — is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data

    Chronological detection of depression in social media threads by means of natural language processing

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    Detecting depression in social media has become an increasingly important research area in recent years. With the widespread use of social media platforms, individuals at risk of suicide often express their thoughts and emotions online, providing an opportunity for early detection and intervention. Artificial Intelligence and, particularly, Natural Language Processing open pathways towards the processing of massive amount of messages and the detection of depression traits and other risks related to mental health. Our main thesis question rests on the early prediction of depression detection in social media messages. We explore the accuracy gained by a system as more and more information (in terms of more social messages over time) from a user are available. Is the system becoming more and more accurate given subsequent information or is there a limit? How many messages do we need to train a simple model capable to attain an accuracy above a threshold? Do recent messages add much information to older ones? These research questions have arisen in our work. A key cornerstone in artificial intelligence-based approaches rests, needless to say, on to the available data-sets. The data available bounds the ability of the system to gain knowledge. Thus, an important part of this work consists on an overview of the data-sets used to detect depression in social media, also mentioning various extra data-sets along the way. In our study we found that there are international challenges devoted to this task, among others, CLPsych. We explore simple though efficient inference algorithms able to classify messages; next, we test the ability of the models to classify a user as with or without risk, just given social messages written by the user. In an attempt to put the focus on our main research question (i.e. assessing the impact of getting more and more information across time to gain accuracy in the task of message classification in the frame of early detection of depression signs) we opted for simple classifiers, that is, linear approaches, and left out of the scope exploring the behaviour of different classification approaches. Our experimental framework is developed using the practice data-set made available at CLPSych 2021. To make use of the data more intelligently, the chronological factor is added. Using a specific technique that progressively takes into account new data (chronologically) at each time, we can observe promising changes in the classification accuracy. These values might provide key ideas about the evolution of depression signs for detection. In other words, the results in a time-line might help to gain evidences that a user might be showing traces of or towards depression. At the end, some comparisons and discussion are made regarding past research work related to this field, to do a critical analysis of the results. Hizkuntza: Ingelesa

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

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    [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

    Automatic Detection of Emotions and Distress in Textual Data

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    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)
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