Depression and Self-Harm Risk Assessment in Online Forums

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4, FastText baseline, and CNN-

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

Detecting Addiction, Anxiety, and Depression by Users Psychometric Profiles

Detecting and characterizing people with mental disorders is an important task that could help the work of different healthcare professionals. Sometimes, a diagnosis for specific mental disorders requires a long time, possibly causing problems because being diagnosed can give access to support groups, treatment programs, and medications that might help the patients. In this paper, we study the problem of exploiting supervised learning approaches, based on users' psychometric profiles extracted from Reddit posts, to detect users dealing with Addiction, Anxiety, and Depression disorders. The empirical evaluation shows an excellent predictive power of the psychometric profile and that features capturing the post's content are more effective for the classification task than features describing the user writing style. We achieve an accuracy of 96% using the entire psychometric profile and an accuracy of 95% when we exclude from the user profile linguistic features

Detecting Mental Distresses Using Social Behavior Analysis in the Context of COVID-19: A Survey

Online social media provides a channel for monitoring people\u27s social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published since the COVID-19 outbreak. We analyze relevant research and its characteristics and propose new approaches to organizing the large amount of studies arising from this emerging research area, thus drawing new views, insights, and knowledge for interested communities. Specifically, we first classify the studies in terms of feature extraction types, language usage patterns, aesthetic preferences, and online behaviors. We then explored various methods (including machine learning and deep learning techniques) for detecting mental health problems. Building upon the in-depth review, we present our findings and discuss future research directions and niche areas in detecting mental health problems using social media data. We also elaborate on the challenges of this fast-growing research area, such as technical issues in deploying such systems at scale as well as privacy and ethical concerns