Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text (original) (raw)
Related papers
Deep Learning for Depression Detection of Twitter Users
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 2018
Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people's life. This close relationship between social media platforms and their users has made these platforms to reflect the users' personal life on many levels. In such an environment, researchers are presented with a wealth of information regarding one's life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.
Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms
Computational Intelligence and Neuroscience
Millions of people worldwide suffer from depression. Assessing, treating, and preventing recurrence requires early detection of depressive symptoms as depression-related datasets expand and machine learning improves, intelligent approaches to detect depression in written material may emerge. This study provides an effective method for identifying texts describing self-perceived depressive symptoms by using long short-term memory (LSTM) based recurrent neural networks (RNN). On a huge dataset of a suicide and depression detection dataset taken from Kaggle with 233337 datasets, this information channel featured text-based teen questions. Then, using a one-hot technique, medical and psychiatric practitioners extract strong features from probably depressed symptoms. The characteristics outperform the usual techniques, which rely on word frequencies rather than symptoms to explain the underlying events in text messages. Depression symptoms can be distinguished from nondepression signals ...
Deep Learning for Depression Detection from Textual Data
Electronics, 2022
Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to i...
Characterisation of mental health conditions in social media using Informed Deep Learning
Scientific Reports, 2017
The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients’ own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of ‘in the moment’ daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accu...
Automatic Detection and Classification of Mental Illnesses from General Social Media Texts
Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Methods and Applications, 2021
Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. (2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts.
Deep learning for prediction of depressive symptoms in a large textual dataset
Neural Computing and Applications, 2021
Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth's own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Social media platforms are vast reservoirs of human sentiment and behavior, making them ripe for depression detection. This literature review delves into approaches for this detection using data analysis, deep learning, natural language processing (NLP), and machine learning (ML). We discuss data types used and explore deep learning techniques like CNN, RNN, and DNN, applied across platforms such as Facebook, Twitter, and Reddit. The review also highlights NLP's role and ML algorithms, notably SVM, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Trees. We analyze depression causes, its link with social media, and variations across age and gender. This comprehensive study guides researchers and practitioners in technology-driven mental health solutions.
Multitask learning for recognizing stress and depression in social media
arXiv (Cornell University), 2023
Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early recognition of stress and depression. Although many research works have been introduced targeting the early recognition of stress and depression, there are still limitations. There have been proposed multi-task learning settings, which use depression and emotion (or figurative language) as the primary and auxiliary tasks respectively. However, although stress is inextricably linked with depression, researchers face these two tasks as two separate tasks. To address these limitations, we present the first study, which exploits two different datasets collected under different conditions, and introduce two multitask learning frameworks, which use depression and stress as the main and auxiliary tasks respectively. Specifically, we use a depression dataset and a stressful dataset including stressful posts from ten subreddits of five domains. In terms of the first approach, each post passes through a shared BERT layer, which is updated by both tasks. Next, two separate BERT encoder layers are exploited, which are updated by each task separately. Regarding the second approach, it consists of shared and task-specific layers weighted by attention fusion networks. We conduct a series of experiments and compare our approaches with existing research initiatives, single-task learning, and transfer learning. Experiments show multiple advantages of our approaches over state-of-the-art ones.
Mental Illness and Suicide Ideation Detection Using Social Media Data
2021
Mental disorders and suicide have become a global public health problem. Over the years, researchers in computational linguistics have extracted features from social media data for the early detection of users susceptible to mental disorders and suicide ideation. Lack of reliable and inadequate data and the requirement of interpretability can be identified as the principal reasons for the low adoption of neural network architectures in recognizing individuals with mental disorders and suicide ideation. In recent years, a gradual increase in the use of deep neural network architectures in detecting mental disorders and suicide ideation with low false positive and false negative rates became feasible. Our research investigates the efficacy of using a shared representation to learn lower-level features mutual among mental disorders and between mental disorders and suicide ideation. 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 comorbidities on suicide ideation and use two unseen datasets to investigate the generalizability of the trained models. We use data from two different social media platforms to identify if knowledge can be shared between suicide ideation and mental illness detection tasks across platforms. Through multiple experiments with different but related tasks, we demonstrate the effectiveness of multi-task learning (MTL) when predicting users with mental disorders and suicide my journey to become a researcher and supporting me exceedingly throughout these years. I am incredibly thankful to my kids Thevinshya, Thevinya and Partheeshan, for their understanding of the time and energy I had to contribute throughout the years. I am ever grateful to my mother Turin, my father Piyasena and my sister Chaminie for encouraging my journey in the pursuit of knowledge and my father-in-law Karunagaran and my mother-in-law Nagulambigai for their continuous support. Great thanks to my labmate Ehsan Amjadian for being an amazing friend throughout the years. Through our insightful discussions over the years, I gained a wealth of knowledge about NLP and machine learning. I would like to give my heartful gratitude to all my lab mates and colleagues for their support throughout the years. I would like to thank
New Generation Computing
People share textual posts about their interests, routines, and moods on social platforms, which can be targeted to evaluate their mental state using diverse techniques such as lexical approaches, machine learning (ML), and deep learning (DL). Bigger grams (bi, tri, or quad) carry more contextual information than unigrams. However, most of the models used in the classification of depression include only unigrams. Moreover, the well-known depression classifiers, the recurrent neural networks (RNN), retain only the sequential information of the text and ignores the local features of postings. We suggest using a convolutional neural network of multiple channels (MCNN) to capture local features and larger context from user posts. Also, each channel has a dedicated dot-product attention layer to capture global features from local features of various context levels. The proposed model is tested on a depression dataset CLEF-eRisk 2018 with 214 depressed and 1493 non-depressed users' posts. Experimental results show that our model achieved competitive accuracy, recall, and f-score of 91.00%, 76.50%, and 70.51%, respectively. Accuracy is up to 5.00% higher and recall is approximately 24% higher than multi-channel CNN without an attention layer. Significant grams highlighted by the attention mechanism can be employed to provide Sarika Jain and Mayank Dave have contributed equally to this work.