Exploring the Impact of Evolutionary Computing based Feature Selection in Suicidal Ideation Detection

© 2019 IEEE. The ubiquitous availability of smartphones and the increasing popularity of social media provide a platform for users to express their feelings, including suicidal ideation. Suicide prevention by suicidal ideation detection on social media lights the path to controlling the rapidly increasing suicide rates amongst youth. This paper proposes a diverse set of features and investigates into feature selection using the Firefly algorithm to build an efficient and robust supervised approach to classifying tweets with suicidal ideation. The development of a suicidal language to create three diverse, manually annotated datasets leads to the validation of the proposed model. An in-depth result and error analysis lead to an accurate system for monitoring suicidal ideation on social media along with the discovery of optimal feature subsets and selection methods using a penalty based Firefly algorithm

Ruh sağlığı hastalıkları tanısında LIWC ve makine öğrenimi yaklaşımlarının incelenmesi

Machine learning methods are becoming increasingly popular in data analysis. In the field of mental healthcare, these methods provide support to mental disorder diagnosis. Pennebaker developed a dictionary-based text analysis program, and it is also used in mental health diagnosis. In this study, ML and Linguistic Inquiry Word Count (LIWC) studies conducted in the field of mental disorder diagnosis were examined. Researchers aim to integrate LIWC with machine learning to conduct more comprehensive studies. The objective of this study is to examine how combining ML and LIWC methods can detect mental disorder with a focus on comparative research. For this purpose, publications related to ML and LIWC in Google Scholar, Web of Science, Scopus, EBSCO, PubMed were examined. Studies utilizing machine learning and LIWC methods in mental health diagnosis were reviewed to establish an overview of the literature. A table summarizing 15 articles on integrating machine learning and LIWC for mental disorder identification was compiled. Subsequently, the working principles of machine learning and LIWC were examined, and research conducted in the field of mental disorder diagnosis was reviewed. Further research particularly those integrating or comparing these two methods needed to better understand machine learning and LIWC in mental disorder detection.Makine öğrenmesi yöntemleri veri analizi alanlarında giderek popülerlik kazanmaktadır. Bu yöntemler ruh sağlığı alanındaki tanı belirleme çalışmalarına da destek sağlamaktadır. İlk olarak, Pennebaker sözlük tabanlı bir metin analizi programı geliştirmiştir ve bu program ruh sağlığı teşhisinde de kullanılmaktadır. Bu çalışma kapsamında ruh sağlığı hastalıklar teşhisi alanında yapılmış olan makine öğrenmesi ve Linquistic Inquiry Word Count (LIWC) çalışmaları incelenmiştir. Günümüzde daha geniş araştırmalar yapabilmesi için LIWC ile makine öğrenimini birbirine entegre etmek amaçlanmaktadır. Bu çalışmanın amacı, makine öğrenmesi ve LIWC yöntemlerinin birbirine entegre edilmesinin ruh sağlığı hastalıklarının teşhisinde etkisinin araştırılmasıdır. Özellikle karşılaştırmalı araştırmalara odaklanılmıştır. Bu amaçla, makine öğrenmesi ve LIWC ile ilgili olan Google Scholar, SAGE journals, Web of Science, Scopus, EBSCO, PubMed kaynaklarındaki yayınlar incelenmiştir. Literatürdeki genel durumun ortaya konması amacıyla, ruh sağlığı hastalıkları tespitinde makine öğrenmesi ve LIWC yöntemlerinden yararlanan çalışmalar derlenmiştir. Son olarak makine öğrenimi ve LIWC’in çalışma prensipleri incelenip ruh sağlığı hastalıkları alanında yapılan araştırmalar ve bazı çalışmalar tablolaştırılmıştır. Bu çalışmanın, ruh sağlığı hastalıkları tespitinde makine öğrenimi ve Dilbilimsel Sorgulama Kelime Sayımını daha iyi anlamak için özellikle bu iki yöntemi entegre eden veya karşılaştıran daha fazla araştırmaya ihtiyaç olduğundan, araştırmacılara faydalı olabileceği umulmaktadır.Publisher's Versio

Finding the online cry for help : automatic text classification for suicide prevention

Successful prevention of suicide, a serious public health concern worldwide, hinges on the adequate detection of suicide risk. While online platforms are increasingly used for expressing suicidal thoughts, manually monitoring for such signals of distress is practically infeasible, given the information overload suicide prevention workers are confronted with. In this thesis, the automatic detection of suicide-related messages is studied. It presents the first classification-based approach to online suicidality detection, and focuses on Dutch user-generated content. In order to evaluate the viability of such a machine learning approach, we developed a gold standard corpus, consisting of message board and blog posts. These were manually labeled according to a newly developed annotation scheme, grounded in suicide prevention practice. The scheme provides for the annotation of a post's relevance to suicide, and the subject and severity of a suicide threat, if any. This allowed us to derive two tasks: the detection of suicide-related posts, and of severe, high-risk content. In a series of experiments, we sought to determine how well these tasks can be carried out automatically, and which information sources and techniques contribute to classification performance. The experimental results show that both types of messages can be detected with high precision. Therefore, the amount of noise generated by the system is minimal, even on very large datasets, making it usable in a real-world prevention setting. Recall is high for the relevance task, but at around 60%, it is considerably lower for severity. This is mainly attributable to implicit references to suicide, which often go undetected. We found a variety of information sources to be informative for both tasks, including token and character ngram bags-of-words, features based on LSA topic models, polarity lexicons and named entity recognition, and suicide-related terms extracted from a background corpus. To improve classification performance, the models were optimized using feature selection, hyperparameter, or a combination of both. A distributed genetic algorithm approach proved successful in finding good solutions for this complex search problem, and resulted in more robust models. Experiments with cascaded classification of the severity task did not reveal performance benefits over direct classification (in terms of F1-score), but its structure allows the use of slower, memory-based learning algorithms that considerably improved recall. At the end of this thesis, we address a problem typical of user-generated content: noise in the form of misspellings, phonetic transcriptions and other deviations from the linguistic norm. We developed an automatic text normalization system, using a cascaded statistical machine translation approach, and applied it to normalize the data for the suicidality detection tasks. Subsequent experiments revealed that, compared to the original data, normalized data resulted in fewer and more informative features, and improved classification performance. This extrinsic evaluation demonstrates the utility of automatic normalization for suicidality detection, and more generally, text classification on user-generated content

Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT layers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model\u27s robustness and reliability for distinguishing the depression symptoms

Social media mental health analysis framework through applied computational approaches

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

Towards AI-governance in psychosocial care: A systematic literature review analysis

With increased digitalization and e-government services, Artificial Intelligence (AI) gained momentum. This paper focuses on AI-governance in Child Social Care field, exploring how aspects of individual, family/community factors are embedded in organizational level, especially when dealing with children resilience and wellbeing. A three-level based review has been conducted. In the first part we explored the interlink between individual factors associated to either resilience or wellbeing are connected to community and governance level where a new conceptual model is provided. In the second phase, we conducted an in-depth systematic literature review using PRISMA review protocol where new categorizations of identified literature with respect to individual, family and community levels in child social care field were suggested, while in the third phase, a review of relevant AI-initiatives in Europe and USA was performed. Finally, a comprehensive discussion of the literature review outcomes was carried out and a new updated conceptual model was provided.© 2023 The Author(s). Published by Elsevier Ltd on behalf of Prof JinHyo Joseph Yun. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed