1,654 research outputs found

    Learning multimodal representations for drowsiness detection

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    See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data

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    Mental disorders such as depression and anxiety have been increasing at alarming rates in the worldwide population. Notably, the major depressive disorder has become a common problem among higher education students, aggravated, and maybe even occasioned, by the academic pressures they must face. While the reasons for this alarming situation remain unclear (although widely investigated), the student already facing this problem must receive treatment. To that, it is first necessary to screen the symptoms. The traditional way for that is relying on clinical consultations or answering questionnaires. However, nowadays, the data shared at social media is a ubiquitous source that can be used to detect the depression symptoms even when the student is not able to afford or search for professional care. Previous works have already relied on social media data to detect depression on the general population, usually focusing on either posted images or texts or relying on metadata. In this work, we focus on detecting the severity of the depression symptoms in higher education students, by comparing deep learning to feature engineering models induced from both the pictures and their captions posted on Instagram. The experimental results show that students presenting a BDI score higher or equal than 20 can be detected with 0.92 of recall and 0.69 of precision in the best case, reached by a fusion model. Our findings show the potential of large-scale depression screening, which could shed light upon students at-risk.Comment: This article was accepted (15 November 2019) and will appear in the proceedings of ICWSM 202

    Unveiling the Emotional and Psychological States of Instagram Users: A Deep Learning Approach to Mental Health Analysis

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    People can now communicate with others who have common tastes to them and engage in conversation together while furthermore exchanging ideas, photos, and clips that convey their emotional states due to social media’s technology. As a consequence, there is an opportunity to investigate person sentiments and thoughts in social networking sites data in order to understand their viewpoints and sentiments when utilizing these digital platforms for interaction. Utilizing social network data to diagnose depression has gained extensive acceptance, there is still a number of unidentified characteristics. Due to its potential to shed light on the forecasting model, model complexity is crucial for facilitating communication. For example, the majority of algorithms for machine learning produce results in the automatic depression forecasting test that are challenging for people to understand. In this research the mental health condition is analyzed using deep learning approach by considering the data from Instagram data. In this investigation, researchers created the Hybrid deep learning approach, which divided the sentiment ratings into different categories: Neutral, Positive, Negative. Researchers also contrasted the performance of the recommended approach with other machine learning algorithm on a number of criteria, including accuracy, sensitivity, F1 score, and precision

    Examining the Role of Mood Patterns in Predicting Self-reported Depressive Symptoms

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    Depression is the leading cause of disability worldwide. Initial efforts to detect depression signals from social media posts have shown promising results. Given the high internal validity, results from such analyses are potentially beneficial to clinical judgment. The existing models for automatic detection of depressive symptoms learn proxy diagnostic signals from social media data, such as help-seeking behavior for mental health or medication names. However, in reality, individuals with depression typically experience depressed mood, loss of pleasure nearly in all the activities, feeling of worthlessness or guilt, and diminished ability to think. Therefore, a lot of the proxy signals used in these models lack the theoretical underpinnings for depressive symptoms. It is also reported that social media posts from many patients in the clinical setting do not contain these signals. Based on this research gap, we propose to monitor a type of signal that is well-established as a class of symptoms in affective disorders -- mood. The mood is an experience of feeling that can last for hours, days, or even weeks. In this work, we attempt to enrich current technology for detecting symptoms of potential depression by constructing a 'mood profile' for social media users.Comment: Accepted at The Web Science Conference 202

    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

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

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    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
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