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

Computers (Basel)

Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.CC999999/ImCDC/Intramural CDC HHSUnited States

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

Suicidal behaviour in young people accessing mental health care

Rates of suicidal behaviour in young people have increased despite increased awareness and investment in suicide prevention. Mental health system reform has led to the development of early intervention mental health services and new clinical cohorts distinct from adults accessing mental health services. The effectiveness of clinical suicide prevention relies on in-depth, contemporaneous understanding of suicidal behaviour in clinical populations. The overall aim of this thesis was to generate new scientific understanding of suicidal behaviour in these new clinical cohorts, which may be applied in clinical services to increase the impact of clinical suicide prevention. Chapter 2 uses systematic review and meta-analyses to assess the predictive performance of key risk factors used to direct clinical suicide prevention. Chapter 3 then considers the application of machine learning to the prediction of suicidal behaviour. Chapter 4 explores the utility of cognition as a potential biomarker of suicidal behaviour in young people. Finally, Chapter 5 uses data-linkage to characterise premature mortality and identify health service use predictors of suicide and accident-related deaths in young people accessing early intervention youth mental health services. In summary, there are unique social, clinical and cognitive predictors of suicidal behaviour in young people accessing clinical services. The high burden of disease associated with these behaviours suggests that all young people accessing mental health services should receive evidence-based suicide prevention interventions. Further, the high rates of adverse outcomes due to accident and injury, drug and alcohol use and emergency department presentations, suggest that developing strategies and designing interventions to address a broader construct of harm should be considered

Designing a Patient-Centered Clinical Workflow to Assess Cyberbully Experiences of Youths in the U.S. Healthcare System

Cyberbullying or online harassment is often defined as when someone repeatedly and intentionally harasses, mistreats, or makes fun of others aiming to scare, anger or shame them using electronic devices [296]. Youths experiencing cyberbullying report higher levels of anxiety and depression, mental distress, suicide thoughts, and substance abuse than their non-bullied peers [360, 605, 261, 354]. Even though bullying is associated with significant health problems, to date, very little youth anti-bullying efforts are initiated and directed in clinical settings. There is presently no standardized procedure or workflow across health systems for systematically assessing cyberbullying or other equally dangerous online activities among vulnerable groups like children or adolescents [599]. Therefore, I developed a series of research projects to link digital indicators of cyberbullying or online harassment to clinical practices by advocating design considerations for a patient-centered clinical assessment and workflow that addresses patients’ needs and expectations to ensure quality care. Through this dissertation, I aim to answer these high-level research questions:RQ1. How does the presence of severe online harassment on online platforms contribute to negative experiences and risky behaviors within vulnerable populations? RQ2. How efficient is the current mechanism of screening these risky online negative experiences and behaviors, specifically related to cyberbully, within at-risk populations like adolescent in clinical settings? RQ3. How might evidence of activities and negative harassing experiences on online platforms best be integrated into electronic health records during clinical treatment? I first explore how harassment is presented within different social media platforms from diverse contexts and cultural norms (study 1,2, and 3); next, by analyzing actual patient data, I address current limitations in the screening process in clinical settings that fail to efficiently address core aspect of cyberbullying and their consequences within adolescent patients (study 4 and 5); finally, connecting all my findings, I recommend specific design guidelines for a refined screening tool and structured processes for implementation and integration of the screened data into patients’ electronic health records (EHRs) for better patient assessment and treatment outcomes around cyberbully within adolescent patients (study 6)

Impact of adherence to media reporting guidelines in suicide news-focused Instagram posts by Malaysian news media organisations - A study on user responses

Adherence to media reporting guidelines on suicide news reporting has been found to be an effective population-level prevention strategy for suicidal behaviours. Nevertheless, media professionals encounter several challenges when attempting to implement these guidelines in their work. These challenges can be attributed to the limited research available on how to effectively integrate these guidelines into journalistic practices, and the absence of compelling evidence supporting their efficacy in reducing population-level suicidal behaviours. This need for more research on the significance of adhering to media reporting guidelines is particularly relevant in the context of social media and within Southeast Asian countries. To address this gap, the present study aimed to examine the nature of suicide news reporting on Instagram by Malaysian news media organisations and to explore the user sentiments expressed by audiences in relation to such content. The study conducted a series of content analyses and sentiment analyses on the posts and comments associated with ten Instagram accounts officially affiliated with prominent Malaysian news media organizations. The findings of the study indicate that within these accounts, there is a greater prevalence of suicide news-focused Instagram posts that generally adhere to media reporting guidelines compared to those that did not adhere to these guidelines. Furthermore, the analysis revealed various distinguishing features between posts that adhered strongly to media reporting guidelines and those that adhered less rigorously. Additionally, the analysis on audience sentiments within the comments found support for the importance of adherence to media reporting guidelines by Malaysian news media organizations when covering suicide news-related topics. This study delves into the nature of adherence to media reporting guidelines within suicide

Cyberbullying in educational context

Kustenmacher and Seiwert (2004) explain a man’s inclination to resort to technology in his interaction with the environment and society. Thus, the solution to the negative consequences of Cyberbullying in a technologically dominated society is represented by technology as part of the technological paradox (Tugui, 2009), in which man has a dual role, both slave and master, in the interaction with it. In this respect, it is noted that, notably after 2010, there have been many attempts to involve artificial intelligence (AI) to recognize, identify, limit or avoid the manifestation of aggressive behaviours of the CBB type. For an overview of the use of artificial intelligence in solving various problems related to CBB, we extracted works from the Scopus database that respond to the criterion of the existence of the words “cyberbullying” and “artificial intelligence” in the Title, Keywords and Abstract. These articles were the subject of the content analysis of the title and, subsequently, only those that are identified as a solution in the process of recognizing, identifying, limiting or avoiding the manifestation of CBB were kept in the following Table where we have these data synthesized and organized by years