A Survey on Mental Health Detection in Online Social Network

Mental health detection in Online Social Network (OSN) is widely studied in the recent years. OSN has encouraged new ways to communicate and share information, and it is used regularly by millions of people. It generates a mass amount of information that can be utilised to develop mental health detection. The rich content provided by OSN should not be overlooked as it could give more value to the data explored by the researcher. The main purpose of this study is to extract and scrutinise related works from related literature on detection of mental health using OSN. With the focus on the method used, machine learning algorithm, sources of OSN, and types of language used for the mental health detection were chosen for the study. The basic design of this study is in the form of a survey from the literature related to current research in mental health. Major findings revealed that the most frequently used method in mental health detection is machine learning techniques, with Support Vector Machine (SVM) as the most chosen algorithm. Meanwhile, Twitter is the major data source from OSN with English language used for mental health detection. The researcher found a few challenges from the previous studies and analyses, and these include limitations in language barrier, account privacy in OSN, single type of OSN, text analysis, and limited features selection. Based on the limitations, the researcher outlined a future direction of mental health detection using language based on user’s geo-location and mother tongue. The use of pictorial, audio and video formats in OSN could become one of the potential areas to be explored in future research. Extracting data from multiple sources of OSNs with new features selection will probably improve mental health detection in the future. In conclusion, this research has a big potential to be explored further in the future

e-Mental health for mood and anxiety disorders in general practice

Familiarises general practitioners (GPs) with the range of online programs in Australia that have demonstrated efficacy and are currently available for use by patients with mental health problems. Background Australia is a world leader in the development of internetdelivered programs for the prevention and management of mood and anxiety disorders. Despite a strong evidence base of time- and cost-effectiveness, as well as clinical efficacy, the uptake of these programs in general practice remains low. Objective To familiarise general practitioners (GPs) with the range of online programs in Australia that have demonstrated efficacy and are currently available for use by patients with mental health problems. Discussion E-mental health programs provide an efficacious and accessible form of mental healthcare and have the potential to fill the gap for those for whom such care is inaccessible, unaffordable or unacceptable. Clinicians can also use it in a stepped-care manner to augment existing healthcare services. There are a number of online resources currently available to Australians who have mood or anxiety disorders. These resources have strong evidence to support their effectiveness. Online portals facilitate access to these programs. Recently the Australian Federal Government has funded an education program (eMHPrac) for GPs and mental health professionals, to outline what is available, indicate situations where recommending such resources is appropriate, and suggest ways in which they can be incorporated into general practice

Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table

Detecting and Explaining Crisis

Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation.Comment: Accepted at CLPsych, ACL workshop. 8 pages, 5 figure

The effect of a therapeutic smartphone application on suicidal ideation in young adults : findings from a randomized controlled trial in Australia

Background: Suicidal ideation is a major risk for a suicide attempt in younger people, such that reducing severity of ideation is an important target for suicide prevention. Smartphone applications present a new opportunity for managing ideation in young adults; however, confirmatory evidence for efficacy from randomized trials is lacking. The objective of this study was to assess whether a therapeutic smartphone application (“LifeBuoy”) was superior to an attention-matched control application at reducing the severity of suicidal ideation. Methods and findings: In this 2-arm parallel, double-blind, randomized controlled trial, 455 young adults from Australia experiencing recent suicidal ideation and aged 18 to 25 years were randomly assigned in a 2:2 ratio to use a smartphone application for 6 weeks in May 2020, with the final follow-up in October 2020. The primary outcome was change in suicidal ideation symptom severity scores from baseline (T0) to postintervention (T1) and 3-month postintervention follow-up (T2), measured using the Suicidal Ideation Attributes Scale (SIDAS). Secondary outcomes were symptom changes in depression (Patient Health Questionnaire-9, PHQ-9), generalized anxiety (Generalized Anxiety Disorder-7, GAD-7), distress (Distress Questionnaire-5, DQ5), and well-being (Short Warwick–Edinburgh Mental Well-Being Scale, SWEMWBS). This trial was conducted online, using a targeted social media recruitment strategy. The intervention groups were provided with a self-guided smartphone application based on dialectical behavior therapy (DBT; “LifeBuoy”) to improve emotion regulation and distress tolerance. The control group were provided a smartphone application that looked like LifeBuoy (“LifeBuoy-C”), but delivered general (nontherapeutic) information on a range of health and lifestyle topics. Among 228 participants randomized to LifeBuoy, 110 did not complete the final survey; among 227 participants randomized to the control condition, 91 did not complete the final survey. All randomized participants were included in the intent-to-treat analysis for the primary and secondary outcomes. There was a significant time × condition effect for suicidal ideation scores in favor of LifeBuoy at T1 (p < 0.001, d = 0.45) and T2 (p = 0.007, d = 0.34). There were no superior intervention effects for LifeBuoy on any secondary mental health outcomes from baseline to T1 or T2 [p-values: 0.069 to 0.896]. No serious adverse events (suicide attempts requiring medical care) were reported. The main limitations of the study are the lack of sample size calculations supporting the study to be powered to detect changes in secondary outcomes and a high attrition rate at T2, which may lead efficacy to be overestimated. Conclusions: LifeBuoy was associated with superior improvements in suicidal ideation severity, but not secondary mental health outcomes, compared to the control application, LifeBuoy-C. Digital therapeutics may need to be purposefully designed to target a specific health outcome to have efficacy. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12619001671156

Effectiveness of Self-guided Tailored Implementation Strategies in Integrating and Embedding Internet-Based Cognitive Behavioral Therapy in Routine Mental Health Care: Results of a Multicenter Stepped-Wedge Cluster Randomized Trial

BACKGROUND: Internet-based cognitive behavioral therapy (iCBT) services for common mental health disorders have been found to be effective. There is a need for strategies that improve implementation in routine practice. One-size-fits-all strategies are likely to be ineffective. Tailored implementation is considered as a promising approach. The self-guided integrated theory-based Framework for intervention tailoring strategies toolkit (ItFits-toolkit) supports local implementers in developing tailored implementation strategies. Tailoring involves identifying local barriers; matching selected barriers to implementation strategies; developing an actionable work plan; and applying, monitoring, and adapting where necessary. OBJECTIVE: This study aimed to compare the effectiveness of the ItFits-toolkit with implementation-as-usual (IAU) in implementing iCBT services in 12 routine mental health care organizations in 9 countries in Europe and Australia. METHODS: A stepped-wedge cluster randomized trial design with repeated measures was applied. The trial period lasted 30 months. The primary outcome was the normalization of iCBT delivery by service providers (therapists, referrers, IT developers, and administrators), which was measured with the Normalization Measure Development as a proxy for implementation success. A 3-level linear mixed-effects modeling was applied to estimate the effects. iCBT service uptake (referral and treatment completion rates) and implementation effort (hours) were used as secondary outcomes. The perceived satisfaction (Client Satisfaction Questionnaire), usability (System Usability Scale), and impact of the ItFits-toolkit by implementers were used to assess the acceptability of the ItFits-toolkit. RESULTS: In total, 456 mental health service providers were included in this study. Compared with IAU, the ItFits-toolkit had a small positive statistically significant effect on normalization levels in service providers (mean 0.09, SD 0.04; P=.02; Cohen d=0.12). The uptake of iCBT by patients was similar to that of IAU. Implementers did not spend more time on implementation work when using the ItFits-toolkit and generally regarded the ItFits-toolkit as usable and were satisfied with it. CONCLUSIONS: The ItFits-toolkit performed better than the usual implementation activities in implementing iCBT services in routine practice. There is practical utility in the ItFits-toolkit for supporting implementers in developing and applying effective tailored implementation strategies. However, the effect on normalization levels among mental health service providers was small. These findings warrant modesty regarding the effectiveness of self-guided tailored implementation of iCBT services in routine practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT03652883; https://clinicaltrials.gov/ct2/show/NCT03652883. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-020-04686-4

A framework for classifying online mental health related communities with an interest in depression

Mental illness has a deep impact on individuals, families, and by extension, society as a whole. Social networks allow individuals with mental disorders to communicate with others sufferers via online communities, providing an invaluable resource for studies on textual signs of psychological health problems. Mental disorders often occur in combinations, e.g., a patient with an anxiety disorder may also develop depression. This co-occurring mental health condition provides the focus for our work on classifying online communities with an interest in depression. For this, we have crawled a large body of 620,000 posts made by 80,000 users in 247 online communities. We have extracted the topics and psycho-linguistic features expressed in the posts, using these as inputs to our model. Following a machine learning technique, we have formulated a joint modelling framework in order to classify mental health-related co-occurring online communities from these features. Finally, we performed empirical validation of the model on the crawled dataset where our model outperforms recent state-of-the-art baselines