Bridianne O'Dea

We Feel: Mapping Emotion on Twitter

Research data on predisposition to mental health problems, and the fluctuations and regulation of emotions, thoughts, and behaviors are traditionally collected through surveys, which cannot provide a real-time insight into the emotional state of individuals or communities. Large datasets such as World Health Organization (WHO) statistics are collected less than once per year, whereas social network platforms, such as Twitter, offer the opportunity for real-time analysis of expressed mood. Such patterns are valuable to the mental health research community, to help understand the periods and locations of greatest demand and unmet need. We describe the “We Feel” system for analyzing global and regional variations in emotional expression, and report the results of validation against known patterns of variation in mood.

E-Health Interventions for Suicide Prevention

2.73 \times 10^{9}

A cross-sectional exploration of the clinical characteristics of disengaged (NEET) young people in primary mental healthcare

emotional tweets were collected over a 12-week period, and automatically annotated for emotion, geographic location, and gender. Principal component analysis (PCA) of the data illustrated a dominant in-phase pattern across all emotions, modulated by antiphase patterns for “positive” and “negative” emotions. The first three principal components accounted for over 90% of the variation in the data. PCA was also used to remove the dominant diurnal and weekly variations allowing identification of significant events within the data, with z-scores showing expression of emotions over 80 standard deviations from the mean. We also correlate emotional expression with WHO data at a national level and although no correlations were observed for the burden of depression, the burden of anxiety and suicide rates appeared to correlate with expression of particular emotions.

Using linguistic and topic analysis to classify sub-groups of online depression communities

Many people at risk of suicide do not seek help before an attempt, and do not remain connected to health services following an attempt. E-health interventions are now being considered as a means to identify at-risk individuals, offer self-help through web interventions or to deliver proactive interventions in response to individuals’ posts on social media. In this article, we examine research studies which focus on these three aspects of suicide and the internet: the use of online screening for suicide, the effectiveness of e-health interventions aimed to manage suicidal thoughts, and newer studies which aim to proactively intervene when individuals at risk of suicide are identified by their social media postings. We conclude that online screening may have a role, although there is a need for additional robust controlled research to establish whether suicide screening can effectively reduce suicide-related outcomes, and in what settings online screening might be most effective. The effectiveness of Internet interventions may be increased if these interventions are designed to specifically target suicidal thoughts, rather than associated conditions such as depression. The evidence for the use of intervention practices using social media is possible, although validity, feasibility and implementation remains highly uncertain.

The use of technology in suicide prevention

Objective Youth with mental health problems often have difficulties engaging in education and employment. In Australia, youth mental health services have been widely established with a key aim of improving role functioning; however, there is little knowledge of those who are not engaged in employment, education or training (NEET) and the factors which may influence this. This study aimed to examine NEET status and its correlates in a sample of such youth. Design Cross-sectional data from a longitudinal cohort study. Setting Between January 2011 and August 2012, young people presenting to one of the four primary mental health centres in Sydney or Melbourne were invited to participate. Participants Young adults (N=696) aged between 15 and 25 years (M=19.0, SD=2.8), 68% female, 58% (n=404) attended headspace in Sydney. Measures Individuals ‘Not in any type of Education, Employment or Training’ in the past month were categorised as NEET. Demographic, psychological and clinical factors alongside disability and functioning were assessed using clinical interview and self-report. Results A total of 19% (n=130/696) were NEET. NEETs were more likely to be male, older, have a history of criminal charges, risky cannabis use, higher level of depression, poorer social functioning, greater disability and economic hardship, and a more advanced stage of mental illness than those engaged in education, training or work. Demographics such as postsecondary education, immigrant background and indigenous background, were not significantly associated with NEET status in this sample. Conclusions One in five young people seeking help for mental health problems were not in any form of education, employment and training. The commonly observed risk factors did not appear to influence this association, instead, behavioural factors such as criminal offending and cannabis use appeared to require targeted intervention.

Is e-health the answer to gaps in adolescent mental health service provision?

Depression is a highly prevalent mental health problem and is a co-morbidity of other mental, physical, and behavioural disorders. The internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these discussions have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A total of 5,000 posts were randomly selected from 24 online communities. Five subgroups of online communities were identified: Depression, Bipolar Disorder, Self-Harm, Grief/Bereavement, and Suicide. Psycholinguistic features and content topics were extracted from the posts and analysed. Machine learning techniques were used to discriminate the online conversations in the depression communities from the other subgroups. Topics and psycholinguistic features were found to be highly valid predictors of community subgroup. Clear discrimination between linguistic features and topics, alongside good predictive power is an important step in understanding social media and its use in mental health.

A Linguistic Analysis of Suicide-Related Twitter Posts

Suicide is one of the leading causes of death globally, and is notably a significant cause of death amongst young people. A suicide outcome is a complex combination of personal, social, and health factors, and therefore suicide prevention is a challenge, requiring a systems approach incorporating public health strategies, screening at-risk individuals, targeted interventions, and follow-up for suicide survivors and those bereaved by suicide. Engineering practice has been implicated in the hindrance of the adoption of suicide prevention strategies, such as installing safety barriers at the Golden Gate Bridge, however technological developments offer new opportunities in suicide prevention, and the potential to reduce the number of deaths by suicide. We present an overview of current technological developments which are facilitating research in the field of suicide prevention, including multiple modes of screening such as network analysis of mobile-phone collected connectivity data, automatic detection of suicidality from social media content, and crisis detection from acoustic variability in speech patterns. The current field of mhealth apps for suicide prevention is assessed, and an innovative app for an Indigenous population is presented. From this overview, future challenges - technical and ethical - are discussed.

Attitudes towards suicide attempts broadcast on social media: an exploratory study of Chinese microblogs

Purpose of review Depression and anxiety are prevalent among adolescents; however, many young people do not seek help from professional services. This is due, in part, to the inadequacies of existing healthcare systems. This article aims to review the current evidence for e-health interventions for depression and anxiety in youth, as a potential solution to the gaps in mental health service provision. Recent findings Five randomized controlled trials reporting on e-health interventions for youth depression or anxiety were identified. Of these, two trials focused exclusively on anxiety symptoms, and three trials examined both anxiety and depression. The majority of trials assessed online cognitive behavioral therapy and focused on prevention rather than treatment. In all but one trial, results demonstrated positive effects for the e-health interventions, relative to the control. Summary There is growing evidence for the effectiveness of online cognitive behaviour therapy interventions for reducing the level of anxiety and depressive symptoms in adolescents aged between 12 and 18 years, when delivered in school and clinical settings, with some level of supervision. However, there are a number of gaps in the literature. More research is needed to strengthen the evidence base for prevention and treatment programs that are delivered via the internet, particularly for depression.

Internet Forums for Suicide Bereavement

Background: Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not. Aims: This study aims to characterize the linguistic profiles of suicide-related Twitter posts. Method: Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regression analyses were conducted to determine differences in linguistic profiles. Results: When compared with matched non-suicide-related Twitter posts, strongly concerning suicide-related posts were characterized by a higher word count, increased use of first-person pronouns, and more references to death. When compared with safe-to-ignore suicide-related posts, strongly concerning suicide-related posts were characterized by increased use of first-person pronouns, greater anger, and increased focus on the present. Other differences were found. Limitations: The predictive validity of the identified features needs further testing before these results can be used for interventional purposes. Conclusion: This study demonstrates that strongly concerning suicide-related Twitter posts have unique linguistic profiles. The examination of Twitter data for the presence of such features may help to validate online risk assessments and determine those in need of further support or intervention.

General Practitioners’ Attitudes Toward a Web-Based Mental Health Service for Adolescents: Implications for Service Design and Delivery

Introduction. Broadcasting a suicide attempt on social media has become a public health concern in many countries, particularly in China. In these cases, social media users are likely to be the first to witness the suicide attempt, and their attitudes may determine their likelihood of joining rescue efforts. This paper examines Chinese social media (Weibo) users’ attitudes towards suicide attempts broadcast on Weibo. Methods. A total of 4,969 Weibo posts were selected from a customised Weibo User Pool which consisted of 1.06 million active users. The selected posts were then independently coded by two researchers using a coding framework that assessed: (a) Themes, (b) General attitudes, (c) Stigmatising attitudes, (d) Perceived motivations, and (e) Desired responses. Results and Discussion. More than one third of Weibo posts were coded as “stigmatising” (35%). Among these, 22%, 16%, and 15% of posts were coded as “deceitful,” “pathetic,” and “stupid,” respectively. Among the posts which reflected different types of perceived motivations, 57% of posts were coded as “seeking attention.” Among the posts which reflected desired responses, 37% were “not saving” and 28% were “encouraging suicide.” Furthermore, among the posts with negative desired responses (i.e., “not saving” and “encouraging suicide”), 57% and 17% of them were related to different types of stigmatising attitudes and perceived motivations, respectively. Specifically, 29% and 26% of posts reflecting both stigmatising attitudes and negative desired responses were coded as “deceitful” and “pathetic,” respectively, while 66% of posts reflecting both perceived motivations, and negative desired responses were coded as “seeking attention.” Very few posts “promoted literacy” (2%) or “provided resources” (8%). Gender differences existed in multiple categories. Conclusions. This paper confirms the need for stigma reduction campaigns for Chinese social media users to improve their attitudes towards those who broadcast their suicide attempts on social media. Results of this study support the need for improved public health programs in China and may be insightful for other countries and other social media platforms.