Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach

Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk" words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers.

Infodemiology and Infoveillance: Scoping Review

BACKGROUND

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

OBJECTIVE

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

RESULTS

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

CONCLUSIONS

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Text mining analysis of teachers' reports on student suicide in South Korea

A teacher as a suicide prevention gatekeeper has an important role in identifying suicide risks and warning signs in students. After a student's suicide, teachers in Korea have to write a student suicide case report based on their direct and indirect observations. In particular, the section 'characteristic of student suicide' of this report contains valuable information about the suicide; however, it is unstructured, and thus cannot be analyzed using conventional statistical methods. We aimed to identify the characteristics of observed Korean students, who have committed suicide, using text mining techniques as well as to improve our understanding of suicidal behaviors in the school contexts. Therefore, a series of text mining techniques: topic analysis, word correlation, and word frequency analysis, in three problem categories: health, school, and family problems, were used to analyze the characteristics of student suicides. Topic analysis showed that only 30% of the student suicide case reports identified problematic student characteristics related to suicide. Correlations between words showed that words in one problem category were often correlated with words in other problem categories. Frequency word analysis showed that the three problem categories varied across gender and school levels. These results provide interesting insights into the characteristics of suicides among Korean students and important implications for suicide intervention in the education field.

Methods in predictive techniques for mental health status on social media: a critical review

Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space.

A philosophy of health: life as reality, health as a universal value

Emphases on biomarkers (e.g. when making diagnoses) and pharmaceutical/drug methods (e.g. when researching/disseminating population level interventions) in primary care evidence philosophies of health (and healthcare) that reduce health to the biological level. However, with chronic diseases being responsible for the majority of all cause deaths and being strongly linked to health behavior and lifestyle; predominantly biological views are becoming increasingly insufficient when discussing this health crisis. A philosophy that integrates biological, behavioral, and social determinants of health could benefit multidisciplinary discussions of healthy publics. This manuscript introduces a Philosophy of Health by presenting its first five principles of health. The philosophy creates parallels among biological immunity, health behavior change, social change by proposing that two general functions-precision and variation-impact population health at biological, behavioral, and social levels. This higher-level of abstraction is used to conclude that integrating functions, rather than separated (biological) structures drive healthy publics. A Philosophy of Health provides a framework that can integrate existing theories, models, concepts, and constructs.

Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm

The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper.

Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data

Background

Affective states, including sentiment and emotion, are critical determinants of health. However, few studies among men who have sex with men (MSM) have examined sentiment and emotion specifically using real-time social media technologies. Moreover, the explorations on their associations with sexual and health status among MSM are limited.

Objective

This study aimed to understand and examine the associations of affective states with sexual behaviors and health status among MSM using public data from the Blued (Blued International Inc) app.

Methods

A total of 843,745 public postings of 377,610 MSM users located in Guangdong were saved from the Blued app by automatic screen capture. Positive affect, negative affect, sexual behaviors, and health status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust, were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotion score were also calculated. Univariate and multivariate linear regression models on the basis of a permutation test were used to assess the associations of affective states with sexual behaviors and health status.

Results

A total of 5871 active MSM users and their 477,374 postings were finally selected. Both positive affect and positive emotions (eg, joy) peaked between 7 AM and 9 AM. Negative affect and negative emotions (eg, sadness and disgust) peaked between 2 AM and 4 AM. During that time, 25.1% (97/387) of negative postings were related to health and 13.4% (52/387) of negative postings were related to seeking social support. A multivariate analysis showed that the MSM who were more likely to post sexual behaviors were more likely to express positive affect (beta=0.3107; P<.001) and positive emotions (joy: beta=0.027; P<.001), as well as negative emotions (sadness: beta=0.0443; P<.001 and disgust: beta=0.0256; P<.001). They also had a higher positive sentiment score (beta=0.2947; P<.001) and a higher positive emotion score (beta=0.1612; P<.001). The MSM who were more likely to post their health status were more likely to express negative affect (beta=0.8088; P<.001) and negative emotions, including sadness (beta=0.0705; P<.001), anger (beta=0.0058; P<.001), fear (beta=0.0052; P<.001), and disgust (beta=0.3065; P<.001), and less likely to express positive affect (beta=−0.0224; P=.02). In addition, they had a lower positive sentiment score (beta=−0.8306; P<.001) and a lower positive emotion score (beta=−0.3743; P<.001).

Conclusions

The MSM social media community mainly expressed their positive affect in the early morning and negative affect after midnight. Positive affective states were associated with being sexually active, whereas negative affective states were associated with health problems, mostly about mental health. Our finding suggests the potential to deliver different health-related intervention strategies (eg, psychological counseling and safe sex promotion) on a social media app according to the affective states of MSM in real time.

Mapping the rise of digital mental health technologies: Emerging issues for law and society

The use of digital technologies in mental health initiatives is expanding, leading to calls for clearer legal and regulatory frameworks. However, gaps in knowledge about the scale and nature of change impede efforts to develop responsible public governance in the early stages of what may be the mass uptake of 'digital mental health technologies'. This article maps established and emerging technologies in the mental health context with an eye to locating major socio-legal issues. The paper discusses various types of technology, including those designed for information sharing, communication, clinical decision support, 'digital therapies', patient and/or population monitoring and control, bio-informatics and personalised medicine, and service user health informatics. The discussion is organised around domains of use based on the actors who use the technologies, and those on whom they are used. These actors go beyond mental health service users and practitioners/service providers, and include health and social system or resource managers, data management services, private companies that collect personal data (such as major technology corporations and data brokers), and multiple government agencies and private sector actors across diverse fields of criminal justice, education, and so on. The mapping exercise offers a starting point to better identify cross-cutting legal, ethical and social issues at the convergence of digital technology and contemporary mental health practice.

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

BACKGROUND

Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups.

OBJECTIVE

The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression.

METHODS

The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models).

RESULTS

On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960).

CONCLUSIONS

This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

Crime and social media

Purpose

The purpose of this paper is to complement the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012.

Design/methodology/approach

The empirical evidence is based on ordinary least squares (OLS), Tobit and quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the data set is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa (MENA), Sub-Saharan Africa and Latin America.

Findings

From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime quantile. Further, when the data set is decomposed into regions and income levels, the negative relationship is evident in the MENA while a positive relationship is confirmed for Sub-Saharan Africa. Policy implications are discussed.

Originality/value

Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available data set on Facebook.

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

OBJECTIVE

Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within 'big data' to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management.

METHODS

This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance.

RESULTS

At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports.

CONCLUSION

Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

Distress, Suicidality, and Affective Disorders at the Time of Social Networks

PURPOSE OF REVIEW

We reviewed how scholars recently addressed the complex relationship that binds distress, affective disorders, and suicidal behaviors on the one hand and social networking on the other. We considered the latest machine learning performances in detecting affective-related outcomes from social media data, and reviewed understandings of how, why, and with what consequences distressed individuals use social network sites. Finally, we examined how these insights may concretely instantiate on the individual level with a qualitative case series.

RECENT FINDINGS

Machine learning classifiers are progressively stabilizing with moderate to high performances in detecting affective-related diagnosis, symptoms, and risks from social media linguistic markers. Qualitatively, such markers appear to translate ambivalent and socially constrained motivations such as self-disclosure, passive support seeking, and connectedness reinforcement. Binding data science and psychosocial research appears as the unique condition to ground a translational web-clinic for treating and preventing affective-related issues on social media.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

"I will kill myself" - The series of posts in Facebook and unnoticed departure of a life

Social media has connected the world and transformed the concept of social interaction and traditional communication. However, the concern is rising as people started using the platform for sharing fake news, violent intentions and activities including suicide and homicide. In this paper, I report the case of a media worker in Bangladesh who committed suicide after sharing series of Facebook posts mentioning her intensions to commit suicide. Thousands of her Facebook friends and followers noticed the posts; few of them shared the posts and made critical comments. This case documented the use of Facebook to disclose the intention of committing suicide in Bangladesh. Though Facebook is gaining attention as a potential source to predict and provide timely intervention to prevent suicide, this case raised the question about the effective participation of Facebook users in such programs. We need to emphasis and focus on ensuring active participation of most of Facebook users in any social media based suicide prevention program. This report may inspire future researchers to find out more user friendly and participatory social media focused suicide prevention programs.

Towards Scalable, Integrative Assessment of Children's Self-Regulatory Capabilities: New Applications of Digital Technology

The assessment of self-regulation in children is of significant interest to researchers within education, clinical and developmental psychology, and clinical neuroscience, given its importance to adaptive functioning across a wide range of social, educational, interpersonal, educational and health domains. Because self-regulation is a complex, multidimensional construct, a range of assessment approaches have been developed to assess its various components including behavioural, cognitive and emotional domains. In recent years, digital technology has been increasingly used to enhance or supplement existing measurement approaches; however, developments have predominantly focused on translating traditional testing paradigms into digital formats. There is a need for more innovation in digital psychological assessments that harness modern mechanisms such as game-based design and interactivity. Such approaches have potential for the development of scalable, adaptable universal approaches to screening and assessment of children's self-regulatory capabilities, to facilitate early identification of difficulties in individuals and also guide planning and decision-making at a population level. We highlight a novel, innovative digital assessment tool for children called Rumble's Quest, a new measure of children's socio-emotional functioning that shows promise as an integrative assessment of well-being and self-regulation, and which incorporates both self-report and direct assessment of cognitive self-regulation. This tool is scalable, can be integrated into normal classroom activities, and forms part of a comprehensive prevention support system that can be used to guide stakeholders' decision-making regarding early intervention and support at the individual, classroom, school and community level. We finish by discussing other innovative possibilities for psychological assessment with children, using new and emerging technologies and assessment approaches.

A systematic literature review of machine learning in online personal health data

OBJECTIVE

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

MATERIALS AND METHODS

We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.

RESULTS

We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support.

CONCLUSIONS

The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors

Background

Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and behaviors to seek help.

Objective

The goal of the research was to test the feasibility and acceptability of Proactive Suicide Prevention Online (PSPO), a new approach based on social media that combines proactive identification of suicide-prone individuals with specialized crisis management.

Methods

We first located a microblog group online. Their comments on a suicide note were analyzed by experts to provide a training set for the machine learning models for suicide identification. The best-performing model was used to automatically identify posts that suggested suicidal thoughts and behaviors. Next, a microblog direct message containing crisis management information, including measures that covered suicide-related issues, depression, help-seeking behavior and an acceptability test, was sent to users who had been identified by the model to be at risk of suicide. For those who replied to the message, trained counselors provided tailored crisis management. The Simplified Chinese Linguistic Inquiry and Word Count was also used to analyze the users’ psycholinguistic texts in 1-month time slots prior to and postconsultation.

Results

A total of 27,007 comments made in April 2017 were analyzed. Among these, 2786 (10.32%) were classified as indicative of suicidal thoughts and behaviors. The performance of the detection model was good, with high precision (.86), recall (.78), F-measure (.86), and accuracy (.88). Between July 3, 2017, and July 3, 2018, we sent out a total of 24,727 direct messages to 12,486 social media users, and 5542 (44.39%) responded. Over one-third of the users who were contacted completed the questionnaires included in the direct message. Of the valid responses, 89.73% (1259/1403) reported suicidal ideation, but more than half (725/1403, 51.67%) reported that they had not sought help. The 9-Item Patient Health Questionnaire (PHQ-9) mean score was 17.40 (SD 5.98). More than two-thirds of the participants (968/1403, 69.00%) thought the PSPO approach was acceptable. Moreover, 2321 users replied to the direct message. In a comparison of the frequency of word usage in their microblog posts 1-month before and after the consultation, we found that the frequency of death-oriented words significantly declined while the frequency of future-oriented words significantly increased.

Conclusions

The PSPO model is suitable for identifying populations that are at risk of suicide. When followed up with proactive crisis management, it may be a useful supplement to existing prevention programs because it has the potential to increase the accessibility of antisuicide information to people with suicidal thoughts and behaviors but a low motivation to seek help.

Mining social networks to improve suicide prevention: A scoping review

Attention about the risks of online social networks (SNs) has been called upon reports describing their use to express emotional distress and suicidal ideation or plans. On the Internet, cyberbullying, suicide pacts, Internet addiction, and "extreme" communities seem to increase suicidal behavior (SB). In this study, the scientific literature about SBs and SNs was narratively reviewed. Some authors focus on detecting at-risk populations through data mining, identification of risks factors, and web activity patterns. Others describe prevention practices on the Internet, such as websites, screening, and applications. Targeted interventions through SNs are also contemplated when suicidal ideation is present. Multiple predictive models should be defined, implemented, tested, and combined in order to deal with the risk of SB through an effective decision support system. This endeavor might require a reorganization of care for SNs users presenting suicidal ideation.

The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review

BACKGROUND

Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thoughts and behaviors (SITBs).

METHOD

We systematically searched PsycINFO, PsycARTICLES, ERIC, CINAHL, and MEDLINE for articles published through February 2018.

RESULTS

Thirty-five articles met criteria to be included in the review. Included articles were reviewed by outcome: suicide death, suicide attempt, suicide plan, suicidal ideation, suicide risk, and non-suicidal self-injury. We observed three general aims in the use of SITB-focused machine learning analyses: (1) improving prediction accuracy, (2) identifying important model indicators (i.e., variable selection) and indicator interactions, and (3) modeling underlying subgroups. For studies with the aim of boosting predictive accuracy, we observed greater prediction accuracy of SITBs than in previous studies using traditional statistical methods. Studies using machine learning for variable selection purposes have both replicated findings of well-known SITB risk factors and identified novel variables that may augment model performance. Finally, some of these studies have allowed for subgroup identification, which in turn has helped to inform clinical cutoffs.

LIMITATIONS

Limitations of the current review include relatively low paper sample size, inconsistent reporting procedures resulting in an inability to compare model accuracy across studies, and lack of model validation on external samples.

CONCLUSIONS

We concluded that leveraging machine learning techniques to further predictive accuracy and identify novel indicators will aid in the prediction and prevention of suicide.

Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency

BACKGROUND

Exocrine pancreatic insufficiency (EPI) is a serious condition characterized by a lack of functional exocrine pancreatic enzymes and the resultant inability to properly digest nutrients. EPI can be caused by a variety of disorders, including chronic pancreatitis, pancreatic cancer, and celiac disease. EPI remains underdiagnosed because of the nonspecific nature of clinical symptoms, lack of an ideal diagnostic test, and the inability to easily identify affected patients using administrative claims data.

OBJECTIVES

To develop a machine learning model that identifies patients in a commercial medical claims database who likely have EPI but are undiagnosed.

METHODS

A machine learning algorithm was developed in Scikit-learn, a Python module. The study population, selected from the 2014 Truven MarketScan® Commercial Claims Database, consisted of patients with EPI-prone conditions. Patients were labeled with 290 condition category flags and split into actual positive EPI cases, actual negative EPI cases, and unlabeled cases. The study population was then randomly divided into a training subset and a testing subset. The training subset was used to determine the performance metrics of 27 models and to select the highest performing model, and the testing subset was used to evaluate performance of the best machine learning model.

RESULTS

The study population consisted of 2088 actual positive EPI cases, 1077 actual negative EPI cases, and 437 530 unlabeled cases. In the best performing model, the precision, recall, and accuracy were 0.91, 0.80, and 0.86, respectively. The best-performing model estimated that the number of patients likely to have EPI was about 12 times the number of patients directly identified as EPI-positive through a claims analysis in the study population. The most important features in assigning EPI probability were the presence or absence of diagnosis codes related to pancreatic and digestive conditions.

CONCLUSIONS

Machine learning techniques demonstrated high predictive power in identifying patients with EPI and could facilitate an enhanced understanding of its etiology and help to identify patients for possible diagnosis and treatment.

Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

People with suicidal ideation (PSI) are increasingly using social media to express suicidal feelings. Researchers have found that their internet-based communication may lead to the spread of suicidal ideation, which presents a set of challenges for suicide prevention. To develop effective prevention and intervention strategies that can be efficiently applied in online communities, we need to understand the behavior of PSI in internet-based communities. However, to date there have been no studies that specifically focus on the behavior of PSI in Chinese online communities. A total of 4489 postings in which users explicitly expressed their suicidal ideation were labeled from 560,000 postings in an internet-based suicidal community on Weibo (one of the biggest social media platforms in China) to explore their behavior. The results reveal that PSI are significantly more active than other users in the community. With the use of social network analysis, we also found that the more frequently users communicate with PSI, the more likely that users would become suicidal. In addition, Chinese women may be more likely to be at risk of suicide than men in the community. This study enriches our knowledge of PSI’s behavior in online communities, which may contribute to detecting and assisting PSI on social media.

Looking to the Future: A Synthesis of New Developments and Challenges in Suicide Research and Prevention

Suicide and attempted suicide are major public health concerns. In recent decades, there have been many welcome developments in understanding and preventing suicide, as well as good progress in intervening with those who have attempted suicide. Despite these developments, though, considerable challenges remain. In this article, we explore both the recent developments and the challenges ahead for the field of suicide research and prevention. To do so, we consulted 32 experts from 12 countries spanning four continents who had contributed to the International Handbook of Suicide Prevention (2nd edition). All contributors nominated, in their view, (i) the top 3 most exciting new developments in suicide research and prevention in recent years, and (ii) the top 3 challenges. We have synthesized their suggestions into new developments and challenges in research and practice, giving due attention to implications for psychosocial interventions. This Perspective article is not a review of the literature, although we did draw from the suicide research literature to obtain evidence to elucidate the responses from the contributors. Key new developments and challenges include: employing novel techniques to improve the prediction of suicidal behavior; testing and applying theoretical models of suicidal behavior; harnessing new technologies to monitor and intervene in suicide risk; expanding suicide prevention activities to low and middle-income countries; moving toward a more refined understanding of sub-groups of people at risk and developing tailored interventions. We also discuss the importance of multidisciplinary working and the challenges of implementing interventions in practice.

Social Media and Suicide: A Review of Technology-Based Epidemiology and Risk Assessment

Introduction: Suicide is a significant public health problem among teenagers and young adults in the United States, placing significant stress on emergency departments (EDs) to effectively screen and assess for the presence of suicidality in a rapid yet efficient manner. Methods: A literature search was performed using PubMed and MEDLINE with the following terms: "Social media," "Suicide," "Facebook^®," "Twitter^®," "MySpace^®," "Snapchat^®," "Ethics," "Digital Media," and "Forums and Blog." Data were extracted from each article, specifically the sample size, study setting, and design. Only English-language studies were included. We reviewed the reference lists of included articles for additional studies, as well. Abstracts, unpublished data, and duplicate articles were excluded. Results: A total of 363 articles met our initial criteria. Studies older than 10 years and/or in a language other than English were removed. After review, a total of 31 peer-reviewed articles were included in the study. Teenagers and young adults often fail to disclose risk factors to physicians, despite sharing them with the public on social media platforms such as Facebook and Twitter. Therefore, physician access to a patient's social media can assist in identifying suicidal ideation and/or acts. Conclusions: Viewing a patient's social media accounts can help ED physicians gain perspective into his or her mental health status and identify those at risk for suicide; however, ethical and privacy concerns associated with this method of data gathering make implementation of such a practice controversial. To justify its use, formal prospective studies analyzing if and how physician access to a patient's social media influences care should be performed.

Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook

Objectives:  To summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP).

Methods:  A survey of the literature was performed by the two editors of the NLP section of the International Medical Informatics Association (IMIA) Yearbook. Bibliographic databases PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A total of 709 papers were automatically ranked and then manually reviewed based on title and abstract. A shortlist of 15 candidate best papers was selected by the section editors and peer-reviewed by independent external reviewers to come to the three best clinical NLP papers for 2017.

Results:  Clinical NLP best papers provide a contribution that ranges from methodological studies to the application of research results to practical clinical settings. They draw from text genres as diverse as clinical narratives across hospitals and languages or social media.

Conclusions:  Clinical NLP continued to thrive in 2017, with an increasing number of contributions towards applications compared to fundamental methods. Methodological work explores deep learning and system adaptation across language variants. Research results continue to translate into freely available tools and corpora, mainly for the English language.

Comparing Indicators of Suicidality Among Users in Different Types of Nonprofessional Suicide Message Boards

BACKGROUND

Little is known about linguistic differences between nonprofessional suicide message boards that differ in regard to their predominant attitude to suicide.

AIMS

To compare linguistic indicators potentially related to suicidality between anti-suicide, neutral, and pro-suicide message boards, and between the types of posters (primary posters, who initiate the thread, and the respective respondents).

METHOD

In all, 1,200 threads from seven German-language nonprofessional suicide message boards were analyzed using the software Linguistic Inquiry and Word Count (LIWC) with regard to wording related to suicidal fantasies, aggression, and indicators of so-called suicidal constriction. Data were analyzed with ANOVA.

RESULTS

There were fewer words related to affective, social, cognitive, and communicative processes in pro-suicide message boards than in other boards. Death-related wording and aggression as well as tentative wording appeared more prevalent in pro-suicide boards.

LIMITATIONS

Complex language structures cannot be analyzed with LIWC.

CONCLUSION

The results suggest fewer emotion words and wording related to social circumstances among primary posters and respondents in pro-suicide boards as compared with other boards, and a higher use of death- and aggression-related words. These findings might signal a higher degree of suicidality or sheer differences in matters of interest or social desirability. The differences require attention in practice and research.

Extracting psychiatric stressors for suicide from social media using deep learning

BACKGROUND

Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based methods and transfer learning strategy which leverages an existing annotation dataset from clinical text.

METHODS

First, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier. Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN) based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy was adopted by leveraging existing annotation from clinical text.

RESULTS & CONCLUSIONS

To our best knowledge, this is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by exact match. The results indicate the advantages of deep learning based methods for the automated stressors recognition from social media.

Detecting Suicidal Ideation on Forums: Proof-of-Concept Study

BACKGROUND

In 2016, 44,965 people in the United States died by suicide. It is common to see people with suicidal ideation seek help or leave suicide notes on social media before attempting suicide. Many prefer to express their feelings with longer passages on forums such as Reddit and blogs. Because these expressive posts follow regular language patterns, potential suicide attempts can be prevented by detecting suicidal posts as they are written.

OBJECTIVE

This study aims to build a classifier that differentiates suicidal and nonsuicidal forum posts via text mining methods applied on post titles and bodies.

METHODS

A total of 508,398 Reddit posts longer than 100 characters and posted between 2008 and 2016 on SuicideWatch, Depression, Anxiety, and ShowerThoughts subreddits were downloaded from the publicly available Reddit dataset. Of these, 10,785 posts were randomly selected and 785 were manually annotated as suicidal or nonsuicidal. Features were extracted using term frequency-inverse document frequency, linguistic inquiry and word count, and sentiment analysis on post titles and bodies. Logistic regression, random forest, and support vector machine (SVM) classification algorithms were applied on resulting corpus and prediction performance is evaluated.

RESULTS

The logistic regression and SVM classifiers correctly identified suicidality of posts with 80% to 92% accuracy and F1 score, respectively, depending on different data compositions closely followed by random forest, compared to baseline ZeroR algorithm achieving 50% accuracy and 66% F1 score.

CONCLUSIONS

This study demonstrated that it is possible to detect people with suicidal ideation on online forums with high accuracy. The logistic regression classifier in this study can potentially be embedded on blogs and forums to make the decision to offer real-time online counseling in case a suicidal post is being written.

Harnessing Social Media to Explore Youth Social Withdrawal in Three Major Cities in China: Cross-Sectional Web Survey

BACKGROUND

Socially withdrawn youth belong to an emerging subgroup of youth who are not in employment, education, or training and who have limited social interaction intention and opportunities. The use of the internet and social media is expected to be an alternative and feasible way to reach this group of young people because of their reclusive nature.

OBJECTIVE

The aim of this study was to explore the possibility of using various social media platforms to investigate the existence of the phenomenon of youth social withdrawal in 3 major cities in China.

METHODS

A cross-sectional open Web survey was conducted from October 2015 to May 2016 to identify and reach socially withdrawn youth in 3 metropolitan cities in China: Beijing, Shanghai, and Shenzhen. To advertise the survey, 3 social media platforms were used: Weibo, WeChat, and Wandianba, a social networking gaming website.

RESULTS

In total, 137 participants completed the survey, among whom 13 (9.5%) were identified as belonging to the withdrawal group, 7 (5.1%) to the asocial group, and 9 (6.6%) to the hikikomori group (both withdrawn and asocial for more than 3 months). The cost of recruitment via Weibo was US $7.27 per participant.

CONCLUSIONS

Several social media platforms in China are viable and inexpensive tools to reach socially withdrawn youth, and internet platforms that specialize in a certain culture or type of entertainment appeared to be more effective in reaching socially withdrawn youth.

Traditional and new media's influence on suicidal behavior and contagion

The role of nonfictional and fictional media in suicide contagion has been well established, ostensibly beginning with the publication of Goethe's The Sorrows of Young Werther in 1774. In recent decades, the emergence of several new forms of media (e.g. websites, social media, blogs, smartphone applications) has revolutionized the communication and social interaction paradigms. This article reviews "the Werther effect" (or suicide contagion related to media), special populations who are more influential or susceptible, current media reporting guidelines and their effectiveness, and the latest research on new media and its effect on suicide and suicide contagion. The aim is to update recommendations on how to mitigate the potential negative effects of both traditional and new media on suicidal behavior and suicide contagion.

Influencing the Conversation About Masculinity and Suicide: Evaluation of the Man Up Multimedia Campaign Using Twitter Data

BACKGROUND

It has been suggested that some dominant aspects of traditional masculinity are contributing to the high suicide rates among Australian men. We developed a three-episode documentary called Man Up, which explores the complex relationship between masculinity and suicide and encourages men to question socially imposed rules about what it means to be a man and asks them to open up, express difficult emotions, and seek help if and when needed. We ran a three-phase social media campaign alongside the documentary using 5 channels (Twitter, Facebook, Instagram, YouTube, and Tumblr).

OBJECTIVE

This study aimed to examine the extent to which the Man Up Twitter campaign influenced the social media conversation about masculinity and suicide.

METHODS

We used Twitter insights data to assess the reach of and engagement with the campaign (using metrics on followers, likes, retweets, and impressions) and to determine the highest and lowest performing tweets in the campaign (using an aggregated performance measure of reactions). We used original content tweets to determine whether the campaign increased the volume of relevant Twitter conversations (aggregating the number of tweets for selected campaign hashtags over time), and we used a subset of these data to gain insight into the main content themes with respect to audience engagement.

RESULTS

The campaign generated a strong following that was engaged with the content of the campaign; over its whole duration, the campaign earned approximately 5000 likes and 2500 retweets and gained around 1,022,000 impressions. The highest performing tweets posted by the host included video footage and occurred during the most active period of the campaign (around the screening of the documentary). The volume of conversations in relation to commonly used hashtags (#MANUP, #ABCMANUP, #LISTENUP, and #SPEAKUP) grew in direct relation to the campaign activities, achieving strongest growth during the 3 weeks when the documentary was aired. Strongest engagement was found with content related to help-seeking, masculinity, and expressing emotions. A number of followers tweeted personal stories that revealed overwhelmingly positive perceptions of the content of the documentary and strongly endorsed its messages.

CONCLUSIONS

The Man Up Twitter campaign triggered conversations about masculinity and suicide that otherwise may not have happened. For some, this may have been game-changing in terms of shifting attitudes toward expressing emotions and reaching out to others for help. The campaign was particularly effective in disseminating information and promoting conversations in real time, an advantage that it had over more traditional health promotion campaigns. This sort of approach could well be adapted to other areas of mental (and physical) health promotion campaigns to increase their reach and effectiveness.

Engagement of vulnerable youths using internet platforms

Aim

The aim of this study was to explore the online distress and help-seeking behavior of youths in Hong Kong.

Methods

A cross-sectional telephone-based survey was conducted among 1,010 young people in Hong Kong. Logistic regression analysis was then performed to identify the factors associated with those who reported expressing emotional distress online and the differences in help-seeking behavior among four groups of youths: (1) the non-distressed (reference) group; (2) “Did not seek help” group; (3) “Seek informal help” group; and (4) “Seek formal help” group.

Results

The seeking of help and expression of distress online were found to be associated with a higher lifetime prevalence of suicidal ideation. The “Seek formal help” and “Did not seek help” groups had a similar risk profile, including a higher prevalence of suicidal ideation, non-suicidal self-injury, unsafe sex, and being bullied. The “Seek informal help” group was more likely to express distress online, which indicates that this population of youths may be accessible to professional identification. Approximately 20% of the distressed youths surveyed had not sought help despite expressing their distress online.

Implication

The study’s results indicate that helping professionals have opportunities to develop strategic engagement methods that make use of social media to help distressed youths.