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

Prediction techniques of movie box office using neural networks and emotional mining

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved.

Identifying Reddit Users at a High Risk of Suicide and Their Linguistic Features During the COVID-19 Pandemic: Growth-Based Trajectory Model

BACKGROUND

Suicide has emerged as a critical public health concern during the COVID-19 pandemic. With social distancing measures in place, social media has become a significant platform for individuals expressing suicidal thoughts and behaviors. However, existing studies on suicide using social media data often overlook the diversity among users and the temporal dynamics of suicide risk.

OBJECTIVE

By examining the variations in post volume trajectories among users on the r/SuicideWatch subreddit during the COVID-19 pandemic, this study aims to investigate the heterogeneous patterns of change in suicide risk to help identify social media users at high risk of suicide. We also characterized their linguistic features before and during the pandemic.

METHODS

We collected and analyzed post data every 6 months from March 2019 to August 2022 for users on the r/SuicideWatch subreddit (N=6163). A growth-based trajectory model was then used to investigate the trajectories of post volume to identify patterns of change in suicide risk during the pandemic. Trends in linguistic features within posts were also charted and compared, and linguistic markers were identified across the trajectory groups using regression analysis.

RESULTS

We identified 2 distinct trajectories of post volume among r/SuicideWatch subreddit users. A small proportion of users (744/6163, 12.07%) was labeled as having a high risk of suicide, showing a sharp and lasting increase in post volume during the pandemic. By contrast, most users (5419/6163, 87.93%) were categorized as being at low risk of suicide, with a consistently low and mild increase in post volume during the pandemic. In terms of the frequency of most linguistic features, both groups showed increases at the initial stage of the pandemic. Subsequently, the rising trend continued in the high-risk group before declining, while the low-risk group showed an immediate decrease. One year after the pandemic outbreak, the 2 groups exhibited differences in their use of words related to the categories of personal pronouns; affective, social, cognitive, and biological processes; drives; relativity; time orientations; and personal concerns. In particular, the high-risk group was discriminant in using words related to anger (odds ratio [OR] 3.23, P<.001), sadness (OR 3.23, P<.001), health (OR 2.56, P=.005), achievement (OR 1.67, P=.049), motion (OR 4.17, P<.001), future focus (OR 2.86, P<.001), and death (OR 4.35, P<.001) during this stage.

CONCLUSIONS

Based on the 2 identified trajectories of post volume during the pandemic, this study divided users on the r/SuicideWatch subreddit into suicide high- and low-risk groups. Our findings indicated heterogeneous patterns of change in suicide risk in response to the pandemic. The high-risk group also demonstrated distinct linguistic features. We recommend conducting real-time surveillance of suicide risk using social media data during future public health crises to provide timely support to individuals at potentially high risk of suicide.

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study

Background

Assessing patients' suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have poor agreement in screening suicide risk. Patients' speech may provide more objective, language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk in depression is lacking in the literature.

Objective

This study aimed to determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML).

Methods

This cross-sectional study recruited 305 participants between October 2020 and May 2022 (mean age 53.0, SD 11.77 years; female: n=176, 57%), of which 197 had lifetime depression and 108 were healthy. This study was part of ongoing research on characterizing depression with a case-control design. In this study, 236 participants were nonsuicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants' verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC).

Results

Ordinal logistic regression revealed significant suicide-related language features in participants' responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (odds ratio [OR] 2.91, 95% CI 1.22-8.55; P=.02). Random forest models demonstrated that text analysis of the direct responses to H11 was effective in identifying individuals with high suicide risk (AUC 0.76-0.89; P<.001) and detecting suicide risk in general, including both low and high suicide risk (AUC 0.83-0.92; P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients' disclosure of suicidal ideation. Based on the response to the question on hypochondriasis, ML models were trained to identify individuals with high suicide risk (AUC 0.76; P<.001).

Conclusions

This study examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection, including online chatbot-based interviews for universal screening.

Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Assessing Vulnerability to Surges in Suicide-Related Tweets Using Japan Census Data: Case-Only Study

BACKGROUND

As the use of social media becomes more widespread, its impact on health cannot be ignored. However, limited research has been conducted on the relationship between social media and suicide. Little is known about individuals' vulnerable to suicide, especially when social media suicide information is extremely prevalent.

OBJECTIVE

This study aims to identify the characteristics underlying individuals' vulnerability to suicide brought about by an increase in suicide-related tweets, thereby contributing to public health.

METHODS

A case-only design was used to investigate vulnerability to suicide using individual data of people who died by suicide and tweet data from January 1, 2011, through December 31, 2014. Mortality data were obtained from Japanese government statistics, and tweet data were provided by a commercial service. Tweet data identified the days when suicide-related tweets surged, and the date-keyed merging was performed by considering 3 and 7 lag days. For the merged data set for analysis, the logistic regression model was fitted with one of the personal characteristics of interest as a dependent variable and the dichotomous exposure variable. This analysis was performed to estimate the interaction between the surges in suicide-related tweets and personal characteristics of the suicide victims as case-only odds ratios (ORs) with 95% CIs. For the sensitivity analysis, unexpected deaths other than suicide were considered.

RESULTS

During the study period, there were 159,490 suicides and 115,072 unexpected deaths, and the number of suicide-related tweets was 2,804,999. Following the 3-day lag of a highly tweeted day, there were significant interactions for those who were aged 40 years or younger (OR 1.09, 95% CI 1.03-1.15), male (OR 1.12, 95% CI 1.07-1.18), divorced (OR 1.11, 95% CI 1.03 1.19), unemployed (OR 1.12, 95% CI 1.02-1.22), and living in urban areas (OR 1.26, 95% CI 1.17 1.35). By contrast, widowed individuals had significantly lower interactions (OR 0.83, 95% CI 0.77-0.89). Except for unemployment, significant relationships were also observed for the 7-day lag. For the sensitivity analysis, no significant interactions were observed for other unexpected deaths in the 3-day lag, and only the widowed had a significantly larger interaction than those who were married (OR 1.08, 95% CI 1.02-1.15) in the 7-day lag.

CONCLUSIONS

This study revealed the interactions of personal characteristics associated with susceptibility to suicide-related tweets. In addition, a few significant relationships were observed in the sensitivity analysis, suggesting that such an interaction is specific to suicide deaths. In other words, individuals with these characteristics, such as being young, male, unemployed, and divorced, may be vulnerable to surges in suicide-related tweets. Thus, minimizing public health strain by identifying people who are vulnerable and susceptible to a surge in suicide-related information on the internet is necessary.

Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches

Depression is one of the most common mental illnesses but remains underdiagnosed. Suicide, as a core symptom of depression, urgently needs to be monitored at an early stage, i.e., the suicidal ideation (SI) stage. Depression and subsequent suicidal ideation should be supervised on social media. In this research, we investigated depression and concomitant suicidal ideation by identifying individuals' linguistic characteristics through machine learning approaches. On Weibo, we sampled 487,251 posts from 3196 users from the depression super topic community (DSTC) as the depression group and 357,939 posts from 5167 active users on Weibo as the control group. The results of the logistic regression model showed that the SCLIWC (simplified Chinese version of LIWC) features such as affection, positive emotion, negative emotion, sadness, health, and death significantly predicted depression (Nagelkerke's R2 = 0.64). For model performance: F-measure = 0.78, area under the curve (AUC) = 0.82. The independent samples' t-test showed that SI was significantly different between the depression (0.28 ± 0.5) and control groups (-0.29 ± 0.72) (t = 24.71, p < 0.001). The results of the linear regression model showed that the SCLIWC features, such as social, family, affection, positive emotion, negative emotion, sadness, health, work, achieve, and death, significantly predicted suicidal ideation. The adjusted R2 was 0.42. For model performance, the correlation between the actual SI and predicted SI on the test set was significant (r = 0.65, p < 0.001). The topic modeling results were in accordance with the machine learning results. This study systematically investigated depression and subsequent SI-related linguistic characteristics based on a large-scale Weibo dataset. The findings suggest that analyzing the linguistic characteristics on online depression communities serves as an efficient approach to identify depression and subsequent suicidal ideation, assisting further prevention and intervention.

Digital self-harm and suicidality among adolescents

BACKGROUND

Research on digital self-harm - the anonymous online posting, sending, or otherwise sharing of hurtful content about oneself - is still in its infancy. Yet unexplored is whether digital self-harm is related to suicidal ideation or suicide attempts.

METHODS

In the current study, survey data were collected in 2019 from a national sample of 4972 American middle and high school students (M_age  = 14.5; 50% female). Logistic regression analysis was used to assess whether lifetime engagement in two different indicators of digital self-harm was associated with suicidal thoughts and attempts within the past year.

RESULTS

Logistic regression analysis showed that engagement in digital self-harm was associated with a five- to sevenfold increase in the likelihood of reporting suicidal thoughts and a nine- to 15-fold increase in the likelihood of a suicide attempt.

CONCLUSIONS

Results suggest a connection between digital self-harm and suicidality. As such, health professionals must screen for digital self-harm to address underlying mental health problems among youth that may occur prior to or alongside suicidality, and parents/caregivers must convey to children that they are available to dialog, support, and assist with the root issues that may eventually manifest as digital self-harm.

Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons

INTRODUCTION

In recent years, research has used psycholinguistic features in public discourse, networking behaviors on social media and profile information to train models for depression detection. However, the most widely adopted approach for the extraction of psycholinguistic features is to use the Linguistic Inquiry Word Count (LIWC) dictionary and various affective lexicons. Other features related to cultural factors and suicide risk have not been explored. Moreover, the use of social networking behavioral features and profile features would limit the generalizability of the model. Therefore, our study aimed at building a prediction model of depression for text-only social media data through a wider range of possible linguistic features related to depression, and illuminate the relationship between linguistic expression and depression.

METHODS

We collected 789 users' depression scores as well as their past posts on Weibo, and extracted a total of 117 lexical features via Simplified Chinese Linguistic Inquiry Word Count, Chinese Suicide Dictionary, Chinese Version of Moral Foundations Dictionary, Chinese Version of Moral Motivation Dictionary, and Chinese Individualism/Collectivism Dictionary.

RESULTS

Results showed that all the dictionaries contributed to the prediction. The best performing model occurred with linear regression, with the Pearson correlation coefficient between predicted values and self-reported values was 0.33, the R-squared was 0.10, and the split-half reliability was 0.75.

DISCUSSION

This study did not only develop a predictive model applicable to text-only social media data, but also demonstrated the importance taking cultural psychological factors and suicide related expressions into consideration in the calculation of word frequency. Our research provided a more comprehensive understanding of how lexicons related to cultural psychology and suicide risk were associated with depression, and could contribute to the recognition of depression.

Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field

Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data.In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems.In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings.Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.

Mental Health Analysis in Social Media Posts: A Survey

The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of social media data bring to fore the importance of real-time responsible AI models for mental health analysis. Aiming to classify the research directions for social computing and tracking advances in the development of machine learning (ML) and deep learning (DL) based models, we propose a comprehensive survey on quantifying mental health on social media. We compose a taxonomy for mental healthcare and highlight recent attempts in examining social well-being with personal writings on social media. We define all the possible research directions for mental healthcare and investigate a thread of handling online social media data for stress, depression and suicide detection for this work. The key features of this manuscript are (i) feature extraction and classification, (ii) recent advancements in AI models, (iii) publicly available dataset, (iv) new frontiers and future research directions. We compile this information to introduce young research and academic practitioners with the field of computational intelligence for mental health analysis on social media. In this manuscript, we carry out a quantitative synthesis and a qualitative review with the corpus of over 92 potential research articles. In this context, we release the collection of existing work on suicide detection in an easily accessible and updatable repository:https://github.com/drmuskangarg/mentalhealthcare.

Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning

In this study, we implemented machine learning models that can detect suicidality posts on Twitter. We randomly selected and annotated 20,000 tweets and explored metadata and text features to build effective models. Metadata features were studied in great details to understand their possibility and importance in suicidality detection models. Results showed that posting type (i.e., reply or not) and time-related features such as the month, day of the week, and the time (AM vs. PM) were the most important metadata features in suicidality detection models. Specifically, the probability of a social media post being suicidal is higher if the post is a reply to other users rather than an original tweet. Moreover, tweets created in the afternoon, on Fridays and weekends, and in fall have higher probabilities of being detected as suicidality tweets compared with those created in other times. By integrating metadata and text features, we obtained a model of good performance (i.e., F1 score of 0.846) that can assist humans in the real-world setting to detect suicidality social media posts.

Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results

Suicide, as an increasingly prominent social problem, has attracted widespread social attention in the mental health field. Traditional suicide clinical assessment and risk questionnaires lack timeliness and proactivity, and high-risk groups often conceal their intentions, which is not conducive to early suicide prevention. In this study, we used machine-learning algorithms to extract text features from Sina Weibo data and built a suicide risk-prediction model to predict four dimensions of the Suicide Possibility Scale-hopelessness, suicidal ideation, negative self-evaluation, and hostility-all with model validity of 0.34 or higher. Through this method, we can detect the symptoms of suicidal ideation in a more detailed way and improve the proactiveness and accuracy of suicide risk prevention and control.

Social Service Workers' Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform

To gain insight into current use of social-media platforms in human services delivery, we systematically surveyed 172 social-service workers from six agencies in a Midwest US city to gather data about social-media usage among social-service providers, potential challenges and benefits of using social media, and whether a social-media-based informatics platform could be valuable. Quantitative analyses showed that approximately half of participants have used social media to collect client-related information; nearly one-quarter indicated "often" or "nearly daily" use. Adjusting for the effects of worker characteristics, social-media use was associated with the type of agency involved and with increased tenure in social services. Adjusted results also showed that participants' comfort with using the potential application was greater in those agencies substantially involved with investigative/legal work. However, trust in the information collected by the potential application was a stronger, independent predictor of comfort using the tool. Qualitative analyses identified numerous challenges and ethical concerns, and positive and negative aspects of a social-media-based informatics platform. If the platform is to be created, work must be done carefully, fully considering ethical issues rightly raised by social service workers, existing agency policies, and professional standards. Future research should investigate ways to negotiate these complex challenges.

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.

A Hybrid Deep Learning Model Using Grid Search and Cross-Validation for Effective Classification and Prediction of Suicidal Ideation from Social Network Data

Suicide deaths due to depression and mental stress are growing rapidly at an alarming rate. People freely express their feelings and emotions on social network sites while they feel hesitant to express such feelings during face-to-face interactions with their dear ones. In this study, a dataset comprising 20,000 posts was taken from Reddit and preprocessed into tokens using a variety of effective word2vec techniques. A new hybrid approach is proposed by combining the attention model in a convolutional neural network and long-short-term- memory. The objective of this research is to develop an effective learning model to evaluate the data on social media for the efficient and accurate identification of people with suicidal ideation. The proposed attention convolution long short-term memory (ACL) model uses hyperparameter tuning using a grid search to select optimized hyperparameters. From the experimental evaluation, it is shown that the proposed model, that is, ACL with Glove embedding after hyperparameter tuning gives the highest Accuracy of 88.48%, Precision of 87.36%, F1 score of 90.82% and specificity of 79.23% and ACL with Random embedding gives the highest Recall of 94.94% when compared to the state-of-the-art algorithms.

Social Media Sentiments on Suicides at the New York City Landmark, Vessel: A Twitter Study

Vessel is a landmark created by Heatherwick Studio where visitors can enjoy views of New York City from different heights and perspectives. However, between February 2020 and July 2021, four individuals jumped to their deaths from the landmark. Effective preventive solutions have yet to be identified, and the site is currently closed. In this study, we examined the trajectory of public sentiment on the suicide-related activity at Vessel on Twitter by investigating the engagement patterns and identifying themes about the four suicides from February 2020 to August 2021 (n = 3058 tweets). The results show increased levels of discussion about each successive suicide case in the first 14 days following each incident (from 6 daily tweets for the first case to 104 for the fourth case). It also took longer for relevant discussions to dissipate (4 days for the first and 14 days for the fourth case, KS statistic = 0.71, p < 0.001). Thematic analysis shows a shift from expressions of emotion to urging suicide prevention actions in the third and fourth cases; additionally, we detected growing support for restricting means. We suggest that, prior to the reopening of Vessel, collective efforts should be made to install safety protections and reduce further suicide risks.

Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study

Background

Effective suicide risk assessments and interventions are vital for suicide prevention. Although assessing such risks is best done by health care professionals, people experiencing suicidal ideation may not seek help. Hence, machine learning (ML) and computational linguistics can provide analytical tools for understanding and analyzing risks. This, therefore, facilitates suicide intervention and prevention.

Objective

This study aims to explore, using statistical analyses and ML, whether computerized language analysis could be applied to assess and better understand a person’s suicide risk on social media.

Methods

We used the University of Maryland Suicidality Dataset comprising text posts written by users (N=866) of mental health–related forums on Reddit. Each user was classified with a suicide risk rating (no, low, moderate, or severe) by either medical experts or crowdsourced annotators, denoting their estimated likelihood of dying by suicide. In language analysis, the Linguistic Inquiry and Word Count lexicon assessed sentiment, thinking styles, and part of speech, whereas readability was explored using the TextStat library. The Mann-Whitney U test identified differences between at-risk (low, moderate, and severe risk) and no-risk users. Meanwhile, the Kruskal-Wallis test and Spearman correlation coefficient were used for granular analysis between risk levels and to identify redundancy, respectively. In the ML experiments, gradient boost, random forest, and support vector machine models were trained using 10-fold cross validation. The area under the receiver operator curve and F₁-score were the primary measures. Finally, permutation importance uncovered the features that contributed the most to each model’s decision-making.

Results

Statistically significant differences (P<.05) were identified between the at-risk (671/866, 77.5%) and no-risk groups (195/866, 22.5%). This was true for both the crowd- and expert-annotated samples. Overall, at-risk users had higher median values for most variables (authenticity, first-person pronouns, and negation), with a notable exception of clout, which indicated that at-risk users were less likely to engage in social posturing. A high positive correlation (ρ>0.84) was present between the part of speech variables, which implied redundancy and demonstrated the utility of aggregate features. All ML models performed similarly in their area under the curve (0.66-0.68); however, the random forest and gradient boost models were noticeably better in their F₁-score (0.65 and 0.62) than the support vector machine (0.52). The features that contributed the most to the ML models were authenticity, clout, and negative emotions.

Conclusions

In summary, our statistical analyses found linguistic features associated with suicide risk, such as social posturing (eg, authenticity and clout), first-person singular pronouns, and negation. This increased our understanding of the behavioral and thought patterns of social media users and provided insights into the mechanisms behind ML models. We also demonstrated the applicative potential of ML in assisting health care professionals to assess and manage individuals experiencing suicide risk.

Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic

Since the outbreak of the COVID-19 pandemic, increases in suicidal ideation and suicide attempts in adolescents have been registered. Many adolescents experiencing suicidal ideation turn to online communities for social support. In this retrospective observational study, we investigated the communication-language style, contents and user activity-in 7975 unique posts and 51,119 comments by N = 2862 active adolescent users in a large suicidal ideation support community (SISC) on the social media website reddit.com in the onset period of the COVID-19 pandemic. We found significant relative changes in language style markers for hopelessness such as negative emotion words (+ 10.00%) and positive emotion words (- 3.45%) as well as for social disengagement such as social references (- 8.63%) and 2nd person pronouns (- 33.97%) since the outbreak of the pandemic. Using topic modeling with Latent Dirichlet Allocation (LDA), we identified significant changes in content for the topics Hopelessness (+ 23.98%), Suicide Methods (+ 17.11%), Social Support (- 14.91%), and Reaching Out to users (- 28.97%). Changes in user activity point to an increased expression of mental health issues and decreased engagement with other users. The results indicate a potential shift in communication patterns with more adolescent users expressing their suicidal ideation rather than relating with or supporting other users during the COVID-19 pandemic.

Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion

Suicide has become a serious problem, and how to prevent suicide has become a very important research topic. Social media provides an ideal platform for monitoring suicidal ideation. This paper presents an integrated model for multidimensional information fusion. By integrating the best classification models determined by single and multiple features, different feature information is combined to better identify suicidal posts in online social media. This approach was assessed with a dataset formed from 40,222 posts annotated by Weibo. By integrating the best classification model of single features and multidimensional features, the proposed model ((BSC + RFS)-fs, WEC-fs) achieved 80.61% accuracy and a 79.20% F1-score. Other representative text information representation methods and demographic factors related to suicide may also be important predictors of suicide, which were not considered in this study. To the best of our knowledge, this is the good try that feature combination and ensemble algorithms have been fused to detect user-generated content with suicidal ideation. The findings suggest that feature combinations do not always work well, and that an appropriate combination strategy can make classification models work better. There are differences in the information contained in different functional carriers, and a targeted choice classification model may improve the detection rate of suicidal ideation.

Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study

Background

Social media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research.

Objective

We aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet.

Methods

Tweets containing a specific lexicon relating to suicide were filtered through Twitter's public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed.

Results

Of 2509 tweets, 8.61% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness (ρ=0.266; P<.001), joy (ρ=–0.234; P=.001), general risk (ρ=0.908; P<.001), and intensity of suicidal ideation (ρ=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time (β=0.311; P=.001), as well as being a predictor for fear (β=–0.009; P=.01) and emotional valence (β=0.007; P=.009). The model explained 75% of the variance.

Conclusions

These findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media.

Natural language processing applied to mental illness detection: a narrative review

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults

Background:

Artificial Intelligence is making a significant transformation in human lives. Its application in the medical and healthcare field has been also observed making an impact and improving overall outcomes. There has been a quest for similar processes in mental health due to the lack of observable changes in the areas of suicide prevention. In the last five years, there has been an emerging body of empirical research applying the technology of artificial intelligence (AI) and machine learning (ML) in mental health.

Objective:

To review the clinical applicability of the AI/ML-based tools in suicide prevention.

Methods:

The compelling question of predicting suicidality has been the focus of this research. We performed a broad literature search and then identified 36 articles relevant to meet the objectives of this review. We review the available evidence and provide a brief overview of the advances in this field.

Conclusion:

In the last five years, there has been more evidence supporting the implementation of these algorithms in clinical practice. Its current clinical utility is limited to using electronic health records and could be highly effective in conjunction with existing tools for suicide prevention. Other potential sources of relevant data include smart devices and social network sites. There are some serious questions about data privacy and ethics which need more attention while developing these new modalities in suicide research.

Machine learning of language use on Twitter reveals weak and non-specific predictions

Depressed individuals use language differently than healthy controls and it has been proposed that social media posts can be used to identify depression. Much of the evidence behind this claim relies on indirect measures of mental health and few studies have tested if these language features are specific to depression versus other aspects of mental health. We analysed the Tweets of 1006 participants who completed questionnaires assessing symptoms of depression and 8 other mental health conditions. Daily Tweets were subjected to textual analysis and the resulting linguistic features were used to train an Elastic Net model on depression severity, using nested cross-validation. We then tested performance in a held-out test set (30%), comparing predictions of depression versus 8 other aspects of mental health. The depression trained model had modest out-of-sample predictive performance, explaining 2.5% of variance in depression symptoms (R² = 0.025, r = 0.16). The performance of this model was as-good or superior when used to identify other aspects of mental health: schizotypy, social anxiety, eating disorders, generalised anxiety, above chance for obsessive-compulsive disorder, apathy, but not significant for alcohol abuse or impulsivity. Machine learning analysis of social media data, when trained on well-validated clinical instruments, could not make meaningful individualised predictions regarding users’ mental health. Furthermore, language use associated with depression was non-specific, having similar performance in predicting other mental health problems.

Identifying Depression-Related Behavior on Facebook—An Experimental Study

Depression is one of the major mental health problems in the world and the leading cause of disability worldwide. As people leave more and more digital traces in the online world, it becomes possible to detect depression-related behavior based on people’s online activities. We use a novel Facebook study to identify possible non-textual elements of depression-related behavior in a social media environment. This study focuses on the relationship between depression and the volume and composition of Facebook friendship networks and the volume and temporal variability of Facebook activities. We also tried to establish a link between depression and the interest categories of the participants. The significant predictors were partly different for cognitive-affective depression and somatic depression. Earlier studies found that depressed people have a smaller online social network. We found the same pattern in the case of cognitive-affective depression. We also found that they posted less in others’ timelines, but we did not find that they posted more in their own timeline. Our study was the first to use the Facebook ads interest data to predict depression. Those who were classified into the less interest category by Facebook had higher depression levels on both scales.

Social media and suicide in social movements: a case study in Hong Kong

Research has indicated that excessive and sensationalized suicide reporting can lead to copycat suicides, especially when deaths involve well-known people. Little is known, however, about the impact of the reporting of suspected protestor suicide deaths during social unrest, particularly in an age of social media. In June 2019, the most substantial social unrest in Hong Kong since its handover in 1997 was triggered by the proposed Anti-Extradition Law Amendment Bill (Anti-ELAB). The social unrest subsided when Hong Kong and many parts of the world were hit by Covid-19 and very strict quarantine measures were imposed on crowd gatherings in Hong Kong at the end of January 2020. A number of reported suicides and deaths of undetermined cause took place during this 8-month period that received considerable attention. To better understand the possible effects of these highly publicized deaths, we examined media reports of suspected suicide cases before, during and after the protest period, as well as topics of suicide-related threads and their replies in social media forums. We found no clear evidence of increased rates of suicide as a result of these incidents, or during the protest period; however, it is suggested that certain narratives and attention surrounding the suspected suicides and undetermined deaths may have contributed to collective emotions such as sadness and anxiety. Some implications for misinformation (intentionally or un-intentionally) and mitigation of suicide risk during social unrest are discussed.

Translating promise into practice: a review of machine learning in suicide research and prevention

In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise. In this Review, we synthesise the broad empirical and review literature on electronic health record-based machine learning in suicide research, and focus on matters of crucial importance for implementation of machine learning in clinical practice. The challenge of preventing statistically rare outcomes is well known; progress requires tackling data quality, transparency, and ethical issues. In the future, machine learning models might be explored as methods to enable targeting of interventions to specific individuals depending upon their level of need-ie, for precision medicine. Primarily, however, the promise of machine learning for suicide prevention is limited by the scarcity of high-quality scalable interventions available to individuals identified by machine learning as being at risk of suicide.

Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks

We describe the design and evaluation of a sub-clinical digital assessment tool that integrates digital biomarkers of depression. Based on three standard cognitive tasks (D2 Test of Attention, Delayed Matching to Sample Task, Spatial Working Memory Task) on which people with depression have been known to perform differently than a control group, we iteratively designed a digital assessment tool that could be deployed outside of laboratory contexts, in uncontrolled home environments on computer systems with widely varying system characteristics (e.g., displays resolution, input devices). We conducted two online studies, in which participants used the assessment tool in their own homes, and completed subjective questionnaires including the Patient Health Questionnaire (PHQ-9)-a standard self-report tool for assessing depression in clinical contexts. In a first study (n = 269), we demonstrate that each task can be used in isolation to significantly predict PHQ-9 scores. In a second study (n = 90), we replicate these results and further demonstrate that when used in combination, behavioral metrics from the three tasks significantly predicted PHQ-9 scores, even when taking into account demographic factors known to influence depression such as age and gender. A multiple regression model explained 34.4% of variance in PHQ-9 scores with behavioral metrics from each task providing unique and significant contributions to the prediction.

Hierarchical and nested associations of suicide with marriage, social support, quality of life, and depression among the elderly in rural China: Machine learning of psychological autopsy data

OBJECTIVES

To identify mechanisms underpinning the complex relationships between influential factors and suicide risk with psychological autopsy data and machine learning method.

DESIGN

A case-control study with suicide deaths selected using two-stage stratified cluster sampling method; and 1:1 age-and-gender matched live controls in the same geographic area.

SETTING

Disproportionately high risk of suicide among rural elderly in China.

PARTICIPANTS

A total of 242 subjects died from suicide and 242 matched live controls, 60 years of age and older.

MEASUREMENTS

Suicide death was determined based on the ICD-10 codes. Influential factors were measured using validated instruments and commonly accepted variables.

RESULTS

Of the total sample, 270 (55.8%) were male with mean age = 74.2 (SD = 8.2) years old. Four CART models were used to select influential factors using the criteria: areas under the curve (AUC) ≥ 0.8, sensitivity ≥ 0.8, and specificity ≥ 0.8. Each model included a lead predictor plus 8-10 hierarchically nested factors. Depression was the first to be selected in Model 1 as the lead predictor; After depression was excluded, quality of life (QOL) was selected in Model 2; After depression and QOL were excluded, social support was selected in Model 3. Finally, after all 3 lead factors were excluded, marital status was selected in Model 4. In addition, CART demonstrated the significance of several influential factors that would not be associated with suicide if the data were analyzed using the conventional logistic regression.

CONCLUSION

Associations between the key factors and suicide death for Chinese rural elderly are not linear and parallel but hierarchically nested that could not be effectively detected using conventional statistical methods. Findings of this study provide new and compelling evidence supporting tailored suicide prevention interventions at the familial, clinical and community levels.

Characteristics of High Suicide Risk Messages From Users of a Social Network-Sina Weibo "Tree Hole"

BACKGROUND

People with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and "Zoufan" is one of the largest depression "Tree Holes." To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the "Zoufan" tree hole via artificial intelligence robots.

OBJECTIVE

To explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the "Zoufan" tree hole.

METHODS

Knowledge graph technology was used to screen high suicide risk comments in the "Zoufan" tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis.

RESULTS

Among 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason.

CONCLUSION

Users sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment.

A Suicide Monitoring and Crisis Intervention Strategy Based on Knowledge Graph Technology for "Tree Hole" Microblog Users in China

“Zou Fan” is currently the largest “tree hole” on Weibo, where people having suicidal ideation often express their thoughts and use this channel to seek support. Therefore, early suicide monitoring and timely crisis intervention based on artificial intelligence technology are needed for this social media user group. This research was based on the knowledge graph technology, whereby “Tree Hole Intelligent Agent” (i.e., Artificial Intelligence Program) was used to identify “Zou Fan Tree Hole” users at high risk for suicide, and then, the “Tree Hole Action” carried out proactive suicide crisis intervention with them. The “Tree Hole Action” has temporarily prevented 3,629 potential suicides. The “Tree Hole Action” plays a significant role in suicide risk monitoring and crisis intervention for social media users and has been seen to have an important social impact.

Investigating Self-injury Support Solicitations and Responses on a Mobile Peer Support Application

Online informal support networks may provide a critical source of support for young people who self-injure. While these platforms are often intended to mitigate digital harm, there is limited understanding of how individuals use peer support venues to seek self-injury related support and the specific contingencies of supportive exchanges. The present mixed-methods study was designed to explore the types of concerns members express on a mobile peer support application and the types of responses that they receive. Specifically, our aims were to (1) understand the prevalence of peer support types exchanged and (2) surface more nuanced themes within these categories of support. We also explore the relationship between support sought through posts and received through comments. Findings have important theoretical implications for understanding support seeking and provision through a mobile peer support app, which can help guide the design and optimization of peer-driven platforms for individuals who self-injure.

An informatics approach to examine decision-making impairments in the daily life of individuals with depression

Mental health informatics studies methods that collect, model, and interpret a wide variety of data to generate useful information with theoretical or clinical relevance to improve mental health and mental health care. This article presents a mental health informatics approach that is based on the decision-making theory of depression, whereby daily life data from a natural sequential decision-making task are collected and modeled using a reinforcement learning method. The model parameters are then estimated to uncover specific aspects of decision-making impairment in individuals with depression. Empirical results from a pilot study conducted to examine decision-making impairments in the daily lives of university students with depression are presented to illustrate this approach. Future research can apply and expand on this approach to investigate a variety of daily life situations and psychiatric conditions and to facilitate new informatics applications. Using this approach in mental health research may generate useful information with both theoretical and clinical relevance and high ecological validity.

Online Suicide Identification in the Framework of Rhetorical Structure Theory (RST)

Background: Suicide is a serious social problem. Substantial efforts have been made to prevent suicide for many decades. The internet has become an important arena for suicide prevention and intervention. However, to the best of our knowledge, only one study has analyzed suicidal comments online from the perspective of rhetorical structure with incomplete rhetorical relations. We aimed to examine the rhetorical differences between Chinese social media users who died by suicide and those without suicidal ideation. Methods: The posts of 15 users who died by suicide and 15 not suffering from suicide ideation were annotated by five postgraduates with expertise in analyzing suicidal posts based on rhetorical structure theory (RST). Group differences were compared via a chi-square test. Results: Results showed that users who died by suicide posted significantly more posts and used more rhetorical relations. Moreover, the two groups displayed significant differences in 17 out of 23 rhetorical relations. Limitations: Because this study is largely exploratory and tentative, caution should be taken in generalizing our findings. Conclusions: Our results expand the methods of RST to the online suicidal identification field. There are implications for population-based suicide prevention by combining rhetorical structures with context analysis.

Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental health successfully detect signs of mental disorders, such as depression, by using data from social media. These AI depression detectors (AIDDs) identify users who are at risk of depression prior to any contact with the healthcare system. The article focuses on the ethical implications of AIDDs regarding affected users' health-related autonomy. Firstly, it presents the (ethical) discussion of AI in medicine and, specifically, in mental health. Secondly, two models of AIDDs using social media data and different usage scenarios are introduced. Thirdly, the concept of patient autonomy, according to Beauchamp and Childress, is critically discussed. Since this concept does not encompass the specific challenges linked with the digital context of AIDDs in social media sufficiently, the current analysis suggests, finally, an extended concept of health-related digital autonomy.

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

BACKGROUND

Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation.

OBJECTIVE

This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised.

METHODS

We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis.

RESULTS

We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance.

CONCLUSIONS

Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.

Comparing Suicide Risk Insights derived from Clinical and Social Media data

Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.

Information and Communication Technology Use in Suicide Prevention: Scoping Review

BACKGROUND

The use of information and communication technology (ICT) in suicide prevention has progressed rapidly over the past decade. ICT plays a major role in suicide prevention, but research on best and promising practices has been slow.

OBJECTIVE

This paper aims to explore the existing literature on ICT use in suicide prevention to answer the following question: what are the best and most promising ICT practices for suicide prevention?

METHODS

A scoping search was conducted using the following databases: PubMed, PsycINFO, Sociological Abstracts, and IEEE Xplore. These databases were searched for articles published between January 1, 2013, and December 31, 2018. The five stages of the scoping review process were as follows: identifying research questions; targeting relevant studies; selecting studies; charting data; and collating, summarizing, and reporting the results. The World Health Organization suicide prevention model was used according to the continuum of universal, selective, and indicated prevention.

RESULTS

Of the 3848 studies identified, 115 (2.99%) were selected. Of these, 10 regarded the use of ICT in universal suicide prevention, 53 referred to the use of ICT in selective suicide prevention, and 52 dealt with the use of ICT in indicated suicide prevention.

CONCLUSIONS

The use of ICT plays a major role in suicide prevention, and many promising programs were identified through this scoping review. However, large-scale evaluation studies are needed to further examine the effectiveness of these programs and strategies. In addition, safety and ethics protocols for ICT-based interventions are recommended.

Machine Learning for Mental Health in Social Media: Bibliometric Study

Background

Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention.

Objective

We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.

Methods

Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described.

Results

We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated.

Conclusions

The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

Editorial overview: Analytic and methodological innovations for suicide-focused research

This editorial overview provides an introduction to the Suicide and Life-Threatening Behaviors Special Issue: "Analytic and Methodological Innovations for Suicide-Focused Research." We outline several challenges faced by modern suicidologists, such as the need to integrate different analytical and methodological techniques from other fields with the unique data problems in suicide research. Therefore, the overall aim of this issue was to provide up-to-date methodological and analytical guidelines, recommendations, and considerations when conducting suicide-focused research. The articles herein present this information in an accessible way for researchers/clinicians and do not require a comprehensive background in quantitative methods. We introduce the topics covered in this special issue, which include how to conduct power analyses using simulations, work with large data sets, use experimental therapeutics, and choose covariates, as well as open science considerations, decision-making models, ordinal regression, machine learning, network analysis, and measurement considerations. Many of the topics covered in this issue provide step-by-step walkthroughs using worked examples with the accompanied code in free statistical programs (i.e., R). It is our hope that these articles provide suicidologists with valuable information and strategies that can help overcome some of the past limitations of suicide research, and improve the methodological rigor of our field.

Applying text mining methods to suicide research

OBJECTIVE

To introduce the research methods of computerized text mining and its possible applications in suicide research and to demonstrate the procedures of applying a specific text mining area, document classification, to a suicide-related study.

METHOD

A systematic search of academic papers that applied text mining methods to suicide research was conducted. Relevant papers were reviewed focusing on their research objectives and sources of data. Furthermore, a case of using natural language processing and document classification methods to analyze a large amount of suicide news was elaborated to showcase the methods.

RESULTS

Eighty-six papers using text mining methods for suicide research have been published since 2001. The most common research objective (72.1%) was to classify which documents exhibit suicide risk or were written by suicidal people. The most frequently used data source was online social media posts (45.3%), followed by e-healthcare records (25.6%). For the news classification case, the top three classifiers trained for classification tasks achieved 84% or higher accuracy.

CONCLUSIONS

Computerized text mining methods can help to scale up content analysis capacity and efficiency and uncover new insights and perspectives for suicide research.

The use of text-based responses to improve our understanding and prediction of suicide risk

OBJECTIVE

Text-based responses may provide significant contributions to suicide risk prediction, yet research including text data is limited. This may be due to a lack of exposure and familiarity with statistical analyses for this data structure.

METHOD

The current study provides an overview of data processing and statistical algorithms for text data, guided by an empirical example of 947 online participants who completed both open-ended items and traditional self-report measures. We give an introduction to a number of text-based statistical approaches, including dictionary-based methods, topic modeling, word embeddings, and deep learning.

RESULTS

We analyze responses from the open-ended question "How do you feel today?", detailing characteristics of the responses, as well as predicting past-year suicidal ideation.

CONCLUSIONS

We see the analysis of text from social media, open-ended questions, and other text sources (i.e., medical records) as an important form of complementary assessment to traditional scales, shedding insight on what we are missing in our current set of questionnaires, which may ultimately serve to improve both our understanding and prediction of suicide.

Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches

Machine learning is being used to discover models to predict the progression from suicidal ideation to action in clinical populations. While quantifiable improvements in prediction accuracy have been achieved over theory-driven efforts, models discovered through machine learning continue to fall short of clinical relevance. Thus, the value of machine learning for reaching this objective is hotly contested. We agree that machine learning, treated as a "black box" approach antithetical to theory-building, will not discover clinically relevant models of suicide. However, such models may be developed through deliberate synthesis of data- and theory-driven approaches. By providing an accessible overview of essential concepts and common methods, we highlight how generalizable models and scientific insight may be obtained by incorporating prior knowledge and expectations to machine learning research, drawing examples from suicidology. We then discuss challenges investigators will face when using machine learning to discover models of low prevalence outcomes, such as suicide.

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.