Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment

BACKGROUND

Recent research highlights the dynamics of suicide risk, resulting in a shift toward real-time methodologies, such as ecological momentary assessment (EMA), to improve suicide risk identification. However, EMA's reliance on active self-reporting introduces challenges, including participant burden and reduced response rates during crises. This study explores the potential of Screenomics-a passive digital phenotyping method that captures intensive, real-time smartphone screenshots-to detect suicide risk through text-based analysis.

METHOD

Seventy-nine participants with past-month suicidal ideation or behavior completed daily EMA prompts and provided smartphone data over 28 days, resulting in approximately 7.5 million screenshots. Text from screenshots was analyzed using a validated dictionary encompassing suicide-related and general risk language.

RESULTS

Results indicated significant associations between passive and active suicidal ideation and suicide planning with specific language patterns. Detection of words related to suicidal thoughts and general risk-related words strongly correlated with self-reported suicide risk, with distinct between- and within-person effects highlighting the dynamic nature of suicide risk factors.

CONCLUSIONS

This study demonstrates the feasibility of leveraging smartphone text data for real-time suicide risk detection, offering a scalable, low-burden alternative to traditional methods. Findings suggest that dynamic, individualized monitoring via passive data collection could enhance suicide prevention efforts by enabling timely, tailored interventions. Future research should refine language models and explore diverse populations to extend the generalizability of this innovative approach.

Online disclosure of suicide method: What can online posts tell us about suicidal planning?

Rates of self-disclosure of, and subsequently formal help-seeking for, suicidal thoughts and behaviors (STBs) remains low. However, online discussions of STBs have become increasingly prevalent, creating potential opportunities to identify those at potentially elevated suicide risk (i.e., engaging in suicidal planning). This study used linguistic analysis to examine online STB discussions to identify posts that reference a suicide method, as well as the lethality and timeframe of the identified suicide method. A corpus of 3974 posts from the online forum Reddit SuicideWatch were extracted for data analysis. Approximately one-fifth of analyzed posts mentioned a suicide method; over 35% were classified as severe risk with regard to lethality and almost 70% discussed a suicide method yet to occur. Analyses demonstrated that three categories of words - Substances, Physical, and Past Focus - were associated with increased odds of the presence of a suicide method in a post, whereas five different word categories were associated with a decrease in odds of method presence; most notably was the use of Social words. Together, results support the utility of this approach in detecting discussions of a suicide method. However, they may be less fruitful in distinguishing planning severity among posts including method discussions.

Mindsets of suicide trajectories: An Linguistic Inquiry and Word Count analysis of suicide hotline conversations

OBJECTIVE

The aim of the study was to explore the psychological characteristics of the individuals with various suicide risks using computerized text analysis, in the hopes of a better understanding of suicide trajectories.

METHODS

627 first-time callers' records were randomly selected from Taiwan An-Shin Hotline database between 2013 and 2018. The voice records were evaluated by two psychologists to determine the levels of suicide risk (156 with uncertainty of risk, 177 with low suicidal ideation, 157 with high suicidal ideation, and 137 with suicide preparation/attempt) and transcribed into text. The Linguistic Inquiry and Word Count 2015 (LIWC2015) program combined with Chinese dictionary were then used to calculate the frequency of word categories.

RESULTS

Exploratory factor analysis identified four mindsets of language characteristics, named "opposition and questioning", "active engagement", "negative rumination", and "focus on death". Psychological descriptions of the mindsets were also obtained through correlation analysis with the LIWC2015 categories and indicators. The four mindsets effectively distinguished the callers with different levels of suicide risk.

CONCLUSION

The psychological characteristics of people with various suicide risks can be described and differentiated via the closed-word categories and composite indicators. These results provide useful information for practitioners and researchers.

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study

Background

Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover varied forms of negative attitudes and develop a classification model for predicting negative attitudes as a whole, and then to externally validate its performance on population and individual levels.

Methods

938,866 Weibo posts with relevant keywords were downloaded, including 737,849 posts updated between 2009 and 2014 (2009-2014 dataset), and 201,017 posts updated between 2015 and 2020 (2015-2020 dataset). (1) For model development, based on 10,000 randomly selected posts from 2009 to 2014 dataset, a human-based content analysis was performed to manually determine labels of each post (non-negative or negative attitudes). Then, a computer-based content analysis was conducted to automatically extract psycholinguistic features from each of the same 10,000 posts. Finally, a classification model for predicting negative attitudes was developed on selected features. (2) For model validation, on the population level, the developed model was implemented on remaining 727,849 posts from 2009 to 2014 dataset, and was externally validated by comparing proportions of negative attitudes between predicted and human-coded results. Besides, on the individual level, similar analyses were performed on 300 randomly selected posts from 2015 to 2020 dataset, and the developed model was externally validated by comparing labels of each post between predicted and actual results.

Results

For model development, the F1 and area under ROC curve (AUC) values reached 0.93 and 0.97. For model validation, on the population level, significant differences but very small effect sizes were observed for the whole sample (χ 2 1 = 32.35, p < 0.001; Cramer's V = 0.007, p < 0.001), men (χ 2 1 = 9.48, p = 0.002; Cramer's V = 0.005, p = 0.002), and women (χ 2 1 = 25.34, p < 0.001; Cramer's V = 0.009, p < 0.001). Besides, on the individual level, the F1 and AUC values reached 0.76 and 0.74.

Conclusion

This study demonstrates the efficiency and necessity of machine learning prediction of negative attitudes as a whole, and confirms that external validation is essential before implementing prediction models into practice.

The Association Between Linguistic Characteristics of Physicians' Communication and Their Economic Returns: Mixed Method Study

BACKGROUND

Web-based health care has the potential to improve health care access and convenience for patients with limited mobility, but its success depends on active physician participation. The economic returns of internet-based health care initiatives are an important factor that can motivate physicians to continue their participation. Although several studies have examined the communication patterns and influences of web-based health consultations, the correlation between physicians' communication characteristics and their economic returns remains unexplored.

OBJECTIVE

This study aims to investigate how the linguistic features of 2 modes of physician-patient communication, instrumental and affective, determine the physician's economic returns, measured by the honorarium their patients agree to pay per consultation. We also examined the moderating effects of communication media (web-based text messages and voice messages) and the compounding effects of different communication features on economic returns.

METHODS

We collected 40,563 web-based consultations from 528 physicians across 4 disease specialties on a large, web-based health care platform in China. Communication features were extracted using linguistic inquiry and word count, and we used multivariable linear regression and K-means clustering to analyze the data.

RESULTS

We found that the use of cognitive processing language (ie, words related to insight, causation, tentativeness, and certainty) in instrumental communication and positive emotion-related words in affective communication were positively associated with the economic returns of physicians. However, the extensive use of discrepancy-related words could generate adverse effects. We also found that the use of voice messages for service delivery magnified the effects of cognitive processing language but did not moderate the effects of affective processing language. The highest economic returns were associated with consultations in which the physicians used few expressions related to negative emotion; used more terms associated with positive emotions; and later, used instrumental communication language.

CONCLUSIONS

Our study provides empirical evidence about the relationship between physicians' communication characteristics and their economic returns. It contributes to a better understanding of patient-physician interactions from a professional-client perspective and has practical implications for physicians and web-based health care platform executives.

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.

Extracting time patterns from the lifespans of TikTok challenges to characterize non-dangerous and dangerous ones

One of the key aspects that distinguish TikTok from other social media is the presence of challenges. A challenge is a kind of competition that starts when a user posts a video with certain actions and a certain hashtag and invites other users to replicate the same video in their own way. Most challenges are fun and harmless, but sometimes dangerous challenges are launched as well. The authors of these challenges use various tricks to bypass TikTok’s controls. In this paper, we analyze the lifespans of some TikTok challenges and show how they are very different for non-dangerous and dangerous ones. Then, we deepen our analysis by identifying some time patterns that characterize the two types of challenges. Finally, we test the accuracy of the results obtained on a large set of challenges different from those used during the detection of time patterns. The focus of this paper is the detection of time patterns allowing the classification of challenges in dangerous and non-dangerous ones. This could represent a first step towards an approach for the early detection of dangerous challenges in TikTok.

Bootstrapping semi-supervised annotation method for potential suicidal messages

The suicide of a person is a tragedy that deeply affects families, communities, and countries. According to the standardized rate of suicides per number of inhabitants worldwide, in 2022 there will be approximately about 903,450 suicides and 18,069,000 unconsummated suicides, affecting people of all ages, countries, races, beliefs, social status, economic status, sex, etc. The publication of suicidal intentions by users of social networks has led to the initiation of research processes in this field, to detect them and encourage them not to commit suicide. This study focused on determining a semi-supervised method to populate the Life Corpus, using a bootstrapping technique, to automatically detect and classify texts extracted from social networks and forums related to suicide and depression based on initial supervised samples. To carry out the experiments we used two different classifiers: Support Vector Machine (SVM) (with Bag of Words (BoW) features with and without Term-Frequency/Inverse Document Frequency (Tf/Idf), as a weighted term, and with or without stopwords) and Rasa (with the default feature extraction system). In addition, we performed the experiments using five data collections: Life, Reddit, Life+Reddit, Life_en, and Life_en + Reddit. Using the semi-supervised method, we managed to increase the size of the Life Corpus from 102 to 273 samples with texts from the social network Reddit, in a combination Life+Reddit+BoW_Embeddings, with the SVM classifier, with which a macro f1 value of 0.80 was achieved. These texts were in turn evaluated by annotators manually with a Cohen's Kappa level of agreement of 0.86.

Find Supports for the Post about Mental Issues: More than Semantic Matching

Mental-health-oriented question-answering (MH-QA) aims at retrieving an appropriate response for a question post involving mental health issues, which will be used to assist counsellors to reply the support seeker. This task is different from the general QA task because many additional criteria such as emotions are involved. In this paper, we propose the Multi-Feature Graph Convolutional Network model (MF-GCN) to integrate not only semantic features, but also mental health related features and context features, to match question post and responses. Different types of feature are exploited through multiple graph convolutional networks. A new dataset is constructed for MH-QA to evaluate our model. Experimental results show that our model with mental health features significantly outperforms a wide range of state-of-the-art models without them.

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.

Analytical framework for mental health feature extraction methods in social networks

Today, with the development of internet technology, a new kind of social relations and interactions have been formed in the newly emerged social networks. Through social networks, the users can share different types of content, including personal information, text, image, video, music, poem, and other related information, which express their mental states, emotions, feelings, and thoughts. Thus, a new and essential aspect of human life is being formed in a virtual space in social networks, which must be explored from several viewpoints, such as mental disorders. Analyzing mental disorders according to the social network data can guide us to gain new approaches to improve the public health of the whole society. To this aim, developing mental health feature extraction (MHFE) methods in a social network is essential and is now becoming an active research area. Therefore, in this paper, a review of existing techniques and methods in MHFE is presented, and a comprehensive framework is provided to classify these approaches. Furthermore, to analyze and evaluate each approach in extraction methods, an appropriate set of functional criteria is proposed, which leads to a more accurate understanding and correct use of them.

Automatic detection of depression symptoms in twitter using multimodal analysis

Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user's psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information.

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.

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.

Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users

BACKGROUND

Suicide is a growing global public health problem that has resulted in an increase in the demand for psychological services to address mental health issues. It is expected that 1 in 6 people on a waiting list for mental health services will attempt suicide. Although suicidal ideation has been shown to be linked to a higher risk of death by suicide, not everybody openly discloses their suicidal thoughts or plans to friends and family or seeks professional help before suicide. Therefore, new methods are needed to track suicide risk in real time together with a better understanding of the ways in which people communicate or express their suicidality. Considering the dynamic nature and challenges in understanding suicide ideation and suicide risk, mobile apps could be better suited to prevent suicide as they have the ability to collect real-time data.

OBJECTIVE

This study aims to report the practicalities and acceptability of setting up and trialing digital technologies within an inpatient mental health setting in the United Kingdom and highlight their implications for future studies.

METHODS

Service users were recruited from 6 inpatient wards in the north west of England. Service users who were eligible to participate and provided consent were given an iPhone and Fitbit for 7 days and were asked to interact with a novel phone app, Strength Within Me (SWiM). Interaction with the app involved journaling (recording daily activities, how this made them feel, and rating their mood) and the option to create safety plans for emotions causing difficulties (identifying strategies that helped with these emotions). Participants also had the option to allow the study to access their personal Facebook account to monitor their social media use and activity. In addition, clinical data (ie, assessments conducted by trained researchers targeting suicidality, depression, and sleep) were also collected.

RESULTS

Overall, 43.0% (80/186 response rate) of eligible participants were recruited for the study. Of the total sample, 67 participants engaged in journaling, with the average number of entries per user being 8.2 (SD 8.7). Overall, only 24 participants created safety plans and the most common difficult emotion to be selected was feeling sad (n=21). This study reports on the engagement with the SWiM app, the technical difficulties the research team faced, the importance of building key relationships, and the implications of using Facebook as a source to detect suicidality.

CONCLUSIONS

To develop interventions that can be delivered in a timely manner, prediction of suicidality must be given priority. This paper has raised important issues and highlighted lessons learned from implementing a novel mobile app to detect the risk of suicidality for service users in an inpatient setting.

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.

Assessment of supervised classifiers for the task of detecting messages with suicidal ideation

According to the World Health Organization (WHO) close to 800,000 people worldwide die by suicide each year, and many more attempts to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries but poor and middle-income countries as well. This study makes a systematic analysis of 28 supervised classifiers using different features of the corpus Life to detect messages with suicidal ideation and depression to know if these can be used in an automatic prevention online system. The Life Corpus, used in this research, is a bilingual text corpus (English and Spanish) oriented to the detection of suicide ideation. This corpus was constructed retrieving texts from several social networks and its quality was measured using mutual annotation agreement. The different experiments determined that the classifier with the best performance was KStar, with the corpus features POS-SYNSETS-NUM, achieving the best results with the ROC Area metrics of 0,81036 and F-measure of 0,7148. The present research fulfilled the objective of discovering which supervised classifiers and which features are the most suitable for the automatic classification of messages with suicidal ideation using the Life Corpus. Also, given the imbalance of the results, a new precision measure was developed called the Two-dimensional Accuracy and Recovery Index (GDP), which can provide better results, in unbalanced systems, than the usual measures to assess the quality of the results (measure F, Area ROC), and thus increase the number of messages at risk of suicidal ideation, detected at the cost of receiving more messages that are not related to suicide or vice versa.

Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

BACKGROUND

Depression is a serious personal and public mental health problem. Self-reporting is the main method used to diagnose depression and to determine the severity of depression. However, it is not easy to discover patients with depression owing to feelings of shame in disclosing or discussing their mental health conditions with others. Moreover, self-reporting is time-consuming, and usually leads to missing a certain number of cases. Therefore, automatic discovery of patients with depression from other sources such as social media has been attracting increasing attention. Social media, as one of the most important daily communication systems, connects large quantities of people, including individuals with depression, and provides a channel to discover patients with depression. In this study, we investigated deep-learning methods for depression risk prediction using data from Chinese microblogs, which have potential to discover more patients with depression and to trace their mental health conditions.

OBJECTIVE

The aim of this study was to explore the potential of state-of-the-art deep-learning methods on depression risk prediction from Chinese microblogs.

METHODS

Deep-learning methods with pretrained language representation models, including bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and generalized autoregressive pretraining for language understanding (XLNET), were investigated for depression risk prediction, and were compared with previous methods on a manually annotated benchmark dataset. Depression risk was assessed at four levels from 0 to 3, where 0, 1, 2, and 3 denote no inclination, and mild, moderate, and severe depression risk, respectively. The dataset was collected from the Chinese microblog Weibo. We also compared different deep-learning methods with pretrained language representation models in two settings: (1) publicly released pretrained language representation models, and (2) language representation models further pretrained on a large-scale unlabeled dataset collected from Weibo. Precision, recall, and F1 scores were used as performance evaluation measures.

RESULTS

Among the three deep-learning methods, BERT achieved the best performance with a microaveraged F1 score of 0.856. RoBERTa achieved the best performance with a macroaveraged F1 score of 0.424 on depression risk at levels 1, 2, and 3, which represents a new benchmark result on the dataset. The further pretrained language representation models demonstrated improvement over publicly released prediction models.

CONCLUSIONS

We applied deep-learning methods with pretrained language representation models to automatically predict depression risk using data from Chinese microblogs. The experimental results showed that the deep-learning methods performed better than previous methods, and have greater potential to discover patients with depression and to trace their mental health conditions.

Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study

BACKGROUND

Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally.

OBJECTIVE

This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records.

METHODS

We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit.

RESULTS

K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices.

CONCLUSIONS

Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.

Examining the Self-Harm and Suicide Contagion Effects of the Blue Whale Challenge on YouTube and Twitter: Qualitative Study

BACKGROUND

Research suggests that direct exposure to suicidal behaviors and acts of self-harm through social media may increase suicidality through imitation and modeling, particularly in more vulnerable populations. One example of a social media phenomenon that demonstrates how self-harming behavior could potentially be propagated is the blue whale challenge. In this challenge, adolescents and young adults are encouraged to engage in self-harm and eventually kill themselves.

OBJECTIVE

This paper aimed to investigate the way individuals portray the blue whale challenge on social media, with an emphasis on factors that could pose a risk to vulnerable populations.

METHODS

We first used a thematic analysis approach to code 60 publicly posted YouTube videos, 1112 comments on those videos, and 150 Twitter posts that explicitly referenced the blue whale challenge. We then deductively coded the YouTube videos based on the Suicide Prevention Resource Center (SPRC) safe messaging guidelines as a metric for the contagion risk associated with each video.

RESULTS

The thematic analysis revealed that social media users post about the blue whale challenge to raise awareness and discourage participation, express sorrow for the participants, criticize the participants, or describe a relevant experience. The deductive coding of the YouTube videos showed that most of the videos violated at least 50% of the SPRC safe and effective messaging guidelines.

CONCLUSIONS

These posts might have the problematic effect of normalizing the blue whale challenge through repeated exposure, modeling, and reinforcement of self-harming and suicidal behaviors, especially among vulnerable populations such as adolescents. More effort is needed to educate social media users and content generators on safe messaging guidelines and factors that encourage versus discourage contagion effects.

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 Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media

Live-stream suicide has become an emerging public health problem in many countries. Regular users are often the first to witness and respond to such suicides, emphasizing their impact on the success of crisis intervention. In order to reduce the likelihood of suicide deaths, this paper aims to use psycholinguistic analysis methods to facilitate automatic detection of negative expressions in responses to live-stream suicides on social media. In this paper, a total of 7212 comments posted on suicide-related messages were collected and analyzed. First, a content analysis was performed to investigate the nature of each comment (negative or not). Second, the simplified Chinese version of the LIWC software was used to extract 75 psycholinguistic features from each comment. Third, based on 19 selected key features, four classification models were established to differentiate between comments with and without negative expressions. Results showed that 19.55% of 7212 comments were recognized as "making negative responses". Among the four classification models, the highest values of Precision, Recall, F-Measure, and Screening Efficacy reached 69.8%, 85.9%, 72.9%, and 47.1%, respectively. This paper confirms the need for campaigns to reduce negative responses to live-stream suicides and support the use of psycholinguistic analysis methods to improve suicide prevention efforts.

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.

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.

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.

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.

Detecting depression stigma on social media: A linguistic analysis

BACKGROUND

Efficient detection of depression stigma in mass media is important for designing effective stigma reduction strategies. Using linguistic analysis methods, this paper aims to build computational models for detecting stigma expressions in Chinese social media posts (Sina Weibo).

METHODS

A total of 15,879 Weibo posts with keywords were collected and analyzed. First, a content analysis was conducted on all 15,879 posts to determine whether each of them reflected depression stigma or not. Second, using four algorithms (Simple Logistic Regression, Multilayer Perceptron Neural Networks, Support Vector Machine, and Random Forest), two groups of classification models were built based on selected linguistic features; one for differentiating between posts with and without depression stigma, and one for differentiating among posts with three specific types of depression stigma.

RESULTS

First, 967 of 15,879 posts (6.09%) indicated depression stigma. 39.30%, 15.82%, and 14.99% of them endorsed the stigmatizing view that "People with depression are unpredictable", "Depression is a sign of personal weakness", and "Depression is not a real medical illness", respectively. Second, the highest F-Measure value for differentiating between stigma and non-stigma reached 75.2%. The highest F-Measure value for differentiating among three specific types of stigma reached 86.2%.

LIMITATIONS

Due to the limited and imbalanced dataset of Chinese Weibo posts, the findings of this study might have limited generalizability.

CONCLUSIONS

This paper confirms that incorporating linguistic analysis methods into online detection of stigma can be beneficial to improve the performance of stigma reduction programs.

Mining social media data: How are research sponsors and researchers addressing the ethical challenges?

Background:

Data representing people’s behaviour, attitudes, feelings and relationships are increasingly being harvested from social media platforms and re-used for research purposes. This can be ethically problematic, even where such data exist in the public domain. We set out to explore how the academic community is addressing these challenges by analysing a national corpus of research ethics guidelines and published studies in one interdisciplinary research area.

Methods:

Ethics guidelines published by Research Councils UK (RCUK), its seven-member councils and guidelines cited within these were reviewed. Guidelines referring to social media were classified according to published typologies of social media research uses and ethical considerations for social media mining. Using health research as an exemplar, PubMed was searched to identify studies using social media data, which were assessed according to their coverage of ethical considerations and guidelines.

Results:

Of the 13 guidelines published or recommended by RCUK, only those from the Economic and Social Research Council, the British Psychological Society, the International Association of Internet Researchers and the National Institute for Health Research explicitly mentioned the use of social media. Regarding data re-use, all four mentioned privacy issues but varied with respect to other ethical considerations. The PubMed search revealed 156 health-related studies involving social media data, only 50 of which mentioned ethical concepts, in most cases simply stating that they had obtained ethical approval or that no consent was required. Of the nine studies originating from UK institutions, only two referred to RCUK ethics guidelines or guidelines cited within these.

Conclusions:

Our findings point to a deficit in ethical guidance for research involving data extracted from social media. Given the growth of studies using these new forms of data, there is a pressing need to raise awareness of their ethical challenges and provide actionable recommendations for ethical research practice.

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

BACKGROUND

Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention.

OBJECTIVE

The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media.

METHODS

A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors.

RESULTS

A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC.

CONCLUSIONS

SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life.

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach

OBJECTIVE

Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide.

METHODS

We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models.

RESULTS

All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71.

CONCLUSION

Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Background

Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

Objective

The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

Methods

We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

Results

The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

Conclusions

Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

BACKGROUND

One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time.

OBJECTIVE

Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population.

METHODS

Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk.

RESULTS

Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%).

CONCLUSIONS

Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

Suicide Communication on Social Media and Its Psychological Mechanisms: An Examination of Chinese Microblog Users

BACKGROUND

This study aims to examine the characteristics of people who talk about suicide on Chinese microblogs (referred to as Weibo suicide communication (WSC)), and the psychological antecedents of such behaviors.

METHODS

An online survey was conducted on Weibo users. Differences in psychological and social demographic characteristics between those who exhibited WSC and those who did not were examined. Three theoretical models were proposed to explain the psychological mechanisms of WSC and their fitness was examined by Structural Equation Modeling (SEM).

RESULTS

12.03% of our respondents exhibited WSC in the past 12 months. The WSC group was significantly younger and less educated, preferred using blogs and online forums for expressing themselves, and reported significantly greater suicide ideation, negative affectivity, and vulnerable personality compared to non-WSC users. SEM examinations found that Weibo users with higher negative affectivity or/and suicidal ideation, who were also using blogs and forums more, exhibited a significantly higher possibility of WSC.

CONCLUSION

Weibo users who are at greater suicide risk are more likely to talk about suicide on Weibo. WSC is a sign of negative affectivity or suicide ideation, and should be responded to with emotional support and suicide prevention services.