Tracking the Werther Effect on social media: Emotional responses to prominent suicide deaths on twitter and subsequent increases in suicide

Does suicide contagion (Werther effect) take place in response to social media? A systematic review

INTRODUCTION

The Werther, Copycat or contagion effect of suicidal behaviour is a complex phenomenon that can arise due to exposure to media stories in which identifiable people take their lives. On the contrary, the Papageno effect prevents people from suicide by promoting positives examples of suicidal crisis management. Impact of both effects has been widely studied in different types of situations, but its existence in social media is a source of much debate.

METHODS

A systematic search following the PRISMA guidelines of PubMed, Scopus, Embase, PsycInfo, Web of Science and the references of prior reviews yielded 25 eligible studies.

RESULTS

Most of the studies found were observational, with very different methodologies and generally with low risk of bias. In these, the results suggest the existence of the Werther effect in response to social media stories about suicide. This is mediated by multiple factors, including the characteristic of the users, the type of interaction and the content of the publications. At the same time, the Papageno effect is also described. Evidence found by type of social media and future implications are discussed.

CONCLUSION

Suicidal content on social media can be both contagious and protective. It is confirmed that the Werther and Papageno effects may occur in response to social media, so they could be an interesting target for preventive interventions.

A machine learning approach to detect potentially harmful and protective suicide-related content in broadcast media

Suicide-related media content has preventive or harmful effects depending on the specific content. Proactive media screening for suicide prevention is hampered by the scarcity of machine learning approaches to detect specific characteristics in news reports. This study applied machine learning to label large quantities of broadcast (TV and radio) media data according to media recommendations reporting suicide. We manually labeled 2519 English transcripts from 44 broadcast sources in Oregon and Washington, USA, published between April 2019 and March 2020. We conducted a content analysis of media reports regarding content characteristics. We trained a benchmark of machine learning models including a majority classifier, approaches based on word frequency (TF-IDF with a linear SVM) and a deep learning model (BERT). We applied these models to a selection of more simple (e.g., focus on a suicide death), and subsequently to putatively more complex tasks (e.g., determining the main focus of a text from 14 categories). Tf-idf with SVM and BERT were clearly better than the naive majority classifier for all characteristics. In a test dataset not used during model training, F1-scores (i.e., the harmonic mean of precision and recall) ranged from 0.90 for celebrity suicide down to 0.58 for the identification of the main focus of the media item. Model performance depended strongly on the number of training samples available, and much less on assumed difficulty of the classification task. This study demonstrates that machine learning models can achieve very satisfactory results for classifying suicide-related broadcast media content, including multi-class characteristics, as long as enough training samples are available. The developed models enable future large-scale screening and investigations of broadcast media.

The impact of media reporting of suicides on subsequent suicides in Asia: A systematic review

Introduction

This systematic review is aimed at (1) evaluating the association between media portrayals of suicides and subsequent copycat suicides or attempts among the general public in Asia, (2) understanding the factors associated with copycat suicides and (3) determining the positive impacts of the media reporting of suicides (e.g. increased help-seeking, coping).

Method

A systematic review and narrative synthesis of English and Chinese articles from 8 electronic databases (i.e. PsycINFO, MEDLINE, Embase, CINAHL, Web of Science, Ariti, China National Knowledge Infrastructure and OpenGrey) from January 2000 to May 2023 was conducted. Observational studies were included, and the data were analysed through narrative synthesis. The protocol was registered with PROSPERO (CRD42021281535).

Results

Among the 32 studies included (n=29 for evidence synthesis) in the review, there is good-quality evidence to show that copycat suicides and suicide attempts increase after media reports of a suicide, regardless of country, celebrity status, study design, type of media, mode of suicide or follow-up period. Females, younger age groups and those sharing similar characteristics as the deceased in publicised suicides (age, gender) were more susceptible to negative impact. Reporting of the mode of death of the deceased increased suicides by the same method among the public.

Conclusion

Media portrayals of suicide appear to have a negative impact on copycat suicides at the population level in Asia. Thus, in addition to tighter media control, healthcare systems, professional medical bodies and community outreach services should work collaboratively to promote early help-seeking in those with psychological distress.

Factors associated with suicide risk among Brazilian graduate students during the COVID-19 pandemic

Though pandemic-related suicides are a concern, little is known about factors potentially linking graduate student life and suicide risk. This study identified factors associated with suicide risk among Brazilian graduate students (N = 5,344) during the COVID-19 pandemic. Utilizing the Mini International Neuropsychiatric Interview, this study revealed that 31.5% of participants presented some risk for suicide: 16.6% "low risk," 4.7% "moderate risk," and 10.2% "high risk." Higher income and religious affiliation were identified as protective factors. Identified risk factors encompass non-heterosexual orientation, a history of depression or posttraumatic stress or common mental disorders diagnoses, the use of medications-both general and psychopharmaceuticals-without medical prescription, antipsychotics use, alcohol consumption, lack of health insurance, and dissatisfaction with life as a result of accessing social media networks. The high vulnerability of graduate students to suicide risk highlights the need for institutional suicide prevention initiatives.

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.

Association of 7 million+ tweets featuring suicide-related content with daily calls to the Suicide Prevention Lifeline and with suicides, United States, 2016-2018

OBJECTIVE

The aim of this study was to assess associations of various content areas of Twitter posts with help-seeking from the US National Suicide Prevention Lifeline (Lifeline) and with suicides.

METHODS

We retrieved 7,150,610 suicide-related tweets geolocated to the United States and posted between 1 January 2016 and 31 December 2018. Using a specially devised machine-learning approach, we categorized posts into content about prevention, suicide awareness, personal suicidal ideation without coping, personal coping and recovery, suicide cases and other. We then applied seasonal autoregressive integrated moving average analyses to assess associations of tweet categories with daily calls to the US National Suicide Prevention Lifeline (Lifeline) and suicides on the same day. We hypothesized that coping-related and prevention-related tweets are associated with greater help-seeking and potentially fewer suicides.

RESULTS

The percentage of posts per category was 15.4% (standard deviation: 7.6%) for awareness, 13.8% (standard deviation: 9.4%) for prevention, 12.3% (standard deviation: 9.1%) for suicide cases, 2.4% (standard deviation: 2.1%) for suicidal ideation without coping and 0.8% (standard deviation: 1.7%) for coping posts. Tweets about prevention were positively associated with Lifeline calls (B = 1.94, SE = 0.73, p = 0.008) and negatively associated with suicides (B = -0.11, standard error = 0.05, p = 0.038). Total number of tweets were negatively associated with calls (B = -0.01, standard error  = 0.0003, p = 0.007) and positively associated with suicide, (B = 6.4 × 10-5, standard error  = 2.6 × 10-5, p = 0.015).

CONCLUSION

This is the first large-scale study to suggest that daily volume of specific suicide-prevention-related social media content on Twitter corresponds to higher daily levels of help-seeking behaviour and lower daily number of suicide deaths.

PREREGISTRATION

As Predicted, #66922, 26 May 2021.

Emotional Distress During COVID-19 due to Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm

BACKGROUND

Monitoring the psychological conditions of social media users during rapidly developing public health crises, such as the COVID-19 pandemic, using their posts on social media has rapidly gained popularity as a relatively easy and cost-effective method. However, the characteristics of individuals who created these posts are largely unknown, making it difficult to identify groups of individuals most affected by such crises. In addition, large annotated data sets for mental health conditions are not easily available, and thus, supervised machine learning algorithms can be infeasible or too costly.

OBJECTIVE

This study proposes a machine learning framework for the real-time surveillance of mental health conditions that does not require extensive training data. Using survey-linked tweets, we tracked the level of emotional distress during the COVID-19 pandemic by the attributes and psychological conditions of social media users in Japan.

METHODS

We conducted online surveys of adults residing in Japan in May 2022 and collected their basic demographic information, socioeconomic status, and mental health conditions, along with their Twitter handles (N=2432). We computed emotional distress scores for all the tweets posted by the study participants between January 1, 2019, and May 30, 2022 (N=2,493,682) using a semisupervised algorithm called latent semantic scaling (LSS), with higher values indicating higher levels of emotional distress. After excluding users by age and other criteria, we examined 495,021 (19.85%) tweets generated by 560 (23.03%) individuals (age 18-49 years) in 2019 and 2020. We estimated fixed-effect regression models to examine their emotional distress levels in 2020 relative to the corresponding weeks in 2019 by the mental health conditions and characteristics of social media users.

RESULTS

The estimated level of emotional distress of our study participants increased in the week when school closure started (March 2020), and it peaked at the beginning of the state of emergency (estimated coefficient=0.219, 95% CI 0.162-0.276) in early April 2020. Their level of emotional distress was unrelated to the number of COVID-19 cases. We found that the government-induced restrictions disproportionately affected the psychological conditions of vulnerable individuals, including those with low income, precarious employment, depressive symptoms, and suicidal ideation.

CONCLUSIONS

This study establishes a framework to implement near-real-time monitoring of the emotional distress level of social media users, highlighting a great potential to continuously monitor their well-being using survey-linked social media posts as a complement to administrative and large-scale survey data. Given its flexibility and adaptability, the proposed framework is easily extendable for other purposes, such as detecting suicidality among social media users, and can be used on streaming data for continuous measurement of the conditions and sentiment of any group of interest.

Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach

Background

Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are lacking. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of the content posted on these platforms.

Objective

This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention.

Methods

We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multiclass and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (term frequency-inverse document frequency with a linear support vector machine) and 2 state-of-the-art deep learning models (Bidirectional Encoder Representations from Transformers [BERT] and XLNet). The first task classified posts into 6 main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either suicidal ideation and attempts or coping and recovery, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these 5 categories. The second classification task was binary and separated posts in the 11 categories referring to actual suicide from posts in the off-topic category, which use suicide-related terms in another meaning or context.

Results

In both tasks, the performance of the 2 deep learning models was very similar and better than that of the majority or the word frequency classifier. BERT and XLNet reached accuracy scores above 73% on average across the 6 main categories in the test set and F₁-scores between 0.69 and 0.85 for all but the suicidal ideation and attempts category (F₁=0.55). In the binary classification task, they correctly labeled around 88% of the tweets as about suicide versus off-topic, with BERT achieving F₁-scores of 0.93 and 0.74, respectively. These classification performances were similar to human performance in most cases and were comparable with state-of-the-art models on similar tasks.

Conclusions

The achieved performance scores highlight machine learning as a useful tool for media effects research on suicide. The clear advantage of BERT and XLNet suggests that there is crucial information about meaning in the context of words beyond mere word frequencies in tweets about suicide. By making data labeling more efficient, this work has enabled large-scale investigations on harmful and protective associations of social media content with suicide rates and help-seeking behavior.

Differences in Suicide-Related Twitter Content According to User Influence

Background: The content of suicide-specific social media posts may impact suicide rates, and putatively harmful and/or protective content may vary by the author's influence. Aims: This study sought to characterize how suicide-related Twitter content differs according to user influence. Method: Suicide-related tweets from July 1, 2015, to June 1, 2016, geolocated to Toronto, Canada, were collected and randomly selected for coding (n = 2,250) across low, medium, or high user influence levels (based on the number of followers, tweets, retweets, and posting frequency). Logistic regression was used to identify differences by user influence for various content variables. Results: Low- and medium-influence users typically tweeted about personal experiences with suicide and associations with mental health and shared morbid humor/flippant tweets. High-influence users tended to tweet about suicide clusters, suicide in youth, older adults, indigenous people, suicide attempts, and specific methods. Tweets across influence levels predominantly focused on suicide deaths, and few described suicidal ideation or included helpful content. Limitations: Social media data were from a single location and epoch. Conclusion: This study demonstrated more problematic content vis-à-vis safe suicide messaging in tweets by high-influence users and a paucity of protective content across all users. These results highlight the need for further research and potential intervention.

Problematic Use of the Internet Mediates the Association between Reduced Mentalization and Suicidal Ideation: A Cross-Sectional Study in Young Adults

Suicide is a major public health problem, and it is urgent to investigate its underlying clinical and psychological concomitants. It has been suggested that low mentalization skills and problematic use of the internet (PUI) are factors that can play a role in suicidal behaviors. It is possible that poor mentalization skills contribute to leading to forms of PUI, which, in turn, can represent triggers for suicidal ideation (SI). We tested this hypothesis through a quantitative and cross-sectional study on a sample (n = 623) of young adults (age range: 18−34). Self-report measures investigating symptoms related to Social Media Addiction (SMA), Internet Gaming Disorder (IGD), mentalization capacity, and SI were used. A single mediation analysis with two mediators was carried out to evaluate the direct and indirect effects of mentalization on SI through the mediating role of SMA- and IGD-related symptoms, controlling for potential confounding factors (e.g., socio-demographic and addiction-related variables). The four explored variables were significantly associated with each other (all p < 0.001) across all subjects; the mediational model showed that the total effect of mentalization on SI was significant (B = −0.821, SE = 0.092 (95% CI: −1.001; −0.641)) and that both SMA- (B = −0.073, SE = 0.034 (95% CI: −0.145; −0.008)) and IGD-related symptoms (B = 0.046, SE = 0.027 (95% CI: −0.107; −0.001)) were significant mediators of such association. Our findings support the possibility that PUI severity plays a relevant role in mediating the association between low mentalization skills and levels of SI.

Social media monitoring of suicidal content and change in trends of Japanese twitter content around the Zama suicide pact slayings

Social media-related suicides remain a serious issue despite efforts to address this problem. On October 31st, 2017, the Zama Suicide Pact Slayings were reported in which a criminal targeted victims who expressed suicidal ideation using social media. We analyzed how communication on Twitter was used concerning suicidal ideation in relation to the slayings. We extracted data from 1,246 Twitter accounts using the hashtag "#I_want_to_die" between October 1st to November 30th, 2017. We performed thematic content analysis to identify the characteristics of these Twitter accounts and their tweets. The number and categories of related posts from before and after the slayings were compared. Relevant online communication increased from 159 to 1,037 Twitter accounts after the incident. Before the incident, most accounts had tweets related to suicide, mental health issues, or sought to make connections. After the incident, most tweets from these accounts were related to opinions (especially offensive ones) or non-prevention-oriented advertisements. The results suggest that the number of accounts tweeting suicidal-related themes decreased after the homicides, while the number of accounts posting offensive opinions and non-preventive advertisements increased. This implies that the efficacy of social media-based prevention measures may be undermined by this content.

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.

Reasons for Suicide During the COVID-19 Pandemic in Japan

IMPORTANCE

Although the suicide rate in Japan increased during the COVID-19 pandemic, the reasons for suicide have yet to be comprehensively investigated.

OBJECTIVE

To assess which reasons for suicide had rates that exceeded the expected number of suicide deaths for that reason during the COVID-19 pandemic.

DESIGN, SETTING, AND PARTICIPANTS

This national, population-based cross-sectional study of data on suicides gathered by the Ministry of Health, Labor, and Welfare from January 2020 to May 2021 used a times-series analysis on the numbers of reason-identified suicides. Data of decedents were recorded by the National Police Agency and compiled by the Ministry of Health, Labor, and Welfare.

EXPOSURE

For category analysis, we compared data from January 2020 to May 2021 with data from December 2014 to June 2020. For subcategory analysis, data from January 2020 to May 2021 were compared with data from January 2019 to June 2020.

MAIN OUTCOMES AND MEASURES

The main outcome was the monthly excess suicide rate, ie, the difference between the observed number of monthly suicide deaths and the upper bound of the 1-sided 95% CI for the expected number of suicide deaths in that month. Reasons for suicide were categorized into family, health, economy, work, relationships, school, and others, which were further divided into 52 subcategories. A quasi-Poisson regression model was used to estimate the expected number of monthly suicides. Individual regression models were used for each of the 7 categories, 52 subcategories, men, women, and both genders.

RESULTS

From the 29 938 suicides (9984 [33.3%] women; 1093 [3.7%] aged <20 years; 3147 [10.5%] aged >80 years), there were 21 027 reason-identified suicides (7415 [35.3%] women). For both genders, all categories indicated monthly excess suicide rates, except for school in men. October 2020 had the highest excess suicide rates for all cases (observed, 1577; upper bound of 95% CI for expected number of suicides, 1254; 25.8% greater). In men, the highest monthly excess suicide rate was 24.3% for the other category in August 2020 (observed, 87; upper bound of 95% CI for expected number, 70); in women, it was 85.7% for school in August 2020 (observed, 26; upper bound of 95% CI for expected number, 14).

CONCLUSIONS AND RELEVANCE

In this study, observed suicides corresponding to all 7 categories of reasons exceeded the monthly estimates (based on data from before or during the COVID-19 pandemic), except for school-related reasons in men. This study can be used as a basis for developing intervention programs for suicide prevention.

A Pilot Case-Control Study of the Social Media Activity Following Cluster and Non-Cluster Suicides in Australia

Social media may play a role in the "contagion" mechanism thought to underpin suicide clusters. Our pilot case-control study presented a novel methodological approach to examining whether Facebook activity following cluster and non-cluster suicides differed. We used a scan statistic to identify suicide cluster cases occurring in spatiotemporal clusters and matched each case to 10 non-cluster control suicides. We identified the Facebook accounts of 3/48 cluster cases and 20/480 non-cluster controls and their respective friends-lists and retrieved 48 posthumous posts and replies (text segments) referring to the deceased for the former and 606 for the latter. We examined text segments for "putatively harmful" and "putatively protective" content (e.g., discussion of the suicide method vs. messages discouraging suicidal acts). We also used concept mapping, word-emotion association, and sentiment analysis and gauged user reactions to posts using the reactions-to-posts ratio. We found no "putatively harmful" or "putatively protective" content following any suicides. However, "family" and "son" concepts were more common for cluster cases and "xx", "sorry" and "loss" concepts were more common for non-cluster controls, and there were twice as many surprise- and disgust-associated words for cluster cases. Posts pertaining to non-cluster controls were four times as receptive as those about cluster cases. We hope that the approach we have presented may help to guide future research to explain suicide clusters and social-media contagion.

Appropriate Strategies for Reducing the Negative Impact of Online Reports of Suicide and Public Opinion From Social Media in China

Suicide events may have a negative impact on all of society. The media plays a significant role in suicide prevention. Therefore, the aims of this study are (a) to understand the association between characteristics of suicide events and characteristics of who committed suicide, and event impact indexes (EIIs) of suicide reported on the internet; (b) to analyze violation of recommendations for reporting suicide by Weibo, and (c) to investigate the effect of online reports of suicide on public opinion. We carried out a content analysis of online reports of suicide. This study analyzed 113 suicide events, 300 news reports of suicide, and 2,654 Weibo comments about suicide collected from the WeiboReach between 2015 and 2020. We used a t-test and analysis of variance (ANOVA) to explore the potential factors associated with the EIIs of suicide events. The results found that (a) The suicide events reported on the internet during COVID-19 and those related to celebrities and students tend to have higher EIIs; (b) suicide reports on Weibo frequently violated WHO recommendations for suicide reporting in the media; and (c) public opinion of suicide reporting in the online media was mostly emotional and irrational, which is not beneficial for public mental health and suicide prevention. In conclusion, first, the situation of many people working from home or studying from home and spreading more time online during COVID-19 may lead to suicide events obtain more public attention. Online media could further improve public responsible reporting and daily media-content surveillance, especially taking particular care in those suicide events during COVID-19, and related to celebrities and students, which may have a higher event impact on the internet. Second, health managers should regular assessment of observance of the WHO recommendations for suicide reporting by online social media to prevent suicide. Third, health communication managers should use big data to identify, assess, and manage harmful information about suicide; and track anyone affected by suicide-related reports on social media to reduce the negative impact of public opinion to intervene suicide in the early stage of suicide.

Time-series analyses of firearm-related Google searches and U.S. suicide rates 2004-2016

OBJECTIVE

The U.S. suicide rate has increased 35% since 1999. The role of the Internet has not been thoroughly investigated despite Internet use more than doubling from 1999 to present. The majority of U.S. suicide deaths are by firearm; however, there is no examination of the association between trends in firearm Internet searches and overall and firearm monthly suicide rates. We hypothesized that search strings related to firearm suicide would be significantly associated with monthly suicide rates (both all methods and firearm).

METHODS

Google Trends provides data on request frequencies of searches. Twenty-four search strings were examined representing possible searches by individuals considering firearm suicide and compared to U.S. suicide rates with time-series modeling.

RESULTS

In the time series with higher search volumes, consistent associations were found of negative cross-correlation at lag +1.

CONCLUSIONS

Several searches appeared at least sensitive enough to consistently show associations with overall and firearm suicide rates in the following month. This novel finding should be followed up as the potential exists to predict suicide trends.

Suicide-related Twitter Content in Response to a National Mental Health Awareness Campaign and the Association between the Campaign and Suicide Rates in Ontario

OBJECTIVE

Mental health awareness (MHA) campaigns have been shown to be successful in improving mental health literacy, decreasing stigma, and generating public discussion. However, there is a dearth of evidence regarding the effects of these campaigns on behavioral outcomes such as suicides. Therefore, the objective of this article is to characterize the association between the event and suicide in Canada's most populous province and the content of suicide-related tweets referencing a Canadian MHA campaign (Bell Let's Talk Day [BLTD]).

METHODS

Suicide counts during the week of BTLD were compared to a control window (2011 to 2016) to test for associations between the BLTD event and suicide. Suicide tweets geolocated to Ontario, posted in 2016 with the BLTD hashtag were coded for specific putatively harmful and protective content.

RESULTS

There was no associated change in suicide counts. Tweets (n = 3,763) mainly included content related to general comments about suicide death (68%) and suicide being a problem (42.8%) with little putatively helpful content such as stories of resilience (0.6%) and messages of hope (2.2%).

CONCLUSIONS

In Ontario, this national mental health media campaign was associated with a high volume of suicide-related tweets but not necessarily including content expected to diminish suicide rates. Campaigns like BLTD should strongly consider greater attention to suicide-related messaging that promotes help-seeking and resilience. This may help to further decrease stigmatization, and potentially, reduce suicide rates.

Suicide and mental health during the COVID-19 pandemic in Japan

Background

The coronavirus disease (COVID-19) pandemic is an unprecedented public health crisis, but its effect on suicide deaths is little understood.

Methods

We analyzed data from monthly suicide statistics between January 2017 and October 2020 and from online surveys on mental health filled out by the general population in Japan.

Results

Compared to the 2017–19 period, the number of suicide deaths during the initial phase of the pandemic was lower than average but exceeded the past trend from July 2020. Female suicides, whose numbers increased by approximately 70% in October 2020 (incidence rate ratio: 1.695, 95% confidence interval: 1.558–1.843), were the main source of this increase. The largest increase was found among young women (less than 40 years of age). Our survey data indicated that the status of young women’s mental health has been deteriorating in recent months and that young female workers were more likely to have experienced a job or income loss than any other group, suggesting adverse economic conditions surrounding them.

Conclusions

Continuous monitoring of mental health, particularly that of the most vulnerable populations identified in this study, and appropriate suicide prevention efforts are necessary during the COVID-19 pandemic.

It's good to talk, but it matters how we do it'

Rosie Allister explains that although talking about suicide can be a good thing and can create opportunities to help, it must be done in a responsible way so that it doesn't cause further harm.

Troubling content: Guiding discussion of death by suicide on social media

Growing concerns about "online harm" and "duty of care" fuel debate about how best to regulate and moderate "troubling content" on social media. This has become a pressing issue in relation to the potential application of media guidelines to online discussion of death by suicide-discussion which is troubling because it is often transgressive and contested. Drawing on an innovative mixed-method analysis of a large-scale Twitter dataset, this article explores in depth, for the first time, the complexities of applying existing media guidelines on reporting death by suicide to online contexts. By focusing on five highly publicised deaths, it illustrates the limits of this translation but also the significance of empathy (its presence and absence) in online accounts of these deaths. The multi-relational and politicised nature of empathy, and the polarised nature of Twitter debate, suggests that we need to step back from calls for the automatic application of guidelines produced in a pre-digital time to understand more about the sociocultural context of how suicide is discussed on social media. This stepping back matters because social media is now a key part of how lives and deaths are deemed grievable and deserving of our attention.

The association between Twitter content and suicide

OBJECTIVE

A growing body of research has established that specific elements of suicide-related news reporting can be associated with increased or decreased subsequent suicide rates. This has not been systematically investigated for social media. The aim of this study was to identify associations between specific social media content and suicide deaths.

METHODS

Suicide-related tweets (n = 787) geolocated to Toronto, Canada and originating from the highest level influencers over a 1-year period (July 2015 to June 2016) were coded for general, putatively harmful and putatively protective content. Multivariable logistic regression was used to examine whether tweet characteristics were associated with increases or decreases in suicide deaths in Toronto in the 7 days after posting, compared with a 7-day control window.

RESULTS

Elements independently associated with increased subsequent suicide counts were tweets about the suicide of a local newspaper reporter (OR = 5.27, 95% CI = [1.27, 21.99]), 'other' social causes of suicide (e.g. cultural, relational, legal problems; OR = 2.39, 95% CI = [1.17, 4.86]), advocacy efforts (OR = 2.34, 95% CI = [1.48, 3.70]) and suicide death (OR = 1.52, 95% CI = [1.07, 2.15]). Elements most strongly independently associated with decreased subsequent suicides were tweets about murder suicides (OR = 0.02, 95% CI = [0.002, 0.17]) and suicide in first responders (OR = 0.17, 95% CI = [0.05, 0.52]).

CONCLUSIONS

These findings largely comport with the theory of suicide contagion and associations observed with traditional news media. They specifically suggest that tweets describing suicide deaths and/or sensationalized news stories may be harmful while those that present suicide as undesirable, tragic and/or preventable may be helpful. These results suggest that social media is both an important exposure and potential avenue for intervention.

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.

A machine learning approach predicts future risk to suicidal ideation from social media data

Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10-71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

The Relationship Between Suicide-Related Twitter Events and Suicides in Ontario From 2015 to 2016

Background: Many studies have demonstrated suicide contagion through mainstream journalism; however, few have explored suicide-related social media events and their potential relationship to suicide deaths. Aims: To determine whether Twitter events were associated with changes in subsequent suicides. Methods: Suicide-related Twitter events that garnered at least 100 tweets originating in Ontario, Canada (July 1, 2015 to June 30, 2016) were identified and characterized as putatively "harmful" or "innocuous" based on recommendations for responsible media reporting. The number of suicides in Ontario during the peak of each Twitter event and the subsequent 6 days ("exposure window") was compared with suicides occurring during a pre-event period of the same length ("control window"). Results: There were 17 suicide-related Twitter events during the period of study (12 putatively harmful and five putatively innocuous). The number of tweets per event ranged from 121 for "physician-assisted suicide law in Quebec" to 6,202 for the "Attawapiskat suicide crisis." No significant relationship was detected between Twitter events and actual suicides. Notably, a comprehensive examination of the details of Twitter events showed that even the putatively harmful events lacked many of the characteristics commonly associated with contagion. Limitations: This was an uncontrolled experiment in only one epoch and a single Canadian province. Discussion: This study found no evidence of suicide contagion associated with Twitter events. This finding must be interpreted with caution given the relatively innocuous content of suicide-related Tweets in Ontario during 2015-2016.

Suicide reporting in mass media in the state of Magdalena, Colombia

In 2016 and 2017, the suicide rate in Colombia went up from 5.20 to 5.72, respectively. The same trend was observed for the State of Magdalena for the same period with an increase in the rates that went from 3.37 in 2016 and 4.27 per one hundred thousand inhabitants in 20171,2. The suicide rate in the State of Magdalena occupies third place among the departments of the Colombian Caribbean region1

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Suicide, Stigma, and Utilizing Social Media Platforms to Gauge Public Perceptions

Introduction: Suicide, a multifaceted complex outcome that arises from numerous biopsychosocial factors, is a public health concern which is growing in numbers despite valiant prevention efforts. There is still a lot of stigma surrounding suicide that needs to be addressed. Social media is growing exponentially and there are many forums where suicidality is being discussed. As a result, we conducted a brief survey on the perception of suicide on social media platforms of Facebook and Reddit in order to gather more information. Results: Of the 152 respondents, 86% believed that suicide is preventable, and 72.85% believed that it is a person's right to die by suicide. About a third (31.79%) had lost someone close to them to suicide. Respondents who did not think suicide was preventable also viewed suicide as either a sign of strength (42.86%) or a revenge act (33.3%). Those who responded that someone close to them died by suicide believed that the media glorified suicide (56.25%) while those who did not lose someone, did not believe that (66.99%). Women (61%) found social media to be a good platform for people to ask for help while men did not (60.61%). Conclusions: We utilized the social media platforms to gauge the perception of suicide and found among the sample of mostly young white respondents, suicide is not stigmatized, most believed it is preventable and it is a person's right to die by suicide. While women found social media to be a good platform to ask for support, men did not, which is in keeping with the trend that women tend to be more willing to seek help. A third of the group had lost someone close to them to suicide which was the national average, who tended to believe that media glorified suicide. Limitations of this study include the fact that those who respond voluntarily to a survey likely have an interest in the topic, and this might not accurately reflect the public opinion and attitude.