Bored to death: Artificial Intelligence research reveals the role of boredom in suicide behavior.

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Methods: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusion: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

Deep neural networks detect suicide risk from textual facebook posts.

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.

Participant carelessness and fraud: Consequences for clinical research and potential solutions.

Clinical psychological research studies often require individuals with specific characteristics. The Internet can be used to recruit broadly, enabling the recruitment of rare groups such as people with specific psychological disorders. However, Internet-based research relies on participant self-report to determine eligibility, and thus, data quality depends on participant honesty. For those rare groups, even low levels of participant dishonesty can lead to a substantial proportion of fraudulent survey responses, and all studies will include careless respondents who do not pay attention to questions, do not understand them, or provide intentionally wrong responses. Poor-quality responses should be thought of as categorically different from high-quality responses. Including these responses will lead to the overestimation of the prevalence of rare groups and incorrect estimates of scale reliability, means, and correlations between constructs. We demonstrate that for these reasons, including poor-quality responses-which are usually positively skewed-will lead to several data-quality problems including spurious associations between measures. We provide recommendations about how to ensure that fraudulent participants are detected and excluded from self-report research studies. (PsycINFO Database Record (c) 2019 APA, all rights reserved).