The promising research on Artificial Intelligence usages in suicide
prevention has principal gaps, including black box methodologies, inadequate
outcome measures, and scarce research on non-verbal inputs, such as social
media images (despite their popularity today, in our digital era). This study
addresses these gaps and combines theory-driven and bottom-up strategies to
construct a hybrid and interpretable prediction model of valid suicide risk
from images. The lead hypothesis was that images contain valuable information
about emotions and interpersonal relationships, two central concepts in
suicide-related treatments and theories. The dataset included 177,220 images by
841 Facebook users who completed a gold-standard suicide scale. The images were
represented with CLIP, a state-of-the-art algorithm, which was utilized,
unconventionally, to extract predefined features that served as inputs to a
simple logistic-regression prediction model (in contrast to complex neural
networks). The features addressed basic and theory-driven visual elements using
everyday language (e.g., bright photo, photo of sad people). The results of the
hybrid model (that integrated theory-driven and bottom-up methods) indicated
high prediction performance that surpassed common bottom-up algorithms, thus
providing a first proof that images (alone) can be leveraged to predict
validated suicide risk. Corresponding with the lead hypothesis, at-risk users
had images with increased negative emotions and decreased belonginess. The
results are discussed in the context of non-verbal warning signs of suicide.
Notably, the study illustrates the advantages of hybrid models in such
complicated tasks and provides simple and flexible prediction strategies that
could be utilized to develop real-life monitoring tools of suicide.

本研究使用人工智能技术结合自底向上和理论驱动的策略构建了一个混合和可解释的预测模型，从社交媒体图像中预测自杀风险，并发现使用预定义的视觉元素（如明亮的照片、忧伤人物的照片等）可以提高预测性能，这为开发实时监测自杀的工具提供了简单和灵活的预测策略。

社交媒体图片预测自杀风险的可解释人工智能策略

A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Images

With the increased awareness of situations of mental crisis and their
societal impact, online services providing emergency support are becoming
commonplace in many countries. Computational models, trained on discussions
between help-seekers and providers, can support suicide prevention by
identifying at-risk individuals. However, the lack of domain-specific models,
especially in low-resource languages, poses a significant challenge for the
automatic detection of suicide risk. We propose a model that combines
pre-trained language models (PLM) with a fixed set of manually crafted (and
clinically approved) set of suicidal cues, followed by a two-stage fine-tuning
process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly
outperforming an array of strong baselines even early on in the conversation,
which is critical for real-time detection in the field. Moreover, the model
performs well across genders and age groups.

通过结合预训练语言模型和手动构建的自杀线索集合，我们提出了一个模型来实现自杀风险的自动检测，从而在紧急支持在线服务中支持自杀预防。该模型在跨性别与不同年龄组等多个方面表现良好，并在会话早期就明显优于强基线模型。

在线咨询服务中自杀风险检测：低资源语言中的研究

Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language

The World Health Organization (WHO) has emphasized the importance of
significantly accelerating suicide prevention efforts to fulfill the United
Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper,
we present an end-to-end multitask system to address a novel task of detection
of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and
Thwarted Belongingness (TB) from suicide notes. We also introduce a manually
translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the
benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB
labels. We exploit the temporal orientation and emotion information in the
suicide notes to boost overall performance. For comprehensive evaluation of our
proposed method, we compare it to several state-of-the-art approaches on the
existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset.
Empirical evaluation suggests that temporal and emotional information can
substantially improve the detection of PB and TB.

本文提出一种全面的多任务系统来检测自杀中的两个人际风险因素，并利用情感信息和时间定向提高了检测的性能。

探测自杀笔记中的被视为负担和归属受挫

Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes

In recent years, online communities have formed around suicide and self-harm
prevention. While these communities offer support in moment of crisis, they can
also normalize harmful behavior, discourage professional treatment, and
instigate suicidal ideation. In this work, we focus on how interaction with
others in such a community affects the mental state of users who are seeking
support. We first build a dataset of conversation threads between users in a
distressed state and community members offering support. We then show how to
construct a classifier to predict whether distressed users are helped or harmed
by the interactions in the thread, and we achieve a macro-F1 score of up to
0.69.

探讨在线社区对寻求自杀和自残预防支持的用户心理状态的影响，研究如何构建分类器以预测社区对用户的帮助或伤害。