@article{ref1,
title="Social media images can predict suicide risk using interpretable large language-vision models",
journal="Journal of clinical psychiatry",
year="2023",
author="Badian, Yael and Ophir, Yaakov and Tikochinski, Refael and Calderon, Nitay and Klomek, Anat Brunstein and Fruchter, Eyal and Reichart, Roi",
volume="85",
number="1",
pages="23m14962-23m14962",
abstract="BACKGROUND: Suicide, a leading cause of death and a major public health concern, became an even more pressing matter since the emergence of social media two decades ago and, more recently, following the hardships that characterized the COVID-19 crisis. Contemporary studies therefore aim to predict signs of suicide risk from social media using highly advanced artificial intelligence (AI) methods. Indeed, these new AI-based studies managed to break a longstanding prediction ceiling in suicidology; however, they still have principal limitations that prevent their implementation in real-life settings. These include &quot;black box&quot; methodologies, inadequate outcome measures, and scarce research on non-verbal inputs, such as images (despite their popularity today). <br><br>OBJECTIVE: This study aims to address these limitations and present an interpretable prediction model of clinically valid suicide risk from images. <br><br>METHODS: The data were extracted from a larger dataset from May through June 2018 that was used to predict suicide risk from textual postings. Specifically, the extracted data included a total of 177,220 images that were uploaded by 841 Facebook users who completed a gold-standard suicide scale. The images were represented with CLIP (Contrastive Language-Image Pre-training), a state-of-the-art deep-learning algorithm, which was utilized, unconventionally, to extract predefined interpretable features (eg, &quot;photo of sad people&quot;) that served as inputs to a simple logistic regression model. <br><br>RESULTS: The results of this hybrid model that integrated theory-driven features with bottom-up methods indicated high prediction performance that surpassed common deep learning algorithms (area under the receiver operating characteristic curve [AUC] = 0.720, Cohen d = 0.82). Further analyses supported a theory-driven hypothesis that at-risk users would have images with increased negative emotions and decreased belongingness. <br><br>CONCLUSIONS: This study provides a first proof that publicly available images can be leveraged to predict validated suicide risk. It also provides simple and flexible strategies that could enhance the development of real-life monitoring tools for suicide.<p /> <p>Language: en</p>",
language="en",
issn="0160-6689",
doi="10.4088/JCP.23m14962",
url="http://dx.doi.org/10.4088/JCP.23m14962"
}