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The promising research on Artificial Intelligence usages in suicideprevention has principal gaps, including black box methodologies, inadequateoutcome measures, and scarce research on non-verbal inputs, such as socialmedia images (despite their popularity today, in our digital era). This studyaddresses these gaps and combines theory-driven and bottom-up strategies toconstruct a hybrid and interpretable prediction model of valid suicide riskfrom images. The lead hypothesis was that images contain valuable informationabout emotions and interpersonal relationships, two central concepts insuicide-related treatments and theories. The dataset included 177,220 images by841 Facebook users who completed a gold-standard suicide scale. The images wererepresented with CLIP, a state-of-the-art algorithm, which was utilized,unconventionally, to extract predefined features that served as inputs to asimple logistic-regression prediction model (in contrast to complex neuralnetworks). The features addressed basic and theory-driven visual elements usingeveryday language (e.g., bright photo, photo of sad people). The results of thehybrid model (that integrated theory-driven and bottom-up methods) indicatedhigh prediction performance that surpassed common bottom-up algorithms, thusproviding a first proof that images (alone) can be leveraged to predictvalidated suicide risk. Corresponding with the lead hypothesis, at-risk usershad images with increased negative emotions and decreased belonginess. Theresults are discussed in the context of non-verbal warning signs of suicide.Notably, the study illustrates the advantages of hybrid models in suchcomplicated tasks and provides simple and flexible prediction strategies thatcould be utilized to develop real-life monitoring tools of suicide.

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