A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Images
Published in arXiv, 2023
Recommended citation: Yael Badian, Yaakov Ophir, Refael Tikochinski, Nitay Calderon, Anat Brunstein Klomek and Roi Reichart https://doi.org/10.48550/arXiv.2302.09488
Abstract
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.bibtex
@article{badian2023picture,
author = {Yael Badian and
Yaakov Ophir and
Refael Tikochinski and
Nitay Calderon and
Anat Brunstein Klomek and
Roi Reichart},
title = {A Picture May Be Worth a Thousand Lives: An Interpretable Artificial
Intelligence Strategy for Predictions of Suicide Risk from Social
Media Images},
journal = {CoRR},
volume = {abs/2302.09488},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2302.09488},
doi = {10.48550/arXiv.2302.09488},
eprinttype = {arXiv},
eprint = {2302.09488},
timestamp = {Thu, 23 Feb 2023 16:02:44 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2302-09488.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}