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Over the last decade, there has been a vast increase in eating disorderdiagnoses and eating disorder-attributed deaths, reaching their zenith duringthe Covid-19 pandemic. This immense growth derived in part from the stressorsof the pandemic but also from increased exposure to social media, which is rifewith content that promotes eating disorders. This study aimed to create amultimodal deep learning model that can determine if a given social media postpromotes eating disorders based on a combination of visual and textual data. Alabeled dataset of Tweets was collected from Twitter, recently rebranded as X,upon which twelve deep learning models were trained and evaluated. Based onmodel performance, the most effective deep learning model was the multimodalfusion of the RoBERTa natural language processing model and the MaxViT imageclassification model, attaining accuracy and F1 scores of 95.9% and 0.959,respectively. The RoBERTa and MaxViT fusion model, deployed to classify anunlabeled dataset of posts from the social media sites Tumblr and Reddit,generated results akin to those of previous research studies that did notemploy artificial intelligence-based techniques, indicating that deep learningmodels can develop insights congruent to those of researchers. Additionally,the model was used to conduct a time-series analysis of yet unseen Tweets fromeight Twitter hashtags, uncovering that, since 2014, the relative abundance ofcontent that promotes eating disorders has decreased drastically within thosecommunities. Despite this reduction, by 2018, content that promotes eatingdisorders had either stopped declining or increased in ampleness anew on thosehashtags.

A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media