  Over the last decade, there has been a vast increase in eating disorder
diagnoses and eating disorder-attributed deaths, reaching their zenith during
the Covid-19 pandemic. This immense growth derived in part from the stressors
of the pandemic but also from increased exposure to social media, which is rife
with content that promotes eating disorders. This study aimed to create a
multimodal deep learning model that can determine if a given social media post
promotes eating disorders based on a combination of visual and textual data. A
labeled dataset of Tweets was collected from Twitter, recently rebranded as X,
upon which twelve deep learning models were trained and evaluated. Based on
model performance, the most effective deep learning model was the multimodal
fusion of the RoBERTa natural language processing model and the MaxViT image
classification model, attaining accuracy and F1 scores of 95.9% and 0.959,
respectively. The RoBERTa and MaxViT fusion model, deployed to classify an
unlabeled dataset of posts from the social media sites Tumblr and Reddit,
generated results akin to those of previous research studies that did not
employ artificial intelligence-based techniques, indicating that deep learning
models can develop insights congruent to those of researchers. Additionally,
the model was used to conduct a time-series analysis of yet unseen Tweets from
eight Twitter hashtags, uncovering that, since 2014, the relative abundance of
content that promotes eating disorders has decreased drastically within those
communities. Despite this reduction, by 2018, content that promotes eating
disorders had either stopped declining or increased in ampleness anew on those
hashtags.
