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

TL;DR

This paper develops a multimodal deep learning model combining text and images to identify pro-eating disorder content on social media, achieving high accuracy and revealing temporal trends in such content.

Contribution

Introduces a novel site-agnostic multimodal deep learning approach using RoBERTa and MaxViT for detecting pro-eating disorder content across social media platforms.

Findings

Achieved 95.9% accuracy in classifying pro-eating disorder posts.

Model's insights aligned with previous research without AI techniques.

Discovered fluctuations in pro-eating disorder content over time since 2014.

Abstract

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.