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.

翻译：过去十年间，进食障碍诊断及由其导致的死亡人数大幅增加，在新冠疫情期间达到顶峰。这一急剧增长部分源于疫情带来的压力，但也与社交媒体接触增多有关——社交媒体上充斥着宣扬进食障碍的内容。本研究旨在构建一个多模态深度学习模型，能够根据图文数据组合判断特定社交媒体帖子是否宣扬进食障碍。我们从Twitter（近期更名为X）收集了标注的推文数据集，在此基础上训练并评估了十二个深度学习模型。基于模型性能，最有效的深度学习模型是融合了RoBERTa自然语言处理模型和MaxViT图像分类模型的多模态模型，其准确率和F1分数分别达到95.9%和0.959。将该RoBERTa与MaxViT融合模型部署到Tumblr和Reddit社交平台未标注帖子数据集进行分类时，所得结果与以往未采用人工智能技术的研究成果相似，表明深度学习模型能够生成与研究者一致的见解。此外，该模型被用于对八个Twitter话题标签中未标注的推文进行时间序列分析，发现自2014年以来，这些社区中宣扬进食障碍内容的相对数量已大幅减少。然而，尽管数量有所下降，截至2018年，这些话题标签上宣扬进食障碍的内容要么停止减少，要么重新呈现增长态势。