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, upon which twelve deep learning models were trained and tested. 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 timeseries 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 these hashtags.

翻译：过去十年间，饮食失调诊断病例及由此导致的死亡人数大幅增加，并在新冠疫情期间达到顶峰。这种急剧增长部分源于疫情带来的压力，但更主要的原因是人们越来越多地接触社交媒体——这些平台充斥着宣扬饮食失调的内容。本研究旨在构建一种多模态深度学习模型，该模型能根据视觉与文本数据的组合，判定特定社交媒体帖子是否宣扬饮食失调。我们从Twitter平台收集了带标注的推文数据集，在此基础上训练并测试了十二种深度学习模型。基于模型性能，最有效的深度学习模型是RoBERTa自然语言处理模型与MaxViT图像分类模型的多模态融合方案，其准确率达95.9%，F1分数为0.959。将该RoBERTa-MaxViT融合模型部署到Tumblr和Reddit社交媒体平台的未标注帖子数据集中进行分类时，所得结果与先前未采用人工智能技术的研究结论高度吻合，表明深度学习模型能够产生与研究人员一致的见解。此外，该模型还被用于对来自八个Twitter话题标签的未公开推文进行时间序列分析，结果显示：自2014年以来，这些社区中宣扬饮食失调内容的相对数量已大幅下降。尽管有此降幅，但截至2018年，这些话题标签上宣扬饮食失调的内容要么停止减少，要么重新开始增加。