The increasing incidence of eating disorders (EDs) underscores the critical need for methods that can accurately monitor and assess these complex conditions. In recent years, social media has emerged as a significant source of real-world data, with platforms like Twitter (now X) offering valuable insights into user experiences and behaviours related to EDs. This study investigates whether the severity of EDs can be accurately estimated from patterns in social media posts, using these data as a novel lens to understand the scope and progression of EDs. To this end, we conduct an extensive examination of Twitter, which includes the collection of a large ED-related dataset, manual annotation, and the development of an advanced deep learning model, SHED. Our novel model leverages a semantic heterogeneous graph representation to estimate ED severity from Twitter user data. Specifically, it learns semantic representations from user tweets and biographies, while integrating a heterogeneous ED graph representation that combines Twitter users, named entities mapped to knowledge graphs, and an ED lexicon. This approach enables a comprehensive understanding of users’ tweets, biographies, behaviours, interactions, and contextual factors, thereby improving the accuracy of ED severity estimation at the user level. Extensive experiments demonstrate that SHED outperforms all other models on both balanced and imbalanced datasets, achieving the lowest MAE (0.040 and 0.108) and RMSE (0.145 and 0.258). It also achieves the highest F1 score (85.04% and 81.92%) and accuracy (86.21% and 84.14%) for severity classification, demonstrating superior performance across all evaluation metrics.
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