  Social networks are vital for information sharing, especially in the health
sector for discussing diseases and treatments. These platforms, however, often
feature posts as brief texts, posing challenges for Artificial Intelligence
(AI) in understanding context. We introduce a novel hybrid approach combining
community-maintained knowledge graphs (like Wikidata) with deep learning to
enhance the categorization of social media posts. This method uses advanced
entity recognizers and linkers (like Falcon 2.0) to connect short post entities
to knowledge graphs. Knowledge graph embeddings (KGEs) and contextualized word
embeddings (like BERT) are then employed to create rich, context-based
representations of these posts.
  Our focus is on the health domain, particularly in identifying posts related
to eating disorders (e.g., anorexia, bulimia) to aid healthcare providers in
early diagnosis. We tested our approach on a dataset of 2,000 tweets about
eating disorders, finding that merging word embeddings with knowledge graph
information enhances the predictive models' reliability. This methodology aims
to assist health experts in spotting patterns indicative of mental disorders,
thereby improving early detection and accurate diagnosis for personalized
medicine.
