# Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

arXiv: https://arxiv.org/abs/2402.05536 · HTML: https://mlsanity.com/paper/2402.05536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?](https://mlsanity.com/paper/2010.08660/) (2020)
- [Evaluation of sentence embeddings in downstream and linguistic probing tasks](https://mlsanity.com/paper/1806.06259/) (2018)
- [Falcon 2.0: An Entity and Relation Linking Tool over Wikidata](https://mlsanity.com/paper/1912.11270/) (2019)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [pyRDF2Vec: A Python Implementation and Extension of RDF2Vec](https://mlsanity.com/paper/2205.02283/) (2022)
- [Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data](https://mlsanity.com/paper/1809.06532/) (2018)
- [Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction](https://mlsanity.com/paper/2004.10640/) (2020)

## Built on by (most cited first, 1 of 1)

- [ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification](https://mlsanity.com/paper/2501.14785/) (2025)

## Abstract (arXiv)

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.