Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910322639503360 |
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| author | Benítez-Andrades, José Alberto García-Ordás, María Teresa Russo, Mayra Sakor, Ahmad Rotger, Luis Daniel Fernandes Vidal, Maria-Esther |
| author_facet | Benítez-Andrades, José Alberto García-Ordás, María Teresa Russo, Mayra Sakor, Ahmad Rotger, Luis Daniel Fernandes Vidal, Maria-Esther |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_05536 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts Benítez-Andrades, José Alberto García-Ordás, María Teresa Russo, Mayra Sakor, Ahmad Rotger, Luis Daniel Fernandes Vidal, Maria-Esther Machine Learning Computation and Language 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. |
| title | Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2402.05536 |