Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
| Autor: | Ahne, Adrian, Khetan, Vivek, Tannier, Xavier, Rizvi, Md Imbessat Hassan, Czernichow, Thomas, Orchard, Francisco, Bour, Charline, Fano, Andrew, Fagherazzi, Guy |
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| Rok vydání: | 2021 |
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| Druh dokumentu: | Working Paper |
| Popis: | Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations shared within the diabetes online community from a causality perspective. Materials and Methods: More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect-tweet dataset was manually labeled and used to train 1) a fine-tuned Bertweet model to detect causal sentences containing a causal association 2) a CRF model with BERT based features to extract possible cause-effect associations. Causes and effects were clustered in a semi-supervised approach and visualised in an interactive cause-effect-network. Results: Causal sentences were detected with a recall of 68% in an imbalanced dataset. A CRF model with BERT based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68%. This led to 96,676 sentences with cause-effect associations. "Diabetes" was identified as the central cluster followed by "Death" and "Insulin". Insulin pricing related causes were frequently associated with "Death". Conclusions: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multi-word cause and corresponding effect as expressed in diabetes-related tweets leveraging BERT-based architectures and visualised as cause-effect-network. Extracting causal associations on real-life, patient reported outcomes in social media data provides a useful complementary source of information in diabetes research. Comment: 6 Figures, 4 Tables |
| Databáze: | arXiv |
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