Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach Article Swipe
Adrian Ahne
,
Vivek Khetan
,
Xavier Tannier
,
Md Imbesat Hassan Rizvi
,
Thomas Czernichow
,
Francisco Orchard
,
Charline Bour
,
Andrew Fano
,
Guy Fagherazzi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.2196/37201
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.2196/37201
Background Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient’s perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress. Objective Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect relationships in patient-reported diabetes-related tweets and provide a methodology to better understand the opinions, feelings, and observations shared within the diabetes online community from a causality perspective. 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 data set was manually labeled and used to train (1) a fine-tuned BERTweet model to detect causal sentences containing a causal relation and (2) a conditional random field model with Bidirectional Encoder Representations from Transformers (BERT)-based features to extract possible cause-effect associations. Causes and effects were clustered in a semisupervised approach and visualized in an interactive cause-effect network. Results Causal sentences were detected with a recall of 68% in an imbalanced data set. A conditional random field 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 relationships. “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 multiword cause, and the corresponding effect, as expressed in diabetes-related tweets leveraging BERT-based architectures and visualized as cause-effect network. Extracting causal associations in real life, patient-reported outcomes in social media data provide a useful complementary source of information in diabetes research.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/37201
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- OA Status
- gold
- Cited By
- 16
- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4285794637
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285794637Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/37201Digital Object Identifier
- Title
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Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-19Full publication date if available
- Authors
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Adrian Ahne, Vivek Khetan, Xavier Tannier, Md Imbesat Hassan Rizvi, Thomas Czernichow, Francisco Orchard, Charline Bour, Andrew Fano, Guy FagherazziList of authors in order
- Landing page
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https://doi.org/10.2196/37201Publisher landing page
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https://jmir.org/api/download?alt_name=medinform_v10i7e37201_app1.pdf&filename=7bcec3c0721f05c9db2bb7503c2f9938.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://jmir.org/api/download?alt_name=medinform_v10i7e37201_app1.pdf&filename=7bcec3c0721f05c9db2bb7503c2f9938.pdfDirect OA link when available
- Concepts
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Computer science, Diabetes mellitus, Cognitive psychology, Data science, Natural language processing, Artificial intelligence, Psychology, Medicine, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 2, 2024: 5, 2023: 9Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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