JMIR Publications

ABSTRACT

Background:

Intervening and preventing diabetes distress requires an understanding of its causes and in particular from a patients’ perspective. Social media data provide direct access to how patients see and understand their disease and in consequence express 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 opinion, 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 dataset was manually labeled and used to train 1) a fine-tuned Bertweet BERTweet model to detect causal sentences containing a causal causal relation; 2) a Conditional Random Field (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 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 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.