  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.
