  Breast cancer is a significant public health concern and is the leading cause
of cancer-related deaths among women. Despite advances in breast cancer
treatments, medication non-adherence remains a major problem. As electronic
health records do not typically capture patient-reported outcomes that may
reveal information about medication-related experiences, social media presents
an attractive resource for enhancing our understanding of the patients'
treatment experiences. In this paper, we developed natural language processing
(NLP) based methodologies to study information posted by an automatically
curated breast cancer cohort from social media. We employed a transformer-based
classifier to identify breast cancer patients/survivors on X (Twitter) based on
their self-reported information, and we collected longitudinal data from their
profiles. We then designed a multi-layer rule-based model to develop a breast
cancer therapy-associated side effect lexicon and detect patterns of medication
usage and associated side effects among breast cancer patients. 1,454,637 posts
were available from 583,962 unique users, of which 62,042 were detected as
breast cancer members using our transformer-based model. 198 cohort members
mentioned breast cancer medications with tamoxifen as the most common. Our side
effect lexicon identified well-known side effects of hormone and chemotherapy.
Furthermore, it discovered a subject feeling towards cancer and medications,
which may suggest a pre-clinical phase of side effects or emotional distress.
This analysis highlighted not only the utility of NLP techniques in
unstructured social media data to identify self-reported breast cancer posts,
medication usage patterns, and treatment side effects but also the richness of
social data on such clinical questions.
