Social media language patterns reveal early signs of impending stroke: An observational study
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Background
Stroke remains a major global health concern, contributing substantially to mortality and long-term disability. Current clinical tools lack effective mechanisms for early detection. Here, we investigate whether linguistic and behavioral patterns in online social media can serve as early indicators of impending stroke.
Methods
We analyzed posts from 1,683 Reddit users who reported experiencing a stroke and 2,438 users across three control groups al made between 2015 and 2024. Linguistic features were extracted from posts, and predictive models were trained to distinguish individuals who experienced a stroke from controls.
Results
Our results reveal changes in several linguistic markers, e.g., the rate of spelling errors and the use of word tokens, beginning approximately 20 weeks prior to the stroke. Using posts from the four months preceding the event, the predictive model achieved an area under the curve (AUC) of 0.87.
Discussion
These findings highlight the potential of social media–derived linguistic signals to predict strokes several months in advance, offering a promising avenue for early detection and preventive interventions in digital medicine.
