Cogniscope: Modeling Social Media Interactions as Digital Biomarkers for Early Detection of Cognitive Decline

Abstract

Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), are associated with subtle declines in memory, attention, and language that often go undetected until late in progression. Traditional diagnostic tools such as MRI and neuropsychological testing are invasive, costly, and poorly suited for population-scale monitoring. Social platforms, by contrast, produce continuous multimodal traces that can serve as ecologically valid indicators of cognition. In this paper, we introduce Cogniscope, a simulation framework that generates social-media-style interaction data for studying digital biomarkers of cognitive health. The framework models synthetic users with heterogeneous trajectories, embedding micro-tasks such as video summarization and lightweight question answering into content consumption streams. These interactions yield linguistic markers (semantic drift, disfluency) and behavioral signals (watch time, pausing, sharing), which can be fused to evaluate early detection models. We demonstrate the framework's use through ablation and sensitivity analyses, showing how detection performance varies across modalities, noise levels, and temporal windows. To support reproducibility, we release the generator code, parameter configurations, and synthetic datasets. By providing a controllable and ethically safe testbed, Cogniscope enables systematic investigation of multimodal cognitive markers and offers the community a benchmark resource that complements real-world validation studies.