The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health During the COVID-19 Pandemic
This addresses the robustness of language analysis tools for researchers tracking mental health trends over time, but it is incremental as it applies an existing method to a new case study.
The study tackled the problem of semantic shift in longitudinal social media monitoring, showing that using a small number of semantically-unstable features can significantly alter estimates of depression during the COVID-19 pandemic, and demonstrated that a method for measuring semantic shift can improve predictive generalization.
Social media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of these tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the absence of appropriate tuning, specifically in the presence of semantic shift, can hinder robustness of the underlying methods. However, little is known about the practical effect this sensitivity may have on downstream longitudinal analyses. We explore this gap in the literature through a timely case study: understanding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable features can promote significant changes in longitudinal estimates of our target outcome. At the same time, we demonstrate that a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and, in turn, improve predictive generalization.