Network Density Analysis of Health Seeking Behavior in Metro Manila: A Retrospective Analysis on COVID-19 Google Trends Data

2025-03-27

AI Summary

This study investigates the temporal evolution of public health–related online search behavior in Metro Manila, Philippines, during the COVID-19 pandemic. Using Google Trends data from 2020–2021, we construct multi-threshold temporal networks and jointly model network density and clustering coefficient within rolling windows (15- and 30-day). We innovatively employ distance correlation to quantify node association strength, finding that higher thresholds (e.g., 0.8) yield more interpretable network structures. Results reveal a sharp initial surge in network density, reflecting heightened policy attention early in the pandemic; over time, search focus shifts from containment policies to symptom identification. The 30-day window demonstrates greater robustness, whereas the 15-day window exhibits higher sensitivity to abrupt behavioral changes. This work contributes a transferable temporal network analytics framework for real-time public health sentiment monitoring and situational awareness.

Technology Category

Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunitySearch and Optimization: Distributed SearchApplication Domains: Humanities & Computational Social Science

Application Category

Social Networks and Social Media: Social mining and social search on the WebWeb Mining and Content Analysis: Models for Web evolutionSearch and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30-day rolling windows from March 2020 to March 2021. The methodology involved constructing network graphs using distance correlation coefficients at varying thresholds (0.4, 0.5, 0.6, and 0.8) and analyzing the time-series data of network density and clustering coefficients. Results revealed three key findings: (1) an inverse relationship between the threshold values and network metrics, indicating that higher thresholds provide more meaningful keyword relationships; (2) exceptionally high network connectivity during the initial pandemic months followed by gradual decline; and (3) distinct patterns in keyword relationships, transitioning from policy-focused searches to more symptom-specific queries as the pandemic temporally progressed. The 30-day window analysis showed more stable, but less search activities compared to the 15-day windows, suggesting stronger correlations in immediate search behaviors. These insights are helpful for health communication because it emphasizes the need of a strategic and conscientious information dissemination from the government or the private sector based on the networked search behavior (e.g. prioritizing to inform select symptoms rather than an overview of what the coronavirus is).

Problem

• Analyzes COVID-19 health-seeking behavior via Google Trends data
• Examines keyword relationships using network density analysis
• Identifies shifts from policy-focused to symptom-specific searches

Innovation

• Network density analysis of Google Trends data
• Distance correlation coefficients at varying thresholds
• Time-series analysis of network density metrics