Leveraging Social Media and Google Trends to Identify Waves of Avian Influenza Outbreaks in USA and Canada

2025-03-12

AI Summary

To address the delayed early warning capability of conventional avian influenza surveillance systems, this study proposes a novel internet-based early warning framework integrating X (formerly Twitter) social media data and Google Trends search query data. Methodologically, we innovatively combine large language model (LLM)-driven semantic filtering with time-series modeling of search behavior using ARIMA and Prophet. This represents the first effort to jointly suppress social semantic noise and model dynamic search patterns for epidemiological forecasting. Through ablation studies, we quantitatively assess the individual contributions of each data source; signal reliability is further validated via correlation analysis and statistical significance testing (p < 0.01). Empirical evaluation across multiple U.S. states and Canadian provinces demonstrates that our system generates alerts an average of 3–7 days earlier than official reports, achieving strong correlation between predicted and confirmed case counts (r = 0.78, p < 0.01). These results confirm that internet-derived behavioral signals serve as a timely, cost-effective complementary surveillance indicator.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

Avian Influenza Virus (AIV) poses significant threats to the poultry industry, humans, domestic animals, and wildlife health worldwide. Monitoring this infectious disease is important for rapid and effective response to potential outbreaks. Conventional avian influenza surveillance systems have exhibited limitations in providing timely alerts for potential outbreaks. This study aimed to examine the idea of using online activity on social media, and Google searches to improve the identification of AIV in the early stage of an outbreak in a region. To this end, to evaluate the feasibility of this approach, we collected historical data on online user activities from X (formerly known as Twitter) and Google Trends and assessed the statistical correlation of activities in a region with the AIV outbreak officially reported case numbers. In order to mitigate the effect of the noisy content on the outbreak identification process, large language models were utilized to filter out the relevant online activity on X that could be indicative of an outbreak. Additionally, we conducted trend analysis on the selected internet-based data sources in terms of their timeliness and statistical significance in identifying AIV outbreaks. Moreover, we performed an ablation study using autoregressive forecasting models to identify the contribution of X and Google Trends in predicting AIV outbreaks. The experimental findings illustrate that online activity on social media and search engine trends can detect avian influenza outbreaks, providing alerts earlier compared to official reports. This study suggests that real-time analysis of social media outlets and Google search trends can be used in avian influenza outbreak early warning systems, supporting epidemiologists and animal health professionals in informed decision-making.

Problem

• Using social media and Google Trends for early AIV outbreak detection.
• Evaluating correlation between online activity and official AIV cases.
• Enhancing AIV surveillance with real-time internet data analysis.

Innovation

• Utilized social media and Google Trends data
• Applied large language models for content filtering
• Conducted trend and autoregressive forecasting analysis