# Performance of Social Network Sensors During Hurricane Sandy

arXiv: https://arxiv.org/abs/1402.2482 · HTML: https://mlsanity.com/paper/1402.2482/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2014.

## Built on by (most cited first, 6 of 6)

- [Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach](https://mlsanity.com/paper/2311.09498/) (2023)
- [Leveraging Social Media Data to Identify Factors Influencing Public Attitude Towards Accessibility, Socioeconomic Disparity and Public Transportation](https://mlsanity.com/paper/2402.01682/) (2024)
- [Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents](https://mlsanity.com/paper/2505.24331/) (2025)
- [Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events](https://mlsanity.com/paper/2505.16455/) (2025)
- [Signals from the Floods: AI-Driven Disaster Analysis through Multi-Source Data Fusion](https://mlsanity.com/paper/2505.17038/) (2025)
- [When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Prediction](https://mlsanity.com/paper/2606.15121/) (2026)

## Abstract (arXiv)

Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow, and a mean to derive early-warning sensors, improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioural properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in user's network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays significant role in determining the scale of such advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility of implementing a simple "sentiment sensing" technique to detect and locate disasters.