Dynamics of Collective Information Processing for Risk Encoding in Social Networks during Crises

TL;DR

This study analyzes how collective information processing in social networks during crises exhibits stable patterns in activity and influence, with localized communication spikes and global transmission gaps, informing better risk communication strategies.

Contribution

It provides a detailed empirical analysis of social network dynamics during various disasters, revealing stable influence patterns and spatial-temporal communication behaviors.

Findings

Stable power-law distribution of social influence.

Localized communication spikes during crises.

Global transmission gaps in social networks.

Abstract

Online social networks are increasingly being utilized for collective sense making and information processing in disasters. However, the underlying mechanisms that shape the dynamics of collective intelligence in online social networks during disasters is not fully understood. To bridge this gap, we examine the mechanisms of collective information processing in human networks during five threat cases including airport power outage, hurricanes, wildfire, and blizzard, considering the temporal and spatial dimensions. Using the 13MM Twitter data generated by 5MM online users during these threats, we examined human activities, communication structures and frequency, social influence, information flow, and medium response time in social networks. The results show that the activities and structures are stable in growing networks, which lead to a stable power-law distribution of the social influence in networks. These temporally invariant patterns are not affected by people's memory and ties' strength. In addition, spatially localized communication spikes and global transmission gaps in the networks. The findings could inform about network intervention strategies to enable a healthy and efficient online environment, with potential long-term impact on risk communication and emergency response.