The Impact of Geographic Scale on Identifying Different Social Media Behavior Extremes in Crisis Research

Our relationship with technology is constantly evolving, and that relationship is adapting even more quickly when faced with disaster. Understanding how to utilize human interactions with technology and the limitations of those interactions will be a crucial building block to contextualizing crisis data. The impact of scale on behavioral change analyses is an unexplored yet necessary facet of our ability to identify relative severities of crisis situations, magnitudes of localized crises, and total durations of disaster impacts. In order to analyze the impact of increasing scale on the identification of extreme behaviors, we aggregated Twitter data from Houston, Texas circa Hurricane Harvey across a wide range of scales. We found inversely related power law relationships between the identification of sharp Twitter activity bursts and sharp activity drop-offs. The relationships between these variables indicate the direct, definable dependence of social media aggregation analyses on the scale at which they are performed.