This study examines how Twitter enables citizen self-organization in disasters, revisiting Barton (1963) disaster role typology through the September 19, 2017 Mexico City earthquake.
Social network analysis (SNA) was conducted on Twitter data collected during the first 72 h post-earthquake. Using Python for data mining and Gephi for visualization, we analyzed over 5,000 nodes and their interactions through mentions (@) and hashtags (#). Key metrics included in-degree (relevance proxy), out-degree and modularity to identify emergent groups.
Results reveal a digital “informal mass assault”: citizens displayed strong motivation but lacked clear “role knowledge” (what to do), while possessing high “relational knowledge” (whom to contact). Emergent groups like @Verificado19s became central information hubs, surpassing formal authorities (SINAPROC). The study highlights a disconnect between government-led initiatives (#ERUM) and civil society self-organization (#Verificado19s).
This research updates Barton's framework for the digital age, demonstrating how social media fundamentally alters disaster communication dynamics. It provides actionable insights for integrating emergent online groups into official protocols and fostering bidirectional citizen–government communication.
