Association for Public Policy Analysis & Management

Building collaborative networks among public, nonprofit, and private organizations is crucial for effective emergency management (Comfort et al., 2012; Waugh & Streib, 2006).  These networks facilitate information sharing and resource exchange and aid in the establishment of a common operating picture. The recent proliferation of social media further enables diverse actors to communicate, share ideas, coordinate activities, and break down long-existing silos. However, little attention has been paid to social media's role in fostering and promoting the development of cross-sector emergency information networks.  This paper seeks to fill this gap by examining how emergency management organizations use social media to share and communicate information with other organizations. In particular, this paper analyzes the characteristics and structures of emergency information networks that emerge on the microblogging site Twitter.  

A seed list of Twitter accounts was collected from the state emergency management agencies in the four states of FEMA Region 7 (Iowa, Kansas, Missouri, and Nebraska).  Based on the seed list, we identified 1,086 accounts representing the core set of stakeholders and emergency management actors in Region 7. Given this list, we used the software tool NodeXL to capture each actor’s most recent 200 tweets and gathered evidence of communication among those actors in the form of retweets, mentions, and replies. The resulting data (i.e. dyadic relationships between accounts) provides the basis for the analysis of emergency information networks. We then manually assigned attributes to all accounts determining whether they belonged to a government agency, a nonprofit organization, a for-profit organization, a news media outlet, or a private citizen. We differentiated those categories for additional analysis and identified different types of government and nonprofit organizations based on organizational mission (e.g., general government, public safety, and others) and geographic scale/level of jurisdiction (e.g., the municipal, county, regional, state, national, and international levels).   

We describe the network's composition, identify central actors, and evaluate whether participants seek out and exchange information with others from similar backgrounds and organizational characteristics (i.e. bonding strategies) or from different backgrounds and organizational characteristics (i.e. bridging strategies).  We then utilize an exponential random graph model (ERGM) to explore how a set of multi-level factors operating at the individual, dyadic, and network level influence the transfer of information among the organizations.