%0 Report
%9 EIC working paper series
%A Lopreite, Milena
%A Puliga, Michelangelo
%A Riccaboni, Massimo
%D 2018
%F eprints:3856
%I IMT School for Advanced Studies Lucca
%K Keywords: global health network; social network analysis; machine learning classifier;  tuberculosis; malaria; pneumonia; policy evaluation  -   JEL codes: i15, i18, c8
%N 1
%T The Global Health Networks:  A Comparative Analysis of  Tuberculosis, Malaria and Pneumonia  Using Social Media Data
%U http://eprints.imtlucca.it/3856/
%X Global health networks (GHNs) of organizations fighting major health threats  represent a useful strategy to respond to the challenge of mobilizing and coordinating  different types of health organizations across borders toward a common goal.  In this paper we reconstruct the GHNs of malaria, tuberculosis and pneumonia by  creating a new unique database of health organizations from the official Twitter accounts  of each organization. We use a majority voter Multi Naive Bayes classifier to  discover, among the Twitter users, the ones that represent organizations or groups active  in each disease area. We perform a social network analysis (SNA) of the global  health networks (GHNs) to evaluate the structure of the network and the role and  performance of the organizations in each network. We find evidence that the GHN  of malaria, TBC and pneumonia are different in terms of performance and leadership,  geographical coverage as well as Twitter popularity. Our analysis validate the  use of social media to analyze GHNs, their effectiveness and to mobilize the global  community toward global sustainable development.