Amidst the growing use of social media platforms as actively redundant communication channels, emergency alerting via this pathway is in a position to gain from a more automated and systematic usage approach. The corresponding growth of geographic information systems/services (GIS) in local government agencies and the maturing of social media platforms into functional components of our everyday lives both contribute to the opportunity to create an optimal linkage of online social networking and emergency management resources. This paper proposes a method for which municipalities can use their robust geographic feature datasets along with location prediction techniques for labeling non geo-located social media users as local citizens and potential recipients of critical messages. The breadth of location inference research as well as the complementary aspects of location disambiguation is examined to discover a novel combination of methods suitable for supplementing geographic prediction in social media with regional geographic datasets. Furthermore, a system is proposed which aims to effectively integrate GIS, social media and emergency management resources in order to meet the demand for a modern mass notification infrastructure.
","abstract_html":"<p>Amidst the growing use of social media platforms as actively redundant communication channels, emergency alerting via this pathway is in a position to gain from a more automated and systematic usage approach. The corresponding growth of geographic information systems/services (GIS) in local government agencies and the maturing of social media platforms into functional components of our everyday lives both contribute to the opportunity to create an optimal linkage of online social networking and emergency management resources. This paper proposes a method for which municipalities can use their robust geographic feature datasets along with location prediction techniques for labeling non geo-located social media users as local citizens and potential recipients of critical messages. The breadth of location inference research as well as the complementary aspects of location disambiguation is examined to discover a novel combination of methods suitable for supplementing geographic prediction in social media with regional geographic datasets. Furthermore, a system is proposed which aims to effectively integrate GIS, social media and emergency management resources in order to meet the demand for a modern mass notification infrastructure.</p>","abstract_has_math":false,"creators":["Cassagnol, Alexandria"],"institution":null,"degree_name":"Master of Science in Applied Engineering (M.S.A.E.)","degree_level":"Thesis (restricted to Georgia Southern)","degree_discipline":"Department of Computer Sciences","degree_department":null,"school":null,"contributors":["Aimao Zhang","Cheryl Aasheim"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T02:27:52Z","subjects":["ETD","location inference","social media","emergency management","mass notification","Communication Technology and New Media","Computer and Systems Architecture","Mass Communication"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/1128","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aimao Zhang","Cheryl Aasheim"]},{"key":"dc:creator","label":"Author","values":["Cassagnol, Alexandria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-04-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Computer Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (restricted to Georgia Southern)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Applied Engineering (M.S.A.E.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","location inference","social media","emergency management","mass notification","Communication Technology and New Media","Computer and Systems Architecture","Mass Communication"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/1128"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Amidst the growing use of social media platforms as actively redundant communication channels, emergency alerting via this pathway is in a position to gain from a more automated and systematic usage approach. The corresponding growth of geographic information systems/services (GIS) in local government agencies and the maturing of social media platforms into functional components of our everyday lives both contribute to the opportunity to create an optimal linkage of online social networking and emergency management resources. This paper proposes a method for which municipalities can use their robust geographic feature datasets along with location prediction techniques for labeling non geo-located social media users as local citizens and potential recipients of critical messages. The breadth of location inference research as well as the complementary aspects of location disambiguation is examined to discover a novel combination of methods suitable for supplementing geographic prediction in social media with regional geographic datasets. Furthermore, a system is proposed which aims to effectively integrate GIS, social media and emergency management resources in order to meet the demand for a modern mass notification infrastructure.
"]},{"key":"dc:title","label":"Title","values":["Programmatically Extending Emergency Notifications into Regional Online Social Networks with Latent Variable Inference Techniques"]}]}],"canonical_facts":{"dc:contributor":["Aimao Zhang","Cheryl Aasheim"],"dc:creator":["Cassagnol, Alexandria"],"dc:date.available":["2014-04-17T07:00:00Z"],"dc:description.abstract":["Amidst the growing use of social media platforms as actively redundant communication channels, emergency alerting via this pathway is in a position to gain from a more automated and systematic usage approach. The corresponding growth of geographic information systems/services (GIS) in local government agencies and the maturing of social media platforms into functional components of our everyday lives both contribute to the opportunity to create an optimal linkage of online social networking and emergency management resources. This paper proposes a method for which municipalities can use their robust geographic feature datasets along with location prediction techniques for labeling non geo-located social media users as local citizens and potential recipients of critical messages. The breadth of location inference research as well as the complementary aspects of location disambiguation is examined to discover a novel combination of methods suitable for supplementing geographic prediction in social media with regional geographic datasets. Furthermore, a system is proposed which aims to effectively integrate GIS, social media and emergency management resources in order to meet the demand for a modern mass notification infrastructure.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/1128"],"dc:subject":["ETD","location inference","social media","emergency management","mass notification","Communication Technology and New Media","Computer and Systems Architecture","Mass Communication"],"dc:title":["Programmatically Extending Emergency Notifications into Regional Online Social Networks with Latent Variable Inference Techniques"],"thesis:degree_discipline":["Department of Computer Sciences"],"thesis:degree_level":["Thesis (restricted to Georgia Southern)"],"thesis:degree_name":["Master of Science in Applied Engineering (M.S.A.E.)"]},"updated_at":"2026-07-24T02:27:52Z"}