Temporal network analysis of inter-organizational communications on social media during disasters
A study of Hurricane Harvey in Houston
Datos Bibliográficos
| ID | 22025609 |
|---|---|
| Autores | Akhil Anil Rajput (0000-0001-7207-4920, Indian Institute of Technology Gandhinagar), Qingchun Li (0000-0003-0769-0238, Texas A&M University, autor de correspondencia), Cheng Zhang (0009-0002-0231-8971, Texas A&M University), Ali Mostafavi (0000-0002-9076-9408, Texas A&M University) |
| Año | 2020 |
| Volumen | 46 |
| Páginas | 101622 |
| Fecha de publicación | 2020-06-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International Journal of Disaster Risk Reduction (JOURNAL) |
| Identificadores de la revista | ISSN: 2212-4209 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2020.101622 |
| OpenAlex | W3018005996 |
| Idioma | EN |
| Citas recibidas | 15 |
| Referencias citadas | 48 |
Business · Citizen journalism · Crisis communication · Dissemination · Emergency management · Information Dissemination · Knowledge management · Organizational communication · Organizational learning · Political science · Preparedness · Public relations · Situation awareness · Social media · Social network analysis · Telecommunications · World Wide Web · Complex Network Analysis Techniques · Computer Science · Disaster Management and Resilience · Engineering · Public Relations and Crisis Communication
Dynamics of nonprofit crisis communication in the pandemic
Between hope and hype
A Machine Learning Approach for Detecting Rescue Requests from Social Media
Social media use in disaster recovery
Who has dominated information spreading on social media during the early stage of Covid-19 pandemic in China? A temporal network analysis
Text mining hurricane harvey tweet data
Overlooking the victims
Examining the consistency between geo-coordinates and content-mentioned locations in tweets for disaster situational awareness
Extracting disaster information based on Sina Weibo in China
Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media
Investigating disaster response for resilient communities through social media data and the Susceptible-Infected-Recovered (SIR) model
Quality Management Practices and Inter-Organizational Project Performance
Dynamics of interorganisational emergency communication on Twitter
Emergency communication networks on Twitter during Hurricane Irma
Utilising qualitative data for social network analysis in disaster research
Interfirm Collaboration Networks
Communicating on Twitter during a disaster
Collaborative environmental governance
Social network analysis
Bridges, brokers and boundary spanners in collaborative networks
Institutional congruence for resilience management in interdependent infrastructure systems
Disaster risk reduction is not ‘everyone's business’
Crowd or Hubs
The use of Facebook for information seeking, decision support, and self-organization following a significant disaster
How Firm Responses to Natural Disasters Strengthen Community Resilience
Journal of Contemporary Asia
Centrality in social networks conceptual clarification
Models of core/periphery structures
Preparing communities for disasters
Government to Citizens (G2C) communication and use of social media in the post-disaster reconstruction phase
Including quality in Social network analysis to foster dialogue in urban resilience and adaptation policies
The role of social networks in natural resource governance
Coordinating Plans for Climate Adaptation
Perceived Networks, Activity Foci, and Observable Communication in Social Collectives
Social and Economic Networks
Really Social Disaster
Risk, Security, and Disaster Management
Social Media Use during Japan's 2011 Earthquake
The Crisis Map of the Czech Republic
Technology Adoption and Use in the Aftermath of Hurricane Katrina in New Orleans
Social Capital and Community Resilience
The Strength of Weak Ties
Collaboration and Creativity
Structural Holes and Good Ideas
| Obras citantes distintas | 15 |
|---|---|
| Citas por año | 3 |
| Intervalo de citas | 2021 - 2026 (6) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 14 |