Article

 

Finding Actionable Information on Social Media Acceso Abierto Deposited

Contenido Descargable

File thumbnail: Submission_16_Ammar_Mohamed_Expo2019.pdf Descargar PDF
Descargar Adobe Acrobat Reader
Date Uploaded: 05/15/2020
Date Modified: 05/15/2020

A great deal of data is generated every day on social media, although this information is used for marketing purposes regularly, it has the potential to serve other purposes, such as in crisis management. This study focuses on collecting data from social media, specifically Twitter, in order to help 911 telecommunicators (floor supervisors, call takers, and dispatchers) to 1) identify Twitter users requesting assistance during a crisis, 2) identify information that may be useful to incidents that were called into 911, and 3) pass the information to the first responders (police, fire, and emergency medical services). Previous research in this area can be summarized into three stages. First, a set of information requirements has been developed that must be satisfied to dispatch first responders and meet their immediate awareness needs. Second, a coding schema has been presented to identify six types of actionable information. Finally, it proposed automated methods based on previous literature which can be used to implement these methods in the future (Kropczynski et al. 2018). This research concentration is on refining social media data by starting with finding local tweets that contain this information and recognize patterns of how it is used. Next, patterns will be used in the development of automated methods in the future. The contribution of this work is extending the coding schema of the 6Ws and put it on an action, develop an interface to view the data of social media separated by the 6Ws. It will begin with just on of the six Ws (Weapons).

Creador
Licencia
Tema
Presentador
Colegio
Departamento
Fecha de creacion
Editor
Título de la revista
  • IT Research Symposium’19
Idioma

Digital Object Identifier (DOI)

Identificador: doi:10.7945/y7jh-h521
Enlazar: https://doi.org/10.7945/y7jh-h521

Este enlace DOI es la mejor manera para que otros citen su trabajo.

Relaciones

En Colección:

Elementos

Enlace permanente a esta página: https://scholar.uc.edu/show/7h149r21s