東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Improve disaster communication in on...

Dozier, Jessica Addison.

Linked to FindBook

Google Book

Amazon

博客來

Improve disaster communication in online and offline communities using social media (Twitter) and Big Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improve disaster communication in online and offline communities using social media (Twitter) and Big Data./
Author:	Dozier, Jessica Addison.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	121 p.
Notes:	Source: Masters Abstracts International, Volume: 55-06.
Contained By:	Masters Abstracts International55-06(E).
Subject:	Geographic information science and geodesy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10128157
ISBN:	9781339861869

Improve disaster communication in online and offline communities using social media (Twitter) and Big Data.
Dozier, Jessica Addison.

Improve disaster communication in online and offline communities using social media (Twitter) and Big Data. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 121 p.

Source: Masters Abstracts International, Volume: 55-06.

Thesis (M.S.)--San Diego State University, 2016.

This research places disaster management into a geographic context in order to understand how people communicate during disasters and how to increase local community resilience to foster sustainability and lessen vulnerability. Analyzing communication that occurred over Twitter networks during the 2014 San Diego wildfires and the 2015 Nepal earthquake enabled temporal trends, spatiotemporal patterns, social network structures, socially disconnected communities, and hyperlocal relationships to be identified and measured. The temporal trends examined daily communication by day and by hour to see how well Twitter activity correlated to real world events. In both case studies, there were strong relationships between Twitter activity and real world events; their major peaks and falls reflected occurrences recorded in the After Action Reports provided by the County of San Diego and the Government of Nepal. The spatiotemporal analysis showed the spatial distribution of tweets, and measured changes in communication and movement patterns. The results from the two case studies were similar in that their data produced major hotspots not only in highly populated regions, but also areas who were either directly affected or nearby affected areas. The differential maps showed that when each disaster event occurred, there would be a spike in Twitter activity in or around the disaster.

ISBN: 9781339861869Subjects--Topical Terms:

2122917
Geographic information science and geodesy.

Improve disaster communication in online and offline communities using social media (Twitter) and Big Data.
LDR:04524nmm a2200325 4500 001 2126379
005 20171121080731.5
008 180830s2016 ||||||||||||||||| ||eng d
020 $a 9781339861869
035 $a (MiAaPQ)AAI10128157
035 $a AAI10128157
040 $a MiAaPQ $c MiAaPQ
100 1 $a Dozier, Jessica Addison. $3 3288479
245 1 0 $a Improve disaster communication in online and offline communities using social media (Twitter) and Big Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 121 p.
500 $a Source: Masters Abstracts International, Volume: 55-06.
500 $a Adviser: Ming-Hsiang Tsou.
502 $a Thesis (M.S.)--San Diego State University, 2016.
520 $a This research places disaster management into a geographic context in order to understand how people communicate during disasters and how to increase local community resilience to foster sustainability and lessen vulnerability. Analyzing communication that occurred over Twitter networks during the 2014 San Diego wildfires and the 2015 Nepal earthquake enabled temporal trends, spatiotemporal patterns, social network structures, socially disconnected communities, and hyperlocal relationships to be identified and measured. The temporal trends examined daily communication by day and by hour to see how well Twitter activity correlated to real world events. In both case studies, there were strong relationships between Twitter activity and real world events; their major peaks and falls reflected occurrences recorded in the After Action Reports provided by the County of San Diego and the Government of Nepal. The spatiotemporal analysis showed the spatial distribution of tweets, and measured changes in communication and movement patterns. The results from the two case studies were similar in that their data produced major hotspots not only in highly populated regions, but also areas who were either directly affected or nearby affected areas. The differential maps showed that when each disaster event occurred, there would be a spike in Twitter activity in or around the disaster.
520 $a Methods in social network analysis were performed on the datasets to analyze the structure of the online communities from each case study and to identify their socially disconnected communities. Both San Diego and Nepal's online Twitter networks had similar structures and measurements of modularity, the measurement that forms online communities and indicates the connectedness of a network. The similar measurements are a sign that there are dense connections between people, but fewer connections between communities. This information suggests that methods to improve communication between communities should be established. Recommendations are to create a uniform way for people to communicate during a disaster, such as designating specific hashtags or have influential people act as online sirens to carry any disaster-related information further into the networks. In addition to similar structures and measurements of modularity, both case studies' socially disconnected communities expressed similar characteristics in how they were identified. Since there are similar ways to identify socially disconnected communities in these case studies, there is potential in providing a simple way for response agencies to identify these communities in the future. Once the communities can be identified, outreach programs can be expanded to ensure all people can gain situational awareness and real-time updates.
520 $a The hyperlocal relationships between each case studies' online and offline communities were defined by separately mapping communities to see the users' geographic proximities to each other. The hyperlocal relationships on a city-level were strong, but going closer into a neighborhood-level shows weaker relationships. Trajectories of the users in the datasets were also mapped in order to better show human behavior and movement during disaster events from a micro-level analysis. The information provided by examining people's trajectories may help response agencies in future decision making when establishing shelters and distributing aid, and understanding people's response times to evacuation notices and why they may wait before evacuating.
590 $a School code: 0220.
650 4 $a Geographic information science and geodesy. $3 2122917
650 4 $a Remote sensing. $3 535394
650 4 $a Web studies. $3 2122754
690 $a 0370
690 $a 0799
690 $a 0646
710 2 $a San Diego State University. $b Geography. $3 3177545
773 0 $t Masters Abstracts International $g 55-06(E).
790 $a 0220
791 $a M.S.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10128157