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Understanding the spatio-temporal ch...

Issa, Elias.

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Understanding the spatio-temporal characteristics of twitter data with geo-tagged and non geo-tagged content: Two case studies with the topic of flu and Ted (movie).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding the spatio-temporal characteristics of twitter data with geo-tagged and non geo-tagged content: Two case studies with the topic of flu and Ted (movie)./
Author:	Issa, Elias.
Description:	122 p.
Notes:	Source: Masters Abstracts International, Volume: 55-05.
Contained By:	Masters Abstracts International55-05(E).
Subject:	Geographic information science and geodesy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10119553
ISBN:	9781339807874

Understanding the spatio-temporal characteristics of twitter data with geo-tagged and non geo-tagged content: Two case studies with the topic of flu and Ted (movie).
Issa, Elias.

Understanding the spatio-temporal characteristics of twitter data with geo-tagged and non geo-tagged content: Two case studies with the topic of flu and Ted (movie). - 122 p.

Source: Masters Abstracts International, Volume: 55-05.

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

The dynamic characteristics of Twitter messages have the potential to provide GIS scientists with a great research opportunity to analyze the diffusion of events such as disease outbreaks, environmental changes, and social movements. This could be achieved by digitally collecting tweets that contain geo-tagged data with GPS coordinates. However, the percentage of geo-tagged data is extremely small comparing to non geo-tagged data. On the other side, non geo-tagged tweets often contain noisy data such as automated robots that affect the analysis of tweet content. For this matter, it is essential to analyze and understand the differences between geo-tagged and non geo-tagged tweets.

ISBN: 9781339807874Subjects--Topical Terms:

2122917
Geographic information science and geodesy.

Understanding the spatio-temporal characteristics of twitter data with geo-tagged and non geo-tagged content: Two case studies with the topic of flu and Ted (movie).
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100 1 $a Issa, Elias. $3 3276493
245 1 0 $a Understanding the spatio-temporal characteristics of twitter data with geo-tagged and non geo-tagged content: Two case studies with the topic of flu and Ted (movie).
300 $a 122 p.
500 $a Source: Masters Abstracts International, Volume: 55-05.
500 $a Adviser: MING-HSIANG TSOU.
502 $a Thesis (M.S.)--San Diego State University, 2016.
520 $a The dynamic characteristics of Twitter messages have the potential to provide GIS scientists with a great research opportunity to analyze the diffusion of events such as disease outbreaks, environmental changes, and social movements. This could be achieved by digitally collecting tweets that contain geo-tagged data with GPS coordinates. However, the percentage of geo-tagged data is extremely small comparing to non geo-tagged data. On the other side, non geo-tagged tweets often contain noisy data such as automated robots that affect the analysis of tweet content. For this matter, it is essential to analyze and understand the differences between geo-tagged and non geo-tagged tweets.
520 $a This first part of this research is to compare the context and temporal trends between geo-tagged and non geo-tagged tweets using different keywords "flu" and movie "Ted". Time series analysis has been implemented in order to study internal structure of geo-tagged and non geo-tagged tweet data with "flu" and "Ted" topics. Tweets are collected in four targeted cities (San Diego, Los Angeles, Denver, New York) to represent different geographical areas in the United States.
520 $a The second part of this research utilized a methodological framework to filter out the noises by removing retweets and tweets containing URLs from the non geo-tagged tweets. This study has also analyzed the spatio-temporal distribution of geo-tagged tweets within different type of land use to understand the dynamic human activities in urban environments.
520 $a The third part of this research adopts social network analysis tools to understand the interaction between Twitter users and to identify the most influential users or online celebrities based on their social network connectivity, degree rankings, and dynamic graphs.
520 $a This research has attempted to find the optimal method to filter the non geo-tagged tweets by removing retweets and tweets containing URLs. In general, geo-tagged tweets demonstrate less noises and higher correlation to the event than non geo-tagged tweets. With variation across topics, results showed that filtered non geo-tagged tweets performed better when comparing with geo-tagged tweets in temporal trend, time series analysis and content analysis.
520 $a This study has revealed that the keyword choice in Twitter is essential in how strong geo-tagged tweets correlate with non geo-tagged tweets, and how different geo-tagged tweets affect the spatial distribution in various land uses with different keyword choices. Lastly, this study has discovered how communities and social ties form in social network graphs and how can events such as disease outbreaks and entertainments affect the communication between groups within certain time intervals.
590 $a School code: 0220.
650 4 $a Geographic information science and geodesy. $3 2122917
650 4 $a Remote sensing. $3 535394
690 $a 0370
690 $a 0799
710 2 $a San Diego State University. $b Geography. $3 3177545
773 0 $t Masters Abstracts International $g 55-05(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=10119553