國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Making Thin Data Thick: User Behavio...

Arizona State University.

Making Thin Data Thick: User Behavior Analysis with Minimum Information.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Making Thin Data Thick: User Behavior Analysis with Minimum Information.
作者:	Zafarani, Reza.
面頁冊數:	205 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
附註:	Adviser: Huan Liu.
Contained By:	Dissertation Abstracts International76-12B(E).
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3718191
ISBN:	9781321979541

Making Thin Data Thick: User Behavior Analysis with Minimum Information.
Zafarani, Reza.

Making Thin Data Thick: User Behavior Analysis with Minimum Information. - 205 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2015.

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data.

ISBN: 9781321979541Subjects--Topical Terms:

199325
Computer science.

Making Thin Data Thick: User Behavior Analysis with Minimum Information.
LDR:03845nmm a2200313 4500 001 476108
005 20160418090155.5
008 160517s2015 ||||||||||||||||| ||eng d
020 $a 9781321979541
035 $a (MiAaPQ)AAI3718191
035 $a AAI3718191
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zafarani, Reza. $3 730389
245 1 0 $a Making Thin Data Thick: User Behavior Analysis with Minimum Information.
300 $a 205 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
500 $a Adviser: Huan Liu.
502 $a Thesis (Ph.D.)--Arizona State University, 2015.
520 $a With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data.
520 $a This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick..
520 $a In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information?
520 $a In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information.
520 $a The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied..
590 $a School code: 0010.
650 4 $a Computer science. $3 199325
690 $a 0984
710 2 $a Arizona State University. $b Computer Science. $3 708551
773 0 $t Dissertation Abstracts International $g 76-12B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3718191