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

Mining the relation and implication ...

Jin, Xin.

Linked to FindBook

Google Book

Amazon

博客來

Mining the relation and implication of user generated content in social media.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mining the relation and implication of user generated content in social media./
Author:	Jin, Xin.
Description:	136 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:	Dissertation Abstracts International75-01B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3600391
ISBN:	9781303504860

Mining the relation and implication of user generated content in social media.
Jin, Xin.

Mining the relation and implication of user generated content in social media. - 136 p.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2012.

The phenomenal success of social media sites, such as Facebook, Twitter, LinkedIn, Flickr and YouTube, not only revolutionized the way people communicate and think, but also revolutionized the way how corporations do business. During the current age of social media, web usage can be characterized as the decentralization of online information, which now largely consists of high volume and real-time content generated from the bottom-up, where common users are the contributors and producers of information. The transition from Web 1.0 (represented by static webpages instead of dynamic user-generated content) to Web 2.0 (represented by Social Media which consists of large scale of real-time and dynamic user-generated content), makes Internet information become in larger scale, richer, more interactive and complex.

ISBN: 9781303504860Subjects--Topical Terms:

523869
Computer science.

Mining the relation and implication of user generated content in social media.
LDR:02795nmm a2200277 4500 001 2072286
005 20160729143534.5
008 170521s2012 ||||||||||||||||| ||eng d
020 $a 9781303504860
035 $a (MiAaPQ)AAI3600391
035 $a AAI3600391
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jin, Xin. $3 3178384
245 1 0 $a Mining the relation and implication of user generated content in social media.
300 $a 136 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500 $a Adviser: Jiawei Han.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2012.
520 $a The phenomenal success of social media sites, such as Facebook, Twitter, LinkedIn, Flickr and YouTube, not only revolutionized the way people communicate and think, but also revolutionized the way how corporations do business. During the current age of social media, web usage can be characterized as the decentralization of online information, which now largely consists of high volume and real-time content generated from the bottom-up, where common users are the contributors and producers of information. The transition from Web 1.0 (represented by static webpages instead of dynamic user-generated content) to Web 2.0 (represented by Social Media which consists of large scale of real-time and dynamic user-generated content), makes Internet information become in larger scale, richer, more interactive and complex.
520 $a The goal of this thesis is to mine the relation and implication of user generated content in social media. In this thesis, I will present several studies that I have conducted on how to analyze such relation and implication. First, we proposed an approach for similarity computation based on both visual content and link information in social media by a novel way of mutual reinforcement of content similarity and link similarity. Second, we proposed a GAD (General Activity Detection) framework to fully explore the power of activity detection for clustering, which can be used to partition similar content objects into groups. The algorithms (both exact and approximate) developed within this framework can perform fast clustering for large scale content data. Social media content not only relate to each other, but also to outside phenomena and show strong implication with prediction power. In my third work, by aggregating user content information in social media, we developed a unified model to integrate clustering, ranking and regression for the prediction of stock price change.
590 $a School code: 0090.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a University of Illinois at Urbana-Champaign. $b Computer Science. $3 2096310
773 0 $t Dissertation Abstracts International $g 75-01B(E).
790 $a 0090
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3600391