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

Mobile Random Video Chat: Understand...

Tian, Lei.

Linked to FindBook

Google Book

Amazon

博客來

Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection./
Author:	Tian, Lei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	101 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10108822
ISBN:	9781339721569

Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection.
Tian, Lei.

Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 101 p.

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2016.

Nowadays, the near-ubiquitous availability of smartphones and the significant improvement of cellular networks make the video chat applications become mainstream for mobile devices. Meanwhile, because of the capability to make friends in the virtual domain, online random video chat services such as Chatroulette and Omegle have become increasingly popular. Given these changes, we expect the mobile random video chat services will also gain the public attention and greatly increase in volume and frequency soon. In this thesis, I focus on analyzing the user behavior and seeking for possible improvements of user experience in such kind of mobile service. I build an Android-based Omegle compliant mobile random video chat application to collect data at scale. Using the collected data, we analyze user behavior patterns from multiple aspects and reveal some concerns regarding user experience in such service. We then conduct an in-depth meaningful user behavior analysis to understand the key characteristics of effectiveness for promoting long video chat sessions. Furthermore, motivated by the negative user experience caused by the existence of obscene content, I propose an accurate and efficient misbehavior classifier. The classifier leverages multi-modal sensors and temporal modality in each session to improve accuracy. It also applies a multi-level cascaded classification procedure to quantify the tradeoff between efficiency and accuracy. Finally, I briefly introduce the potential directions which could be further investigated to improve user experience of mobile random video chat services in the future.

ISBN: 9781339721569Subjects--Topical Terms:

523869
Computer science.

Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection.
LDR:02557nmm a2200289 4500 001 2155046
005 20180426100010.5
008 190424s2016 ||||||||||||||||| ||eng d
020 $a 9781339721569
035 $a (MiAaPQ)AAI10108822
035 $a (MiAaPQ)colorado:14322
035 $a AAI10108822
040 $a MiAaPQ $c MiAaPQ
100 1 $a Tian, Lei. $3 1934254
245 1 0 $a Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 101 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
500 $a Adviser: Qin Lv.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2016.
520 $a Nowadays, the near-ubiquitous availability of smartphones and the significant improvement of cellular networks make the video chat applications become mainstream for mobile devices. Meanwhile, because of the capability to make friends in the virtual domain, online random video chat services such as Chatroulette and Omegle have become increasingly popular. Given these changes, we expect the mobile random video chat services will also gain the public attention and greatly increase in volume and frequency soon. In this thesis, I focus on analyzing the user behavior and seeking for possible improvements of user experience in such kind of mobile service. I build an Android-based Omegle compliant mobile random video chat application to collect data at scale. Using the collected data, we analyze user behavior patterns from multiple aspects and reveal some concerns regarding user experience in such service. We then conduct an in-depth meaningful user behavior analysis to understand the key characteristics of effectiveness for promoting long video chat sessions. Furthermore, motivated by the negative user experience caused by the existence of obscene content, I propose an accurate and efficient misbehavior classifier. The classifier leverages multi-modal sensors and temporal modality in each session to improve accuracy. It also applies a multi-level cascaded classification procedure to quantify the tradeoff between efficiency and accuracy. Finally, I briefly introduce the potential directions which could be further investigated to improve user experience of mobile random video chat services in the future.
590 $a School code: 0051.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a University of Colorado at Boulder. $b Computer Science. $3 1018560
773 0 $t Dissertation Abstracts International $g 77-10B(E).
790 $a 0051
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10108822