國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Mobile Random Video Chat :/
其他題名:	Understanding User Behavior and Misbehavior Detection.
作者:	Tian, Lei.
面頁冊數:	1 online resource (101 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339721569

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

Mobile Random Video Chat :Understanding User Behavior and Misbehavior Detection. - 1 online resource (101 pages)

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

Thesis (Ph.D.)

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339721569Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Mobile Random Video Chat : = Understanding User Behavior and Misbehavior Detection.
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500 $a Adviser: Qin Lv.
502 $a Thesis (Ph.D.) $c University of Colorado at Boulder $d 2016.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
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