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Mobile Random Video Chat: Understand...

Tian, Lei.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection./
作者:	Tian, Lei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	101 p.
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:	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.
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