TY - GEN
T1 - Understanding user behavior at scale in a mobile video chat application
AU - Tian, Lei
AU - Li, Shaosong
AU - Ahn, Junho
AU - Chu, David
AU - Han, Richard
AU - Lv, Qin
AU - Mishra, Shivakant
PY - 2013
Y1 - 2013
N2 - Online video chat services such as Chatroulette and Omegle randomly match users in video chat sessions and have become increasingly popular, with tens of thousands of users online at anytime during a day. Our interest is in examining user behavior in the growing domain of mobile video, and in particular how users behave in such video chat services as they are extended onto mobile clients. To date, over four thousand people have downloaded and used our Android based mobile client, which was developed to be compatible with an existing video chat service. The paper provides a first-ever detailed large scale study of mobile user behavior in a random video chat service over a three week period. This study identifies major characteristics such as mobile user session durations, time of use, demographic distribution and the large number of brief sessions that users click through to find good matches. Through content analysis of video and audio, as well as analysis of texting and clicking behavior, we discover key correlations among these characteristics, e.g., normal mobile users are highly correlated with using the front camera and with the presence of a face, whereas misbehaving mobile users have a high negative correlation with the presence of a face.
AB - Online video chat services such as Chatroulette and Omegle randomly match users in video chat sessions and have become increasingly popular, with tens of thousands of users online at anytime during a day. Our interest is in examining user behavior in the growing domain of mobile video, and in particular how users behave in such video chat services as they are extended onto mobile clients. To date, over four thousand people have downloaded and used our Android based mobile client, which was developed to be compatible with an existing video chat service. The paper provides a first-ever detailed large scale study of mobile user behavior in a random video chat service over a three week period. This study identifies major characteristics such as mobile user session durations, time of use, demographic distribution and the large number of brief sessions that users click through to find good matches. Through content analysis of video and audio, as well as analysis of texting and clicking behavior, we discover key correlations among these characteristics, e.g., normal mobile users are highly correlated with using the front camera and with the presence of a face, whereas misbehaving mobile users have a high negative correlation with the presence of a face.
KW - Mobile video chat
KW - Behavior analysis
KW - Effective matching
KW - Misbehavior detection
UR - http://www.scopus.com/inward/record.url?scp=84885216945&partnerID=8YFLogxK
U2 - 10.1145/2493432.2493488
DO - 10.1145/2493432.2493488
M3 - Conference proceeding contribution
SN - 9781450317702
T3 - UbiComp 2013 - Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 647
EP - 656
BT - UbiComp 2013 - Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
CY - New York, NY
T2 - 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013
Y2 - 8 September 2013 through 12 September 2013
ER -