TY - GEN
T1 - Predicting the popularity of DanMu-enabled videos
T2 - 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016
AU - He, Ming
AU - Ge, Yong
AU - Wu, Le
AU - Chen, Enhong
AU - Tan, Chang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Recent years have witnessed the prosperity of a new type of real-time user-generated comment, or so-called DanMu, in many recent online video platforms. These DanMu-enabled video platforms present scrolling marquee comments overlaid directly on top of the videos by synchronizing these comments to specific playback times. In this paper, we study the prediction of video popularity in these platforms, which may benefit a lot of applications ranging from online advertising for website holders to popular video recommendation for audiences. Different from traditional online video platforms where only traditional reviews are available, these DanMus make viewers easily see other viewers’ opinions and communicate with each other in a much more direct way. Consequently, viewers are easily influenced by others’ behaviors over time, which is considered as the herding effect in social science. However, how to address the unique characteristics (i.e., the herding effect) of DanMuenabled online videos for more accurate popularity prediction is still under-explored. To that end, in this paper, we first explore and measure the herding effect of DanMu-enabled video popularity from multiple aspects, including the popular videos, the popular DanMus and the newly updated videos. Also, we recognize that the uploaders’ influence and video quality affect the video popularity as well. Along this line, we propose a model that incorporates the herding effect, uploaders’ influence and video quality for predicting the video popularity. An effective estimation method is also proposed. Finally, experimental results on real-world data show that our proposed prediction model improves the prediction accuracy by 47.19% compared to the baselines.
AB - Recent years have witnessed the prosperity of a new type of real-time user-generated comment, or so-called DanMu, in many recent online video platforms. These DanMu-enabled video platforms present scrolling marquee comments overlaid directly on top of the videos by synchronizing these comments to specific playback times. In this paper, we study the prediction of video popularity in these platforms, which may benefit a lot of applications ranging from online advertising for website holders to popular video recommendation for audiences. Different from traditional online video platforms where only traditional reviews are available, these DanMus make viewers easily see other viewers’ opinions and communicate with each other in a much more direct way. Consequently, viewers are easily influenced by others’ behaviors over time, which is considered as the herding effect in social science. However, how to address the unique characteristics (i.e., the herding effect) of DanMuenabled online videos for more accurate popularity prediction is still under-explored. To that end, in this paper, we first explore and measure the herding effect of DanMu-enabled video popularity from multiple aspects, including the popular videos, the popular DanMus and the newly updated videos. Also, we recognize that the uploaders’ influence and video quality affect the video popularity as well. Along this line, we propose a model that incorporates the herding effect, uploaders’ influence and video quality for predicting the video popularity. An effective estimation method is also proposed. Finally, experimental results on real-world data show that our proposed prediction model improves the prediction accuracy by 47.19% compared to the baselines.
UR - http://www.scopus.com/inward/record.url?scp=84962440430&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962440430&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-32049-6_22
DO - 10.1007/978-3-319-32049-6_22
M3 - Conference contribution
AN - SCOPUS:84962440430
SN - 9783319320489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 351
EP - 366
BT - Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings
A2 - Navathe, Shamkant B.
A2 - Shekhar, Shashi
A2 - Wang, X. Sean
A2 - Wu, Weili
A2 - Du, Xiaoyong
A2 - Xiong, Hui
PB - Springer-Verlag
Y2 - 16 April 2016 through 19 April 2016
ER -