TY - JOUR
T1 - Exploring the emerging type of comment for online videos
T2 - DanMu
AU - He, Ming
AU - Ge, Yong
AU - Chen, Enhong
AU - Liu, Qi
AU - Wang, Xuesong
N1 - Funding Information: This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010) and the National Natural Science Foundation of China (Grants No. U1605251 and 61672483). Qi Liu gratefully acknowledges the support of the Youth Innovation Promotion Association of CAS (No. 2014299). Yong Ge acknowledges the support of the Natural Science Foundation of China (NSFC, Grants No. 61602234 and No. 61572032). Authors’ addresses: M. He, Q. Liu, and E. Chen (corresponding author), Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, Anhui 230027, China; emails: [email protected], {qiliuql, cheneh}@ustc.edu.cn; Y. Ge (corresponding author), Eller College of Management, The University of Arizona, Tucson, Arizona 85721, America; email: [email protected]; X. Wang, School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2017 ACM 1559-1131/2017/08-ART1 $15.00 https://doi.org/10.1145/3098885 Publisher Copyright: © 2017 ACM.
PY - 2017/8
Y1 - 2017/8
N2 - DanMu, an emerging type of user-generated comment, has become increasingly popular in recent years. Many online video platforms such as Tudou.com have provided the DanMu function. Unlike traditional online reviews such as reviews at Youtube.com that are outside the videos, DanMu is a scrolling marquee comment, which is overlaid directly on top of the video and synchronized to a specific playback time. Such comments are displayed as streams of moving subtitles overlaid on the video screen. Viewers could easily write DanMus while watching videos, and the written DanMus will be immediately overlaid onto the video and displayed to writers themselves and other viewers as well. Such DanMu systems have greatly enabled users to communicate with each other in a much more direct way, creating a real-time sharing experience. Although there are several unique features of DanMu and has had a great impact on online video systems, to the best of our knowledge, there is no work that has provided a comprehensive study on DanMu. In this article, as a pilot study, we analyze the unique characteristics of DanMu from various perspectives. Specifically, we first illustrate some unique distributions of DanMus by comparing with traditional reviews (TReviews) that we collected from a real DanMu-enabled online video system. Second, we discover two interesting patterns in DanMu data: a herding effect and multiple-burst phenomena that are significantly different from those in TRviews and reveal important insights about the growth of DanMus on a video. Towards exploring antecedents of both th herding effect and multiple-burst phenomena, we propose to further detect leading DanMus within bursts, because those leading DanMus make the most contribution to both patterns. A framework is proposed to detect leading DanMus that effectively combines multiple factors contributing to leading DanMus. Based on the identified characteristics of DanMu, finally we propose to predict the distribution of future DanMus (i.e., the growth of DanMus), which is important for many DanMu-enabled online video systems, for example, the predicted DanMu distribution could be an indicator of video popularity. This prediction task includes two aspects: One is to predict which videos future DanMus will be posted for, and the other one is to predict which segments of a video future DanMus will be posted on.We develop two sophisticated models to solve both problems. Finally, intensive experiments are conducted with a real-world dataset to validate all methods developed in this article.
AB - DanMu, an emerging type of user-generated comment, has become increasingly popular in recent years. Many online video platforms such as Tudou.com have provided the DanMu function. Unlike traditional online reviews such as reviews at Youtube.com that are outside the videos, DanMu is a scrolling marquee comment, which is overlaid directly on top of the video and synchronized to a specific playback time. Such comments are displayed as streams of moving subtitles overlaid on the video screen. Viewers could easily write DanMus while watching videos, and the written DanMus will be immediately overlaid onto the video and displayed to writers themselves and other viewers as well. Such DanMu systems have greatly enabled users to communicate with each other in a much more direct way, creating a real-time sharing experience. Although there are several unique features of DanMu and has had a great impact on online video systems, to the best of our knowledge, there is no work that has provided a comprehensive study on DanMu. In this article, as a pilot study, we analyze the unique characteristics of DanMu from various perspectives. Specifically, we first illustrate some unique distributions of DanMus by comparing with traditional reviews (TReviews) that we collected from a real DanMu-enabled online video system. Second, we discover two interesting patterns in DanMu data: a herding effect and multiple-burst phenomena that are significantly different from those in TRviews and reveal important insights about the growth of DanMus on a video. Towards exploring antecedents of both th herding effect and multiple-burst phenomena, we propose to further detect leading DanMus within bursts, because those leading DanMus make the most contribution to both patterns. A framework is proposed to detect leading DanMus that effectively combines multiple factors contributing to leading DanMus. Based on the identified characteristics of DanMu, finally we propose to predict the distribution of future DanMus (i.e., the growth of DanMus), which is important for many DanMu-enabled online video systems, for example, the predicted DanMu distribution could be an indicator of video popularity. This prediction task includes two aspects: One is to predict which videos future DanMus will be posted for, and the other one is to predict which segments of a video future DanMus will be posted on.We develop two sophisticated models to solve both problems. Finally, intensive experiments are conducted with a real-world dataset to validate all methods developed in this article.
KW - Burst detection
KW - DanMu
KW - Herding effect
KW - Leading danmus
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U2 - 10.1145/3098885
DO - 10.1145/3098885
M3 - Article
SN - 1559-1131
VL - 12
JO - ACM Transactions on the Web
JF - ACM Transactions on the Web
IS - 1
M1 - 1
ER -