Multi-Source-Driven Asynchronous Diffusion Model for Video-Sharing in Online Social Networks

Characterizing the video diffusion in online social networks (OSNs) is not only instructive for network traffic engineering, but also provides insights into the information diffusion process. A number of continuous-time diffusion models have been proposed to describe video diffusion under the assumption that the activation latency along social links follows a single parametric distribution. However, such assumption has not been empirically verified. Moreover, a user usually has multiple activated neighbors with different activation times, and it is hard to distinguish the different contributions of these multiple potential sources. To fill this gap, we study the multiple-source-driven asynchronous information diffusion problem based on substantial video diffusion traces. Specifically, we first investigate the latency of information propagation along social links and define the single-source (SS) activation latency for an OSN user. We find that the SS activation latency follows the exponential mixture model. Then we develop an analytical framework which incorporates the temporal factor and the influence of multiple sources to describe the influence propagation process. We show that one's activation probability decreases exponentially with time. We also show that the time shift of the exponential function is only determined by the most recent source (MRS) active user, but the total activation probability is the combination of influence exerted by all active neighbors. Based on these discoveries, we develop a multi-source-driven asynchronous diffusion model (MADM). Using maximum likelihood techniques, we develop an algorithm based on expectation maximization (EM) to learn model parameters, and validate our proposed model with real data. The experimental results show that the MADM obtains better prediction accuracy under various evaluation metrics.published_or_final_versio

사용자 생성 콘텐츠 웹 사이트의 동영상 확산-Bilibili기반으로 실증 분석

학위논문(석사)--서울대학교 대학원 :공과대학 협동과정 기술경영·경제·정책전공,2019. 8. 황준석.User-generated content emphasis user value, and based on the development of Web 2.0, it takes an important position whatever in information diffusion and market management part. Therefore, it is necessary to identify which factors would give influence to information diffusion in UGC. This thesis based on a video UGC website – Bilibili, try to find influential factors during the video diffusion process. Different from previous research which mainly focuses on the social network, this thesis mainly used video characteristic data to explore effective factors to video diffusion. First, after the draw the number of views increase trend within one month, views trend in Bilibili indicates that video diffusion showed a different diffusion curve. Then through careful analysis of each different diffusion periods, this thesis found the influential factors are different during different diffusion periods. The analysis result showed that higher interactive among users and contents could attract more people to watch the video which improves video diffusion rate, and also showed the impacts of general comment below the video and sharing activity, video quality to video diffusion. Based on this thesis, some marginal implications also introduced such as it could provide some basis for web designers, people who use UGC as a marketing tool and users who want to be a UGC content producer.사용자 생성 콘텐츠 (UGC) 사용자의 가치를 강조하며 Web 2.0의 개발을 기반으로 정보 확산 및 시장 관리에 중요한 역할을 가지고있다. 따라서 UGC웹 사이트에서 어떤 요소가 정보 확산에 영향을 미칠 것인지를 식별하는 것이 필요하다. 이 논문은 Bilibili웹 사이트 기반으로, 비디오 확산 과정에서 영향력있는 요인을 찾아 보려고한다. 주로 소셜 네트워크에 초점을 맞춘 선행 문헌와는 달리, 이 논문에서는 주로 비디오 특성 데이터를 사용하여 비디오 확산에 효과적인 요소를 탐구했다. 첫째, 1 개월 이내에 비디오 조회수 증가 추세 파악했고 비디오 확산에는 세 가지 단계가 있다. 둘째, 각 단계에서 비디오 확산에 미치는 요인이 무엇인지 분석했다. 분석결과 보면 사용자와 콘텐츠의 상호 작용이 높을수록 더 많은 사람들이 동영상을 볼 수 있어 동영상 확산 속도가 향상 될 수 있음을 보여주었다. 이 논문을 바탕으로 웹 디자이너, UGC를 마케팅 도구로 사용하는 사람들 및 UGC 콘텐츠 제작자가되기를 원하는 사용자에게 기본적인 근거를 제공 할 수 있다는 점과 같은 몇 가지 중요한 함의가 소개되었다.Table of content: 1. Introduction 1 2. Literature review 4 2.1 UGC and Bilibili. 4 2.2 Video diffusion in UGC 6 2.3 Hypotheses 9 3 Data and Methodology. 12 3.1 Data 12 3.2 Methodology. 16 4. Analysis results and Conclusion. 19 4.1 Analysis result. 19 4.2 Conclusion 21 4.2.1 Conclusion of daily views. 21 4.2.2 Conclusion of video attributes. 22 5. Implications and limitations 26 5.1 Implications 26 5.2 Limitations. 28 Reference: 29 Appendix: 37 국문초록 43Maste