Understanding Video Sharing Propagation in Social Networks

Abstract

Modern online social networking has drastically changed the information distribution landscape. Recently, video has become one of the most important types of objects spreading among social networking service users. The sheer and ever-increasing data volume, the broader coverage, and the longer access durations of video objects, however, present significantly more challenges than other types of objects. This article takes an initial step toward understanding the unique characteristics of video sharing propagation in social networks. Based on realworld data traces from a large-scale online social network, we examine the user behavior from diverse aspects and identify different types of users involved in video propagation. We closely investigate the temporal distribution during propagation as well as the typical propagation structures, revealing more details beyond stationary coverage. We further extend the conventional epidemic models to accommodate diverse types of users and their probabilistic viewing and sharing behaviors. The model, effectively capturing the essentials of the propagation process, serves as a valuable basis for such applications as workload synthesis, traffic prediction, and resource provision of video servers.

Inferring Online Relationships from User Characteristics

“…Therefore, publishers should be aware that it is not easy to make attractive content. This phenomenon not only indicates that only a small fraction of content achieves high popularity and, thus, has potential to spread widely through SNSs [37], but the wisdom of users also provide collaborative filtering to select flavored submissions. Moreover, high numbers of views are associated with people with high Extraversion.…”

Path-Based Epidemic Spreading in Networks

“…G can be represented by A, the N × N symmetric adjacency matrix, with a n,m = 1 if there exists a link between node n and m and 0 otherwise. Furthermore, we describe the effect of the routing protocol via an N × N (N − 1) routing matrix 3 , R. For our purpose, we depart from the conventional routing matrix that maps traffic to links (e.g., [44]) and instead, encode the traffic to nodes it traverses or is destined to. Accordingly, we define the routing matrix as follows:…”

“…O RIGINATED as part of epidemiology in biology studies for modelling disease spreading [1], epidemic theory has found applications in various scientific fields, ranging from natural networks (e.g., hub protein and human brain structure [2], (online) social networks [3], etc.) to manmade infrastructures (e.g., transportation systems [4], [5], power grid [6], telecommunication and computer networks [7], [8], [9], etc.).…”

A Comprehensive Insight towards Research Direction in Information Propagation

“…Although, there are many such studies, but will discuss some of them with higher relevancy. An investigation was carried out towards sharing of the video by Li et al [75] over social networks. The authors have investigated about the pattern of information propagation.…”