Cross-Network Social Multimedia Computing - User-centric Social Multimedia Computing - page 79

Chapter 5
Cross-Network Social Multimedia Computing
Abstract Social multimedia contributes significantly to the arrival of the Big Data
era. The distribution of social multimedia content and users' social multimedia activ-
ities among various social media networks motivate us to investigate social multime-
dia computing under the cross-network circumstances. We interpret cross-network
as the “variety” of social multimedia: the heterogeneous data in various social media
networks. In this chapter, basic tasks of user-centric social multimedia computing
are extended under the cross-network circumstances, by exploiting the overlapped
users among social media networks.
5.1 Introduction
Big Data is currently defined with its “4V” characteristics, i.e., Volume, Velocity,
Variety, and Value. Correspondingly, big data research is devoted to addressing the
capacity in data storage, the efficiency in data capture and computing, the complexity
in data analysis, and the data accuracy and quality. Data analysis, which mainly cor-
responds to the variety characteristic, involves with processing and utilizing various
types of data sources. On one hand, multimedia has been recognized as processing
heterogeneous data types toward a satisfactory solution [ 25 ]. On the other hand, social
media contributes significantly to the arrival of Big Data. Therefore, it is significant
to investigate into the Va r i e t y issue in social multimedia computing as promising
future directions.
Va r i e t y in Big Data concerns beyond text, image, audio, and video. In the context
of social multimedia, variety can be embodied by the heterogeneous data created
and consumed in various social media networks, e.g., multimedia content in media
sharing web sites, social streams in microblogging web sites, inter-user communica-
tions in social networking services, location, and Point-Of-Interest (POI) in check-in
web sites, consuming history in online shopping web sites, etc. The heterogeneous
data record people's online activities from different angles, and reflect the physical
world at the same time. Exploring variety is critical to value mining from social
media big data, which plays important roles in conducting complex social media
analysis and designing advanced social media applications. For example, collective