MAP: Microblogging Assisted Profiling of TV Shows

TL;DR

This paper introduces MAP, a framework that leverages microblogging data to profile TV shows by connecting online discussions with offline content, addressing noise and retrieval challenges.

Contribution

The paper presents a novel microblogging-assisted profiling framework with joint user-content retrieval and social-aware analysis for TV show profiling.

Findings

Effective retrieval of microblogs related to TV shows.

Successful profiling based on content and social relationships.

Insights into social propagation patterns of TV show discussions.

Abstract

Online microblogging services that have been increasingly used by people to share and exchange information, have emerged as a promising way to profiling multimedia contents, in a sense to provide users a socialized abstraction and understanding of these contents. In this paper, we propose a microblogging profiling framework, to provide a social demonstration of TV shows. Challenges for this study lie in two folds: First, TV shows are generally offline, i.e., most of them are not originally from the Internet, and we need to create a connection between these TV shows with online microblogging services; Second, contents in a microblogging service are extremely noisy for video profiling, and we need to strategically retrieve the most related information for the TV show profiling.To address these challenges, we propose a MAP, a microblogging-assisted profiling framework, with contributions as follows: i) We propose a joint user and content retrieval scheme, which uses information about both actors and topics of a TV show to retrieve related microblogs; ii) We propose a social-aware profiling strategy, which profiles a video according to not only its content, but also the social relationship of its microblogging users and its propagation in the social network; iii) We present some interesting analysis, based on our framework to profile real-world TV shows.