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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Benevenuto, F., Rodrigues, T.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM, ACM (2009)
Blondel, V.D., Lambiotte, R.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)
Dimitrova, N., Zhang, H.: Applications of video-content analysis and retrieval. IEEE MultiMedia 9(3), 42–55 (2002)
Du, H., Feldman, M.W., Li, S., Jin, X.: An algorithm for detecting community structure of social networks based on prior knowledge and modularity. Complexity 12(3), 53–60 (2007)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. J. R. Stat. Soc. 28(1), 100–108 (1979)
Holotescu, C., Grosseck, G.: M3-learning-exploring mobile multimedia microblogging learning. World Journal on Educational Technology 3(3), 168–176 (2011)
Milliken, M.C., Gibson, K.: User-generated video and the online public sphere: Will youtube facilitate digital freedom of expression in atlantic canada? American Communication Journal 10(3), 1–14 (2008)
Newman, M.E.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
Soffer, S.N., Vázquez, A.: Network clustering coefficient without degree-correlation biases. Physical Review E 71(51), 057101_1–4 (2005)
Wakamiya, S.: Lee, R. Twitter-based tv audience behavior estimation for better tv ratings. DEIM Forum (2011)
Wang, Z., Sun, L., Chen, X., Zhu, W., Liu, J., Chen, M., Yang, S.: Propagation-based Social-aware Replication for Social Video Contents. In: ACM International Conference on Multimedia, Multimedia (2012)
Wang, Z., Sun, L., Zhu, W., Yang, S., Li, H., Wu, D.: Joint Social and Content Recommendation for User Generated Videos in Online Social Network. IEEE Transactions on Multimedia 15(3), 698–709 (2013)
Xu, T., Chen, Y.: Cuckoo: towards decentralized, socio-aware online microblogging services and data measurements. In: Proceedings of the 2nd ACM HotPlanet. ACM (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lin, X., Wang, Z., Sun, L. (2015). MAP: Microblogging Assisted Profiling of TV Shows. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-14445-0_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
eBook Packages: Computer ScienceComputer Science (R0)