1,374 research outputs found
MAP: Microblogging Assisted Profiling of TV Shows
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
Learning user-specific latent influence and susceptibility from information cascades
Predicting cascade dynamics has important implications for understanding
information propagation and launching viral marketing. Previous works mainly
adopt a pair-wise manner, modeling the propagation probability between pairs of
users using n^2 independent parameters for n users. Consequently, these models
suffer from severe overfitting problem, specially for pairs of users without
direct interactions, limiting their prediction accuracy. Here we propose to
model the cascade dynamics by learning two low-dimensional user-specific
vectors from observed cascades, capturing their influence and susceptibility
respectively. This model requires much less parameters and thus could combat
overfitting problem. Moreover, this model could naturally model
context-dependent factors like cumulative effect in information propagation.
Extensive experiments on synthetic dataset and a large-scale microblogging
dataset demonstrate that this model outperforms the existing pair-wise models
at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015
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