11,251 research outputs found
TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics
Online social networking services allow their users to post content in the
form of text, images or videos. The main mechanism driving content diffusion is
the possibility for users to re-share the content posted by their social
connections, which may then cascade across the system. A fundamental problem
when studying information cascades is the possibility to develop sound
mathematical models, whose parameters can be calibrated on empirical data, in
order to predict the future course of a cascade after a window of observation.
In this paper, we focus on Twitter and, in particular, on the temporal patterns
of retweet activity for an original tweet. We model the system by
Time-Dependent Hawkes process (TiDeH), which properly takes into account the
circadian nature of the users and the aging of information. The input of the
prediction model are observed retweet times and structural information about
the underlying social network. We develop a procedure for parameter
optimization and for predicting the future profiles of retweet activity at
different time resolutions. We validate our methodology on a large corpus of
Twitter data and demonstrate its systematic improvement over existing
approaches in all the time regimes.Comment: The manuscript has been accepted in the 10th International AAAI
Conference on Web and Social Media (ICWSM 2016
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
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