19,219 research outputs found
Characterizing and Modeling the Dynamics of Activity and Popularity
Social media, regarded as two-layer networks consisting of users and items,
turn out to be the most important channels for access to massive information in
the era of Web 2.0. The dynamics of human activity and item popularity is a
crucial issue in social media networks. In this paper, by analyzing the growth
of user activity and item popularity in four empirical social media networks,
i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links
between users and items are more likely to be created by active users and to be
acquired by popular items, where user activity and item popularity are measured
by the number of cross links associated with users and items. This indicates
that users generally trace popular items, overall. However, it is found that
the inactive users more severely trace popular items than the active users.
Inspired by empirical analysis, we propose an evolving model for such networks,
in which the evolution is driven only by two-step random walk. Numerical
experiments verified that the model can qualitatively reproduce the
distributions of user activity and item popularity observed in empirical
networks. These results might shed light on the understandings of micro
dynamics of activity and popularity in social media networks.Comment: 13 pages, 6 figures, 2 table
Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries
How many listens will an artist receive on a online radio? How about plays on
a YouTube video? How many of these visits are new or returning users? Modeling
and mining popularity dynamics of social activity has important implications
for researchers, content creators and providers. We here investigate the effect
of revisits (successive visits from a single user) on content popularity. Using
four datasets of social activity, with up to tens of millions media objects
(e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect
of revisits in the popularity evolution of such objects. Secondly, we propose
the Phoenix-R model which captures the popularity dynamics of individual
objects. Phoenix-R has the desired properties of being: (1) parsimonious, being
based on the minimum description length principle, and achieving lower root
mean squared error than state-of-the-art baselines; (2) applicable, the model
is effective for predicting future popularity values of objects.Comment: To appear on European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases 201
Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data
Use of socially generated "big data" to access information about collective
states of the minds in human societies has become a new paradigm in the
emerging field of computational social science. A natural application of this
would be the prediction of the society's reaction to a new product in the sense
of popularity and adoption rate. However, bridging the gap between "real time
monitoring" and "early predicting" remains a big challenge. Here we report on
an endeavor to build a minimalistic predictive model for the financial success
of movies based on collective activity data of online users. We show that the
popularity of a movie can be predicted much before its release by measuring and
analyzing the activity level of editors and viewers of the corresponding entry
to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the
dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi
Everyday the Same Picture: Popularity and Content Diversity
Facebook is flooded by diverse and heterogeneous content, from kittens up to
music and news, passing through satirical and funny stories. Each piece of that
corpus reflects the heterogeneity of the underlying social background. In the
Italian Facebook we have found an interesting case: a page having more than
followers that every day posts the same picture of a popular Italian
singer. In this work, we use such a page as a control to study and model the
relationship between content heterogeneity on popularity. In particular, we use
that page for a comparative analysis of information consumption patterns with
respect to pages posting science and conspiracy news. In total, we analyze
about likes and comments, made by approximately and
users, respectively. We conclude the paper by introducing a model mimicking
users selection preferences accounting for the heterogeneity of contents
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