60,762 research outputs found
The Pulse of News in Social Media: Forecasting Popularity
News articles are extremely time sensitive by nature. There is also intense
competition among news items to propagate as widely as possible. Hence, the
task of predicting the popularity of news items on the social web is both
interesting and challenging. Prior research has dealt with predicting eventual
online popularity based on early popularity. It is most desirable, however, to
predict the popularity of items prior to their release, fostering the
possibility of appropriate decision making to modify an article and the manner
of its publication. In this paper, we construct a multi-dimensional feature
space derived from properties of an article and evaluate the efficacy of these
features to serve as predictors of online popularity. We examine both
regression and classification algorithms and demonstrate that despite
randomness in human behavior, it is possible to predict ranges of popularity on
twitter with an overall 84% accuracy. Our study also serves to illustrate the
differences between traditionally prominent sources and those immensely popular
on the social web
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
Social Dynamics of Digg
Online social media provide multiple ways to find interesting content. One
important method is highlighting content recommended by user's friends. We
examine this process on one such site, the news aggregator Digg. With a
stochastic model of user behavior, we distinguish the effects of the content
visibility and interestingness to users. We find a wide range of interest and
distinguish stories primarily of interest to a users' friends from those of
interest to the entire user community. We show how this model predicts a
story's eventual popularity from users' early reactions to it, and estimate the
prediction reliability. This modeling framework can help evaluate alternative
design choices for displaying content on the site.Comment: arXiv admin note: text overlap with arXiv:1010.023
Long Trend Dynamics in Social Media
A main characteristic of social media is that its diverse content, copiously
generated by both standard outlets and general users, constantly competes for
the scarce attention of large audiences. Out of this flood of information some
topics manage to get enough attention to become the most popular ones and thus
to be prominently displayed as trends. Equally important, some of these trends
persist long enough so as to shape part of the social agenda. How this happens
is the focus of this paper. By introducing a stochastic dynamical model that
takes into account the user's repeated involvement with given topics, we can
predict the distribution of trend durations as well as the thresholds in
popularity that lead to their emergence within social media. Detailed
measurements of datasets from Twitter confirm the validity of the model and its
predictions
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
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