14,871 research outputs found
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
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
The Impact of Crowds on News Engagement: A Reddit Case Study
Today, users are reading the news through social platforms. These platforms
are built to facilitate crowd engagement, but not necessarily disseminate
useful news to inform the masses. Hence, the news that is highly engaged with
may not be the news that best informs. While predicting news popularity has
been well studied, it has not been studied in the context of crowd
manipulations. In this paper, we provide some preliminary results to a longer
term project on crowd and platform manipulations of news and news popularity.
In particular, we choose to study known features for predicting news popularity
and how those features may change on reddit.com, a social platform used
commonly for news aggregation. Along with this, we explore ways in which users
can alter the perception of news through changing the title of an article. We
find that news on reddit is predictable using previously studied sentiment and
content features and that posts with titles changed by reddit users tend to be
more popular than posts with the original article title.Comment: Published at The 2nd International Workshop on News and Public
Opinion at ICWSM 201
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
Modeling and predicting the popularity of online news based on temporal and content-related features
As the market of globally available online news is large and still growing, there is a strong competition between online publishers in order to reach the largest possible audience. Therefore an intelligent online publishing strategy is of the highest importance to publishers. A prerequisite for being able to optimize any online strategy, is to have trustworthy predictions of how popular new online content may become. This paper presents a novel methodology to model and predict the popularity of online news. We first introduce a new strategy and mathematical model to capture view patterns of online news. After a thorough analysis of such view patterns, we show that well-chosen base functions lead to suitable models, and show how the influence of day versus night on the total view patterns can be taken into account to further increase the accuracy, without leading to more complex models. Second, we turn to the prediction of future popularity, given recently published content. By means of a new real-world dataset, we show that the combination of features related to content, meta-data, and the temporal behavior leads to significantly improved predictions, compared to existing approaches which only consider features based on the historical popularity of the considered articles. Whereas traditionally linear regression is used for the application under study, we show that the more expressive gradient tree boosting method proves beneficial for predicting news popularity
- …