22,728 research outputs found
From Popularity Prediction to Ranking Online News
International audienceNews articles are an engaging type of online content that captures the attention of a significant amount of Internet users. They are particularly enjoyed by mobile users and massively spread through online social platforms. As a result, there is an increased interest in discovering the articles that will become popular among users. This objective falls under the broad scope of content popularity prediction and has direct implications in the development of new services for online advertisement and content distribution. In this paper, we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task as a ranking problem, where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. Using data obtained from two important news sites in France and Netherlands, we analyze the ranking effectiveness of two prediction models. Our results indicate that popularity prediction methods are adequate solutions for this ranking task and could be considered as a valuable alternative for automatic online news ranking
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
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
Breaking the News: First Impressions Matter on Online News
A growing number of people are changing the way they consume news, replacing
the traditional physical newspapers and magazines by their virtual online
versions or/and weblogs. The interactivity and immediacy present in online news
are changing the way news are being produced and exposed by media corporations.
News websites have to create effective strategies to catch people's attention
and attract their clicks. In this paper we investigate possible strategies used
by online news corporations in the design of their news headlines. We analyze
the content of 69,907 headlines produced by four major global media
corporations during a minimum of eight consecutive months in 2014. In order to
discover strategies that could be used to attract clicks, we extracted features
from the text of the news headlines related to the sentiment polarity of the
headline. We discovered that the sentiment of the headline is strongly related
to the popularity of the news and also with the dynamics of the posted comments
on that particular news.Comment: The paper appears in ICWSM 201
Selection Bias in News Coverage: Learning it, Fighting it
News entities must select and filter the coverage they broadcast through
their respective channels since the set of world events is too large to be
treated exhaustively. The subjective nature of this filtering induces biases
due to, among other things, resource constraints, editorial guidelines,
ideological affinities, or even the fragmented nature of the information at a
journalist's disposal. The magnitude and direction of these biases are,
however, widely unknown. The absence of ground truth, the sheer size of the
event space, or the lack of an exhaustive set of absolute features to measure
make it difficult to observe the bias directly, to characterize the leaning's
nature and to factor it out to ensure a neutral coverage of the news. In this
work, we introduce a methodology to capture the latent structure of media's
decision process on a large scale. Our contribution is multi-fold. First, we
show media coverage to be predictable using personalization techniques, and
evaluate our approach on a large set of events collected from the GDELT
database. We then show that a personalized and parametrized approach not only
exhibits higher accuracy in coverage prediction, but also provides an
interpretable representation of the selection bias. Last, we propose a method
able to select a set of sources by leveraging the latent representation. These
selected sources provide a more diverse and egalitarian coverage, all while
retaining the most actively covered events
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