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Tackling food marketing to children in a digital world: trans-disciplinary perspectives. Children’s rights, evidence of impact, methodological challenges, regulatory options and policy implications for the WHO European Region
There is unequivocal evidence that childhood obesity is influenced by marketing of foods and non-alcoholic beverages high in saturated fat, salt and/or free sugars (HFSS), and a core recommendation of the WHO Commission on Ending Childhood Obesity is to reduce children’s exposure to all such marketing. As a result, WHO has called on Member States to introduce restrictions on marketing of HFSS foods to children, covering all media, including digital, and to close any regulatory loopholes. This publication provides up-to-date information on the marketing of foods and non-alcoholic beverages to children and the changes that have occurred in recent years, focusing in particular on the major shift to digital marketing. It examines trends in media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s rights and digital privacy. Finally the report discusses the policy implications and some of the recent policy action by WHO European Member States
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
CSI: A Hybrid Deep Model for Fake News Detection
The topic of fake news has drawn attention both from the public and the
academic communities. Such misinformation has the potential of affecting public
opinion, providing an opportunity for malicious parties to manipulate the
outcomes of public events such as elections. Because such high stakes are at
play, automatically detecting fake news is an important, yet challenging
problem that is not yet well understood. Nevertheless, there are three
generally agreed upon characteristics of fake news: the text of an article, the
user response it receives, and the source users promoting it. Existing work has
largely focused on tailoring solutions to one particular characteristic which
has limited their success and generality. In this work, we propose a model that
combines all three characteristics for a more accurate and automated
prediction. Specifically, we incorporate the behavior of both parties, users
and articles, and the group behavior of users who propagate fake news.
Motivated by the three characteristics, we propose a model called CSI which is
composed of three modules: Capture, Score, and Integrate. The first module is
based on the response and text; it uses a Recurrent Neural Network to capture
the temporal pattern of user activity on a given article. The second module
learns the source characteristic based on the behavior of users, and the two
are integrated with the third module to classify an article as fake or not.
Experimental analysis on real-world data demonstrates that CSI achieves higher
accuracy than existing models, and extracts meaningful latent representations
of both users and articles.Comment: In Proceedings of the 26th ACM International Conference on
Information and Knowledge Management (CIKM) 201
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