28,837 research outputs found
A response to fake news as a response to Citizens United
Published versio
Users' reading habits in online news portals
The aim of this study is to survey reading habits of users of an online news portal. The assumption motivating this study is that insight into the reading habits of users can be helpful to design better news recommendation systems. We estimated the transition probabilities that users who read an article of one news category will move to read an article of another (not necessarily distinct) news category. For this, we analyzed the users' click behavior within plista data set. Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category
Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Satirical news is considered to be entertainment, but it is potentially
deceptive and harmful. Despite the embedded genre in the article, not everyone
can recognize the satirical cues and therefore believe the news as true news.
We observe that satirical cues are often reflected in certain paragraphs rather
than the whole document. Existing works only consider document-level features
to detect the satire, which could be limited. We consider paragraph-level
linguistic features to unveil the satire by incorporating neural network and
attention mechanism. We investigate the difference between paragraph-level
features and document-level features, and analyze them on a large satirical
news dataset. The evaluation shows that the proposed model detects satirical
news effectively and reveals what features are important at which level.Comment: EMNLP 2017, 11 page
The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness
Twitter updates now represent an enormous stream of information originating
from a wide variety of formal and informal sources, much of which is relevant
to real-world events. In this paper we adapt existing bio-surveillance
algorithms to detect localised spikes in Twitter activity corresponding to real
events with a high level of confidence. We then develop a methodology to
automatically summarise these events, both by providing the tweets which fully
describe the event and by linking to highly relevant news articles. We apply
our methods to outbreaks of illness and events strongly affecting sentiment. In
both case studies we are able to detect events verifiable by third party
sources and produce high quality summaries
Designing and Deploying Online Field Experiments
Online experiments are widely used to compare specific design alternatives,
but they can also be used to produce generalizable knowledge and inform
strategic decision making. Doing so often requires sophisticated experimental
designs, iterative refinement, and careful logging and analysis. Few tools
exist that support these needs. We thus introduce a language for online field
experiments called PlanOut. PlanOut separates experimental design from
application code, allowing the experimenter to concisely describe experimental
designs, whether common "A/B tests" and factorial designs, or more complex
designs involving conditional logic or multiple experimental units. These
latter designs are often useful for understanding causal mechanisms involved in
user behaviors. We demonstrate how experiments from the literature can be
implemented in PlanOut, and describe two large field experiments conducted on
Facebook with PlanOut. For common scenarios in which experiments are run
iteratively and in parallel, we introduce a namespaced management system that
encourages sound experimental practice.Comment: Proceedings of the 23rd international conference on World wide web,
283-29
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