14,703 research outputs found
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
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
Fully Automated Fact Checking Using External Sources
Given the constantly growing proliferation of false claims online in recent
years, there has been also a growing research interest in automatically
distinguishing false rumors from factually true claims. Here, we propose a
general-purpose framework for fully-automatic fact checking using external
sources, tapping the potential of the entire Web as a knowledge source to
confirm or reject a claim. Our framework uses a deep neural network with LSTM
text encoding to combine semantic kernels with task-specific embeddings that
encode a claim together with pieces of potentially-relevant text fragments from
the Web, taking the source reliability into account. The evaluation results
show good performance on two different tasks and datasets: (i) rumor detection
and (ii) fact checking of the answers to a question in community question
answering forums.Comment: RANLP-201
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