2,070 research outputs found
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
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News
The problem of fake news has gained a lot of attention as it is claimed to
have had a significant impact on 2016 US Presidential Elections. Fake news is
not a new problem and its spread in social networks is well-studied. Often an
underlying assumption in fake news discussion is that it is written to look
like real news, fooling the reader who does not check for reliability of the
sources or the arguments in its content. Through a unique study of three data
sets and features that capture the style and the language of articles, we show
that this assumption is not true. Fake news in most cases is more similar to
satire than to real news, leading us to conclude that persuasion in fake news
is achieved through heuristics rather than the strength of arguments. We show
overall title structure and the use of proper nouns in titles are very
significant in differentiating fake from real. This leads us to conclude that
fake news is targeted for audiences who are not likely to read beyond titles
and is aimed at creating mental associations between entities and claims.Comment: Published at The 2nd International Workshop on News and Public
Opinion at ICWS
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
Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology, and Framework
This paper describes an approach for supporting automatic satire detection through effective deep
learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection
problems. We both trained and tested the system exploiting articles derived from two important
satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers
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