Authors:
Alvaro Figueira
1
;
Nuno Guimaraes
1
and
Luis Torgo
2
Affiliations:
1
CRACS / INESCTEC and University of Porto, Rua do Campo Alegre, Porto and Portugal
;
2
Faculty of Computer Science, Dalhousie University, Halifax and Canada
Keyword(s):
Fake News, Detection Systems, Survey, Next Challenges.
Related
Ontology
Subjects/Areas/Topics:
Social Media Analytics
;
Society, e-Business and e-Government
;
Web Information Systems and Technologies
Abstract:
Nowadays, false news can be created and disseminated easily through the many social media platforms, resulting in a widespread real-world impact. Modeling and characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient algorithms and tools for their early detection. A recent surge of researching in this area has aimed to address the key issues using methods based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, together with newly created data sets and web services to identify deceiving content. Majority of the research has been targeting fake reviews, biased messages, and against-facts information (false news and hoaxes). In this work, we present a survey on the state of the art concerning types of fake news and the solutions that are being proposed. We focus our survey on content analysis, network propagation, fact-checking and fake news analysis and
emerging detection systems. We also discuss the rationale behind successfully deceiving readers. Finally, we highlight important challenges that these solutions bring.
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