Journal
ELECTRONICS
Volume 10, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/electronics10111348
Keywords
sentiment analysis; opinion mining; fake news; social media
Categories
Funding
- FEDER/Ministerio de Ciencia, Innovacion y Universidades - Agencia Estatal de Investigacion through the ANSWERASAP project [TIN2017-85160-C2-1-R]
- Xunta de Galicia [ED431C 2020/11]
- Conselleria de Educacion, Universidade e Formacion Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER)
- Galicia ERDF 2014-20 Operational Programme
- Secretaria Xeral de Universidades [ED431G 2019/01]
- BBVA Foundation
- European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE) [714150]
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Fake news has been on the rise in recent years, posing a serious threat to social cohesion and trust in leaders. Automatic systems for fake news detection have become increasingly important due to the unfeasibility of manual verification, with sentiment analysis playing a key role in this process.
In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.
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