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Sentiment Analysis for Fake News Detection

Grupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña and CITIC, 15071 A Coruña, Spain
Author to whom correspondence should be addressed.
Academic Editor: Cataldo Musto
Electronics 2021, 10(11), 1348;
Received: 4 May 2021 / Revised: 21 May 2021 / Accepted: 2 June 2021 / Published: 5 June 2021
(This article belongs to the Special Issue Emerging Application of Sentiment Analysis Technologies)
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. View Full-Text
Keywords: sentiment analysis; opinion mining; fake news; social media sentiment analysis; opinion mining; fake news; social media
MDPI and ACS Style

Alonso, M.A.; Vilares, D.; Gómez-Rodríguez, C.; Vilares, J. Sentiment Analysis for Fake News Detection. Electronics 2021, 10, 1348.

AMA Style

Alonso MA, Vilares D, Gómez-Rodríguez C, Vilares J. Sentiment Analysis for Fake News Detection. Electronics. 2021; 10(11):1348.

Chicago/Turabian Style

Alonso, Miguel A., David Vilares, Carlos Gómez-Rodríguez, and Jesús Vilares. 2021. "Sentiment Analysis for Fake News Detection" Electronics 10, no. 11: 1348.

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