Sentiment Analysis for Fake News Detection
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 peo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/11/1348 |
| _version_ | 1797531336417738752 |
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| author | Miguel A. Alonso David Vilares Carlos Gómez-Rodríguez Jesús Vilares |
| author_facet | Miguel A. Alonso David Vilares Carlos Gómez-Rodríguez Jesús Vilares |
| author_sort | Miguel A. Alonso |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:42:20Z |
| format | Article |
| id | doaj.art-c107345e60ed465ca656ca2e81733486 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:42:20Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c107345e60ed465ca656ca2e817334862023-11-21T22:54:14ZengMDPI AGElectronics2079-92922021-06-011011134810.3390/electronics10111348Sentiment Analysis for Fake News DetectionMiguel A. Alonso0David Vilares1Carlos Gómez-Rodríguez2Jesús Vilares3Grupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña and CITIC, 15071 A Coruña, SpainGrupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña and CITIC, 15071 A Coruña, SpainGrupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña and CITIC, 15071 A Coruña, SpainGrupo LyS, Departamento de Ciencias da Computación e Tecnoloxías da Información, Universidade da Coruña and CITIC, 15071 A Coruña, SpainIn 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.https://www.mdpi.com/2079-9292/10/11/1348sentiment analysisopinion miningfake newssocial media |
| spellingShingle | Miguel A. Alonso David Vilares Carlos Gómez-Rodríguez Jesús Vilares Sentiment Analysis for Fake News Detection Electronics sentiment analysis opinion mining fake news social media |
| title | Sentiment Analysis for Fake News Detection |
| title_full | Sentiment Analysis for Fake News Detection |
| title_fullStr | Sentiment Analysis for Fake News Detection |
| title_full_unstemmed | Sentiment Analysis for Fake News Detection |
| title_short | Sentiment Analysis for Fake News Detection |
| title_sort | sentiment analysis for fake news detection |
| topic | sentiment analysis opinion mining fake news social media |
| url | https://www.mdpi.com/2079-9292/10/11/1348 |
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