Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/12/5/87 |
| _version_ | 1797568304186916864 |
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| author | Hugo Queiroz Abonizio Janaina Ignacio de Morais Gabriel Marques Tavares Sylvio Barbon Junior |
| author_facet | Hugo Queiroz Abonizio Janaina Ignacio de Morais Gabriel Marques Tavares Sylvio Barbon Junior |
| author_sort | Hugo Queiroz Abonizio |
| collection | DOAJ |
| description | Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors (<i>k</i>-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF. |
| first_indexed | 2024-03-10T19:55:33Z |
| format | Article |
| id | doaj.art-7abc2a924ab54a22a4e2f64baa1a9439 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T19:55:33Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-7abc2a924ab54a22a4e2f64baa1a94392023-11-20T00:00:57ZengMDPI AGFuture Internet1999-59032020-05-011258710.3390/fi12050087Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual FeaturesHugo Queiroz Abonizio0Janaina Ignacio de Morais1Gabriel Marques Tavares2Sylvio Barbon Junior3State University of Londrina (UEL), 86057-970 Londrina, BrazilState University of Londrina (UEL), 86057-970 Londrina, BrazilUniversità degli Studi di Milano (UNIMI), 20122 Milan, ItalyState University of Londrina (UEL), 86057-970 Londrina, BrazilOnline Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors (<i>k</i>-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF.https://www.mdpi.com/1999-5903/12/5/87fake newstext classificationmulti-languagestylometrymachine learning |
| spellingShingle | Hugo Queiroz Abonizio Janaina Ignacio de Morais Gabriel Marques Tavares Sylvio Barbon Junior Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features Future Internet fake news text classification multi-language stylometry machine learning |
| title | Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features |
| title_full | Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features |
| title_fullStr | Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features |
| title_full_unstemmed | Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features |
| title_short | Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features |
| title_sort | language independent fake news detection english portuguese and spanish mutual features |
| topic | fake news text classification multi-language stylometry machine learning |
| url | https://www.mdpi.com/1999-5903/12/5/87 |
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