Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
| Autor: | Janaina Ignacio de Morais, Hugo Queiroz Abonizio, Sylvio Barbon Junior, Gabriel Marques Tavares |
|---|---|
| Přispěvatelé: | Abonizio, H. Q., de Morais, J. I., Tavares, G. M., Barbon Junior, S. |
| Rok vydání: | 2020 |
| Předmět: |
fake news
text classification Computer Networks and Communications Computer science Stylometry 02 engineering and technology Fake new computer.software_genre Fake news Machine learning Multi-language Text classification Brazilian Portuguese Credibility 0202 electrical engineering electronic engineering information engineering Social media multi-language Structure (mathematical logic) lcsh:T58.5-58.64 lcsh:Information technology business.industry American English 020206 networking & telecommunications language.human_language Support vector machine machine learning stylometry language 020201 artificial intelligence & image processing Artificial intelligence Portuguese business computer Natural language processing |
| Zdroj: | Future Internet, Vol 12, Iss 87, p 87 (2020) Future Internet Volume 12 Issue 5 |
| ISSN: | 1999-5903 |
| Popis: | 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 (k-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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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