RT Journal Article
T1 Application of artificial intelligence techniques to detect fake news: A review
A1 Berrondo Otermin, Maialen
A1 Sarasa Cabezuelo, Antonio
AB With the rapid growth of social media platforms and online news consumption, the proliferation of fake news has emerged as a pressing concern. Detecting and combating fake news has become crucial in ensuring the accuracy and reliability of information disseminated through social media. Machine learning plays a crucial role in fake news detection due to its ability to analyze large amounts of data and identify patterns and trends that are indicative of misinformation. Fake news detection involves analyzing various types of data, such as textual or media content, social context, and network structure. Machine learning techniques enable automated and scalable detection of fake news, which is essential given the vast volume of information shared on social media platforms. Overall, machine learning provides a powerful tool for detecting and preventing the spread of fake news on social media. This review article provides an extensive analysis of recent advancements in fake news detection. The chosen articles cover a wide range of approaches, including data mining, deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based techniques.
PB MDPI
YR 2023
FD 2023-12-02
LK https://hdl.handle.net/20.500.14352/103810
UL https://hdl.handle.net/20.500.14352/103810
LA eng
NO Berrondo-Otermin, M.; Sarasa-Cabezuelo, A. Application of Artificial Intelligence Techniques to Detect Fake News: A Review. Electronics 2023, 12, 5041. https://doi.org/10.3390/electronics12245041
NO 2023 Descuento MDPI
NO Ministerio de Ciencia e Innovación (España)
DS Docta Complutense
RD 8 abr 2025