2026-06-28T15:02:53Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/1038102025-09-01T17:51:11Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Berrondo Otermin, Maialen
author
Sarasa Cabezuelo, Antonio
2024-05-08T10:46:37Z
2024-05-08T10:46:37Z
2023
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
10.3390/electronics12245041
https://hdl.handle.net/20.500.14352/103810
2079-9292
https://doi.org/10.3390/electronics12245041
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.
eng
Attribution 4.0 International
Application of artificial intelligence techniques to detect fake news: A review
journal article
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