Automatic Detection of Fake News Using Gated Recurrent Unit Deep Model
Year: 2024
Type: conference-paper
Abstract: Nowadays, the utilization of social media platforms continues to rise, as is the dissemination of misleading data. This fake information is damaging to both society and individuals. Hence, it is essential to prevent and detect the dissemination of fake news. Furthermore, the main challenge is the lack of an effective technique for distinguishing between truthful and false reviews; even individuals are usually unable to recognize the difference. In contract to numerous machine learning-based methods, the deep learning-based methods exhibit a superior capacity for accurately detecting fake news. Earlier studies focused on machine learning based and data mining techniques, with limited exploration of deep learning-based methods for automated detecting fake news task. This study attempts to introduce a method based on the GRU deep model in order to detect automatically fake news. Experimental evaluation using a benchmark fake news dataset indicates extremely encouraging results and enhanced performances.
Source: Procedia Computer Science
Authors: Hanane ELFAIK, El Habib NFAOUI
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4394565239