Year: 2024
Type: conference-paper
Abstract: In recent years, the issue of fake news has become exceedingly pertinent due to its rapid dissemination through social media and online platforms.Detecting fake news requires the utilization of various methods and resources.This work proposes an approach to building a model for fake news detection using generative artificial intelligence and natural language processing.The main focus of the proposed approach is the synthesis of model features for fake news detection using generative artificial intelligence and Natural Language Processing.The use of this proposed approach not only facilitates the detection of fake news but also renders the detection process transparent and customizable by the user.Additionally, through the proposed method, users gain the ability to expand features and train the system to adapt to new types of fake news and their variations.Experimental results, presented both qualitatively through visual analytics and quantitatively through statistical indicators, convincingly demonstrate the effectiveness of the proposed approach in detecting fake news with satisfactory accuracy levels reaching 90% and provide users with sufficient interpretability of the obtained decisions.Overall, this research aims to create an approach for detecting fake news that may have a significant impact on addressing this issue in contemporary society.
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4403608829