Abstract
Abstract: With the proliferation of digital information, the challenge of identifying and mitigating the spread of fake news has become increasingly crucial. This research paper provides a comprehensive review of existing technologies designed for the detection of fake news, with a particular focus on their adaptation to mobile applications. The study delves into various approaches, including natural language processing, machine learning algorithms, and deep learning models, highlighting their strengths and limitations. Additionally, it explores the integration of user-generated content analysis and social network features in enhancing the accuracy of fake news detection on mobile platforms. By synthesizing insights from recent advancements, this paper aims to inform the development of an effective mobile app for mitigating the impact of fake news in the digital landscape.
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